2021
Guo, Xiaoyuan; Gichoya, Judy Wawira; Trivedi, Hari; Purkayastha, Saptarshi; Banerjee, Imon
MedShift: identifying shift data for medical dataset curation Journal Article
In: arXiv preprint arXiv:2112.13885, 2021.
@article{guo2021medshift,
title = {MedShift: identifying shift data for medical dataset curation},
author = {Xiaoyuan Guo and Judy Wawira Gichoya and Hari Trivedi and Saptarshi Purkayastha and Imon Banerjee},
url = {https://arxiv.org/abs/2112.13885},
year = {2021},
date = {2021-12-27},
urldate = {2021-01-01},
journal = {arXiv preprint arXiv:2112.13885},
abstract = {To curate a high-quality dataset, identifying data variance between the internal and external sources is a fundamental and crucial step. However, methods to detect shift or variance in data have not been significantly researched. Challenges to this are the lack of effective approaches to learn dense representation of a dataset and difficulties of sharing private data across medical institutions. To overcome the problems, we propose a unified pipeline called MedShift to detect the top-level shift samples and thus facilitate the medical curation. Given an internal dataset A as the base source, we first train anomaly detectors for each class of dataset A to learn internal distributions in an unsupervised way. Second, without exchanging data across sources, we run the trained anomaly detectors on an external dataset B for each class. The data samples with high anomaly scores are identified as shift data. To quantify the shiftness of the external dataset, we cluster B's data into groups class-wise based on the obtained scores. We then train a multi-class classifier on A and measure the shiftness with the classifier's performance variance on B by gradually dropping the group with the largest anomaly score for each class. Additionally, we adapt a dataset quality metric to help inspect the distribution differences for multiple medical sources. We verify the efficacy of MedShift with musculoskeletal radiographs (MURA) and chest X-rays datasets from more than one external source. Experiments show our proposed shift data detection pipeline can be beneficial for medical centers to curate high-quality datasets more efficiently.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Guo, Xiaoyuan; Gichoya, Judy Wawira; Purkayastha, Saptarshi; Banerjee, Imon
CVAD/An unsupervised image anomaly detector Journal Article
In: Software Impacts, pp. 100195, 2021.
@article{guo2021cvad,
title = {CVAD/An unsupervised image anomaly detector},
author = {Xiaoyuan Guo and Judy Wawira Gichoya and Saptarshi Purkayastha and Imon Banerjee},
doi = {10.1016/j.simpa.2021.100195},
year = {2021},
date = {2021-12-22},
urldate = {2021-01-01},
journal = {Software Impacts},
pages = {100195},
publisher = {Elsevier},
abstract = {Detecting out-of-distribution samples for image applications plays an important role in safeguarding the reliability of machine learning model deployment. In this article, we developed a software tool to support our OOD detector CVAD - a self-supervised Cascade Variational autoencoder-based Anomaly Detector , which can be easily applied to various image applications without any assumptions. The corresponding open-source software is published for better public research and tool usage.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mahajan, Yohan; Bhimireddy, Ananth; Abid, Areeba; Gichoya, Judy W; Purkayastha, Saptarshi
PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor Journal Article
In: Data in Brief, vol. 38, pp. 107287, 2021.
@article{mahajan2021plhi,
title = {PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor},
author = {Yohan Mahajan and Ananth Bhimireddy and Areeba Abid and Judy W Gichoya and Saptarshi Purkayastha},
doi = {10.1016/j.dib.2021.107287},
year = {2021},
date = {2021-10-01},
urldate = {2021-01-01},
journal = {Data in Brief},
volume = {38},
pages = {107287},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abid, Areeba; Sinha, Priyanshu; Harpale, Aishwarya; Gichoya, Judy; Purkayastha, Saptarshi
Optimizing Medical Image Classification Models for Edge Devices Inproceedings
In: International Symposium on Distributed Computing and Artificial Intelligence, pp. 77–87, Springer, Cham 2021, ISBN: 978-3-030-86261-9.
@inproceedings{abid2021optimizing,
title = {Optimizing Medical Image Classification Models for Edge Devices},
author = {Areeba Abid and Priyanshu Sinha and Aishwarya Harpale and Judy Gichoya and Saptarshi Purkayastha},
doi = {10.1007/978-3-030-86261-9_8},
isbn = {978-3-030-86261-9},
year = {2021},
date = {2021-09-02},
urldate = {2021-01-01},
booktitle = {International Symposium on Distributed Computing and Artificial Intelligence},
pages = {77--87},
organization = {Springer, Cham},
abstract = {Machine learning algorithms for medical diagnostics often require resource-intensive environments to run, such as expensive cloud servers or high-end GPUs, making these models impractical for use in the field. We investigate the use of model quantization and GPU-acceleration for chest X-ray classification on edge devices. We employ 3 types of quantization (dynamic range, float-16, and full int8) which we tested on models trained on the Chest-XRay14 Dataset. We achieved a 2\textendash4x reduction in model size, offset by small decreases in the mean AUC-ROC score of 0.0%\textendash0.9%. On ARM architectures, integer quantization was shown to improve inference latency by up to 57%. However, we also observe significant increases in latency on x86 processors. GPU acceleration also improved inference latency, but this was outweighed by kernel launch overhead. We show that optimization of diagnostic models has the potential to expand their utility to day-to-day devices used by patients and healthcare workers; however, these improvements are context- and architecture-dependent and should be tested on the relevant devices before deployment in low-resource environments.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Umoren, Rachel; Bucher, Sherri; Hippe, Daniel S; Ezenwa, Beatrice Nkolika; Fajolu, Iretiola Bamikeolu; Okwako, Felicitas M; Feltner, John; Nafula, Mary; Musale, Annet; Olawuyi, Olubukola A; others,
In: BMJ open, vol. 11, no. 8, pp. e048506, 2021.
@article{umoren2021ehbbb,
title = {eHBB: a randomised controlled trial of virtual reality or video for neonatal resuscitation refresher training in healthcare workers in resource-scarce settings},
author = {Rachel Umoren and Sherri Bucher and Daniel S Hippe and Beatrice Nkolika Ezenwa and Iretiola Bamikeolu Fajolu and Felicitas M Okwako and John Feltner and Mary Nafula and Annet Musale and Olubukola A Olawuyi and others},
doi = {10.1136/bmjopen-2020-048506},
year = {2021},
date = {2021-08-25},
urldate = {2021-01-01},
journal = {BMJ open},
volume = {11},
number = {8},
pages = {e048506},
publisher = {British Medical Journal Publishing Group},
abstract = {Objective To assess the impact of mobile virtual reality (VR) simulations using electronic Helping Babies Breathe (eHBB) or video for the maintenance of neonatal resuscitation skills in healthcare workers in resource-scarce settings.
Design Randomised controlled trial with 6-month follow-up (2018\textendash2020).
Setting Secondary and tertiary healthcare facilities.
Participants 274 nurses and midwives assigned to labour and delivery, operating room and newborn care units were recruited from 20 healthcare facilities in Nigeria and Kenya and randomised to one of three groups: VR (eHBB+digital guide), video (video+digital guide) or control (digital guide only) groups before an in-person HBB course.
Intervention(s) eHBB VR simulation or neonatal resuscitation video.
Main outcome(s) Healthcare worker neonatal resuscitation skills using standardised checklists in a simulated setting at 1 month, 3 months and 6 months.
Results Neonatal resuscitation skills pass rates were similar among the groups at 6-month follow-up for bag-and-mask ventilation (BMV) skills check (VR 28%, video 25%, control 22%, p=0.71), objective structured clinical examination (OSCE) A (VR 76%, video 76%, control 72%, p=0.78) and OSCE B (VR 62%, video 60%, control 49%, p=0.18). Relative to the immediate postcourse assessments, there was greater retention of BMV skills at 6 months in the VR group (−15% VR, p=0.10; −21% video, p<0.01, \textendash27% control, p=0.001). OSCE B pass rates in the VR group were numerically higher at 3 months (+4%, p=0.64) and 6 months (+3%, p=0.74) and lower in the video (−21% at 3 months, p<0.001; −14% at 6 months, p=0.066) and control groups (−7% at 3 months, p=0.43; −14% at 6 months, p=0.10). On follow-up survey, 95% (n=65) of respondents in the VR group and 98% (n=82) in the video group would use their assigned intervention again.
Conclusion eHBB VR training was highly acceptable to healthcare workers in low-income to middle-income countries and may provide additional support for neonatal resuscitation skills retention compared with other digital interventions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Design Randomised controlled trial with 6-month follow-up (2018–2020).
Setting Secondary and tertiary healthcare facilities.
Participants 274 nurses and midwives assigned to labour and delivery, operating room and newborn care units were recruited from 20 healthcare facilities in Nigeria and Kenya and randomised to one of three groups: VR (eHBB+digital guide), video (video+digital guide) or control (digital guide only) groups before an in-person HBB course.
Intervention(s) eHBB VR simulation or neonatal resuscitation video.
Main outcome(s) Healthcare worker neonatal resuscitation skills using standardised checklists in a simulated setting at 1 month, 3 months and 6 months.
Results Neonatal resuscitation skills pass rates were similar among the groups at 6-month follow-up for bag-and-mask ventilation (BMV) skills check (VR 28%, video 25%, control 22%, p=0.71), objective structured clinical examination (OSCE) A (VR 76%, video 76%, control 72%, p=0.78) and OSCE B (VR 62%, video 60%, control 49%, p=0.18). Relative to the immediate postcourse assessments, there was greater retention of BMV skills at 6 months in the VR group (−15% VR, p=0.10; −21% video, p<0.01, –27% control, p=0.001). OSCE B pass rates in the VR group were numerically higher at 3 months (+4%, p=0.64) and 6 months (+3%, p=0.74) and lower in the video (−21% at 3 months, p<0.001; −14% at 6 months, p=0.066) and control groups (−7% at 3 months, p=0.43; −14% at 6 months, p=0.10). On follow-up survey, 95% (n=65) of respondents in the VR group and 98% (n=82) in the video group would use their assigned intervention again.
Conclusion eHBB VR training was highly acceptable to healthcare workers in low-income to middle-income countries and may provide additional support for neonatal resuscitation skills retention compared with other digital interventions.
Kathiravelu, Pradeeban; Sharma, Puneet; Sharma, Ashish; Banerjee, Imon; Trivedi, Hari; Purkayastha, Saptarshi; Sinha, Priyanshu; Cadrin-Chenevert, Alexandre; Safdar, Nabile; Gichoya, Judy Wawira
A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images Journal Article
In: Journal of Digital Imaging, vol. 34, no. 4, pp. 1005–1013, 2021.
@article{kathiravelu2021dicom,
title = {A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images},
author = {Pradeeban Kathiravelu and Puneet Sharma and Ashish Sharma and Imon Banerjee and Hari Trivedi and Saptarshi Purkayastha and Priyanshu Sinha and Alexandre Cadrin-Chenevert and Nabile Safdar and Judy Wawira Gichoya},
doi = {10.1007/s10278-021-00491-w},
year = {2021},
date = {2021-08-01},
urldate = {2021-01-01},
journal = {Journal of Digital Imaging},
volume = {34},
number = {4},
pages = {1005--1013},
publisher = {Springer International Publishing},
abstract = {Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals’ PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners’ examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Guo, Xiaoyuan; Gichoya, Judy Wawira; Purkayastha, Saptarshi; Banerjee, Imon
Margin-Aware Intra-Class Novelty Identification for Medical Images Journal Article
In: arXiv preprint arXiv:2108.00117, 2021.
@article{guo2021margin,
title = {Margin-Aware Intra-Class Novelty Identification for Medical Images},
author = {Xiaoyuan Guo and Judy Wawira Gichoya and Saptarshi Purkayastha and Imon Banerjee},
url = {https://arxiv.org/abs/2108.00117},
year = {2021},
date = {2021-07-31},
urldate = {2021-07-31},
journal = {arXiv preprint arXiv:2108.00117},
abstract = {Traditional anomaly detection methods focus on detecting inter-class variations while medical image novelty identification is inherently an intra-class detection problem. For example, a machine learning model trained with normal chest X-ray and common lung abnormalities, is expected to discover and flag idiopathic pulmonary fibrosis which a rare lung disease and unseen by the model during training. The nuances from intra-class variations and lack of relevant training data in medical image analysis pose great challenges for existing anomaly detection methods. To tackle the challenges, we propose a hybrid model - Transformation-based Embedding learning for Novelty Detection (TEND) which without any out-of-distribution training data, performs novelty identification by combining both autoencoder-based and classifier-based method. With a pre-trained autoencoder as image feature extractor, TEND learns to discriminate the feature embeddings of in-distribution data from the transformed counterparts as fake out-of-distribution inputs. To enhance the separation, a distance objective is optimized to enforce a margin between the two classes. Extensive experimental results on both natural image datasets and medical image datasets are presented and our method out-performs state-of-the-art approaches. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Banerjee, Imon; Bhimireddy, Ananth Reddy; Burns, John L; Celi, Leo Anthony; Chen, Li-Ching; Correa, Ramon; Dullerud, Natalie; Ghassemi, Marzyeh; Huang, Shih-Cheng; Kuo, Po-Chih; others,
Reading Race: AI Recognises Patient's Racial Identity In Medical Images Journal Article
In: arXiv preprint arXiv:2107.10356, 2021.
@article{banerjee2021reading,
title = {Reading Race: AI Recognises Patient's Racial Identity In Medical Images},
author = {Imon Banerjee and Ananth Reddy Bhimireddy and John L Burns and Leo Anthony Celi and Li-Ching Chen and Ramon Correa and Natalie Dullerud and Marzyeh Ghassemi and Shih-Cheng Huang and Po-Chih Kuo and others},
url = {https://arxiv.org/abs/2107.10356},
year = {2021},
date = {2021-07-21},
urldate = {2021-07-21},
journal = {arXiv preprint arXiv:2107.10356},
abstract = {Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images.
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study.
Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race -- even from corrupted, cropped, and noised medical images -- in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model has access to. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study.
Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race -- even from corrupted, cropped, and noised medical images -- in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model has access to.
Kodela, Snigdha; Pinnamraju, Jahnavi; Gichoya, Judy W; Purkayastha, Saptarshi
Predicting Opioid Prescriptions based on Patient Demographics in MIMIC-IV Inproceedings
In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pp. 225–230, IEEE 2021.
@inproceedings{kodela2021predicting,
title = {Predicting Opioid Prescriptions based on Patient Demographics in MIMIC-IV},
author = {Snigdha Kodela and Jahnavi Pinnamraju and Judy W Gichoya and Saptarshi Purkayastha},
doi = {10.1109/CBMS52027.2021.00023},
year = {2021},
date = {2021-06-07},
urldate = {2021-01-01},
booktitle = {2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)},
pages = {225--230},
organization = {IEEE},
abstract = {Opioids are widely used analgesics because of their efficacy, mild sedative and anxiolytic properties, and flexibility to administer through multiple routes. Understanding the demographics of the patients receiving these medications helps provide customized care for the susceptible group of people. We conducted a demographic evaluation of the frequently prescribed opioid drug prescriptions from the MIMIC IV database. We analyzed prescribing patterns of six commonly used opioids with demographics such as age, gender, ethnicity, marital status, and year predominantly. After conducting exploratory data analysis, we built models using Logistic Regression, Random Forest, and XGBoost to predict opioid prescriptions and demographics for those. We also analyzed the association between demographics and the frequency of prescribed medications for pain management. We found statistically significant differences in opioid prescriptions among the male and female population, married and unmarried, various ages, ethnic groups, and an association with in-hospital deaths.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Tariq, Amara; Celi, Leo Anthony; Newsome, Janice M; Purkayastha, Saptarshi; Bhatia, Neal Kumar; Trivedi, Hari; Gichoya, Judy Wawira; Banerjee, Imon
Patient-specific COVID-19 resource utilization prediction using fusion AI model Journal Article
In: NPJ digital medicine, vol. 4, no. 1, pp. 1–9, 2021.
@article{tariq2021patient,
title = {Patient-specific COVID-19 resource utilization prediction using fusion AI model},
author = {Amara Tariq and Leo Anthony Celi and Janice M Newsome and Saptarshi Purkayastha and Neal Kumar Bhatia and Hari Trivedi and Judy Wawira Gichoya and Imon Banerjee},
doi = {10.1038/s41746-021-00461-0},
year = {2021},
date = {2021-06-03},
urldate = {2021-01-01},
journal = {NPJ digital medicine},
volume = {4},
number = {1},
pages = {1--9},
publisher = {Nature Publishing Group},
abstract = {The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient’s need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1\textendash86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importance analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purkayastha, Saptarshi; Goyal, Shreya; Oluwalade, Bolu; Phillips, Tyler; Wu, Huanmei; Zou, Xukai
Usability and Security of Different Authentication Methods for an Electronic Health Records System Conference
Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (HEALTHINF), 2021.
@conference{purkayastha2021usability,
title = {Usability and Security of Different Authentication Methods for an Electronic Health Records System},
author = {Saptarshi Purkayastha and Shreya Goyal and Bolu Oluwalade and Tyler Phillips and Huanmei Wu and Xukai Zou},
year = {2021},
date = {2021-03-13},
booktitle = {Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (HEALTHINF)},
abstract = {We conducted a survey of 67 graduate students enrolled in the Privacy and Security in Healthcare course at Indiana University Purdue University Indianapolis. This was done to measure user preference and their understanding of usability and security of three different Electronic Health Records authentication methods: single authentication method (username and password), Single sign-on with Central Authentication Service (CAS) authentication method, and a bio-capsule facial authentication method. This research aims to explore the relationship between security and usability, and measure the effect of perceived security on usability in these three aforementioned authentication methods. We developed a formative-formative Partial Least Square Structural Equation Modeling (PLS-SEM) model to measure the relationship between the latent variables of Usability, and Security. The measurement model was developed using five observed variables (measures).-Efficiency and Effectiveness, Satisfaction, Preference, Concerns, and Confidence. The results obtained highlight the importance and impact of these measures on the latent variables and the relationship among the latent variables. From the PLS-SEM analysis, it was found that security has a positive impact on usability for Single sign-on and bio-capsule facial authentication methods. We conclude that the facial authentication method was the most secure and usable among the three authentication methods. Further, descriptive analysis was done to draw out the interesting findings from the survey regarding the observed variables.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Umoren, Rachel A; Patel, Shruti; Bucher, Sherri L; Esamai, Fabian; Ezeaka, Chinyere; Muinga, Naomi; Edgcombe, Hilary; Ezenwa, Beatrice; Fajolu, Iretiola; Feltner, John; others,
2021.
@misc{umoren2021attitudes,
title = {Attitudes Of Healthcare Workers In Low-Resource Settings To Mobile Virtual Reality Simulations For Newborn Resuscitation Training--A Report From The eHBB/mHBS Study},
author = {Rachel A Umoren and Shruti Patel and Sherri L Bucher and Fabian Esamai and Chinyere Ezeaka and Naomi Muinga and Hilary Edgcombe and Beatrice Ezenwa and Iretiola Fajolu and John Feltner and others},
doi = {10.1542/peds.147.3MA3.229},
year = {2021},
date = {2021-03-01},
urldate = {2021-01-01},
publisher = {Am Acad Pediatrics},
abstract = {Background: Virtual simulations provide opportunities for interactive learning, problem-solving, and standardized feedback. Little is known about the attitudes of healthcare workers to using mobile virtual reality (VR) simulations for newborn resuscitation training. Objective: To describe the perceptions and attitudes of healthcare workers in low resource settings towards using mobile VR simulations for neonatal resuscitation training. Methods: From July 2018 to September 2019, nine focus group discussions (FGD) with 5-8 participants per group were held with healthcare workers enrolled in the eHBB/mHBS study on using mobile VR simulations for nedwborn resuscitation training in Nigeria and Kenya. The focus group facilitators used a semi-structured interview guide designed to elicit participants’ experiences with and opinions about using mobile VR for healthcare education in a low resource setting. FGD were audio-recorded and transcribed for qualitative analysis. Data were organized using NVIVO 12 software [QSI International] was used to organize the data...},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Purkayastha, Saptarshi; Trivedi, Hari; Gichoya, Judy Wawira
Failures hiding in success for artificial intelligence in radiology Journal Article
In: Journal of the American College of Radiology, vol. 18, no. 3, pp. 517–519, 2021.
@article{purkayastha2021failuresb,
title = {Failures hiding in success for artificial intelligence in radiology},
author = {Saptarshi Purkayastha and Hari Trivedi and Judy Wawira Gichoya},
doi = {10.1016/j.jacr.2020.11.008},
year = {2021},
date = {2021-03-01},
urldate = {2021-01-01},
journal = {Journal of the American College of Radiology},
volume = {18},
number = {3},
pages = {517--519},
publisher = {Elsevier},
abstract = {Reports of computer algorithms outperforming radiologists have persisted over the last 15 years, starting with the 2005 publication by Rubin et al on detecting pulmonary nodules from CT scans [ 1
]. Back then, these technologies were referred to as computer-aided diagnosis, which could be considered as a precursor, of sorts, to what is now referred to broadly as artificial intelligence (AI). Technology gains in hardware over the past 5 years have facilitated the training of deep neural networks with millions of parameters, exponentially accelerating the pace of AI publications. However, like every other scientific field, successes of AI in radiology are published and publicized with much fanfare, and failures are not discussed or made public. In fact, most AI failures are discovered anecdotally from personal experience or when shared in social media as tweets or blog posts. In this article, we discuss some pitfalls frequently encountered in reporting the success of AI in radiology, which might be considered failures when considered differently.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
]. Back then, these technologies were referred to as computer-aided diagnosis, which could be considered as a precursor, of sorts, to what is now referred to broadly as artificial intelligence (AI). Technology gains in hardware over the past 5 years have facilitated the training of deep neural networks with millions of parameters, exponentially accelerating the pace of AI publications. However, like every other scientific field, successes of AI in radiology are published and publicized with much fanfare, and failures are not discussed or made public. In fact, most AI failures are discovered anecdotally from personal experience or when shared in social media as tweets or blog posts. In this article, we discuss some pitfalls frequently encountered in reporting the success of AI in radiology, which might be considered failures when considered differently.
Umoren, Rachel A; Esamai, Fabian; Ezeaka, Chinyere; Ezenwa, Beatrice; Fajolu, Iretiola; Nafula, Mary; Makokha, Felicitas; Hippe, Dan; Feltner, John; Patel, Shruti; others,
2021.
@misc{umoren2021ehbb,
title = {eHBB: A Randomized Controlled Trial Of Virtual Reality For Newborn Resuscitation Refresher Training Of Healthcare Workers In Nigeria And Kenya},
author = {Rachel A Umoren and Fabian Esamai and Chinyere Ezeaka and Beatrice Ezenwa and Iretiola Fajolu and Mary Nafula and Felicitas Makokha and Dan Hippe and John Feltner and Shruti Patel and others},
doi = {10.1542/peds.147.3MA3.237},
year = {2021},
date = {2021-03-01},
urldate = {2021-01-01},
publisher = {Am Acad Pediatrics},
abstract = {Background: Each year, there are 2.8 million newborn deaths, most of which are preventable. Intrapartum asphyxia is one of the three leading causes of neonatal mortality. In 2017, poor quality of care accounted for almost 1 million neonatal deaths, mostly during the intrapartum period. As the majority of these deaths occur in low- and middle-income country settings where there is high penetrance of mobile devices, we hypothesized that mobile virtual reality (VR) simulation refresher training in neonatal resuscitation (NR) would support the maintenance of HCW NR skills over time. Methods: Healthcare workers who work in labor and delivery and newborn care units at secondary and tertiary healthcare facilities in Lagos, Nigeria and Busia, Western Kenya and who had not received training in Helping Babies Breathe in the past one year were recruited to participate in the study. Participants were consented and randomized to receive the eHBB VR + digital manual,...},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Bucher, Sherri L; Rajapuri, Anushri; Ravindran, Radhika; Rukunga, Janet; Horan, Kevin; Esamai, Fabian; Purkayastha, Saptarshi
2021.
@misc{bucher2021essential,
title = {The Essential Care For Every Baby Digital Action Plan: Design And Usability Testing Of A Mobile Phone-Based Newborn Care Decision Support Tool In Kenya},
author = {Sherri L Bucher and Anushri Rajapuri and Radhika Ravindran and Janet Rukunga and Kevin Horan and Fabian Esamai and Saptarshi Purkayastha},
doi = {10.1542/peds.147.3MA3.263},
year = {2021},
date = {2021-03-01},
urldate = {2021-01-01},
publisher = {American Academy of Pediatrics},
abstract = {Background: Each year, there are 2.5 million neonatal deaths, primarily within low/middle-income countries (LMICs). Helping Babies Survive educational and training programs, including Essential Care for Every Baby (ECEB), equip LMIC healthcare providers (HCPs) with the knowledge, skills, and competencies to save newborn lives. Concurrently, growing access to mobile phones and improved network connectivity in LM4ICs open the possibility for digital decision support tools to assist HCPs to provide higher quality newborn care. Our team has developed an integrated suite of apps, mobile Helping Babies Survive powered by DHIS2 (mHBS/DHIS2), which are purpose-built to support effective implementation of Helping Babies Survive initiatives around the world. Existing mHBS/DHIS2 functionality includes: education, training, monitoring and evaluation, and quality improvement for Helping Babies Breathe. Here, we describe expanding mHBS/DHIS2 capabilities to include decision support for ECEB. Purpose: To describe the design and evaluation of a novel mobile phone-based digital decision-support tool for essential newborn...},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Purkayastha, Saptarshi; Trivedi, Hari; Gichoya, Judy Wawira
Failures hiding in success for artificial intelligence in radiology Journal Article
In: Journal of the American College of Radiology, vol. 18, no. 3, pp. 517-519, 2021.
@article{purkayastha2021failures,
title = {Failures hiding in success for artificial intelligence in radiology},
author = {Saptarshi Purkayastha and Hari Trivedi and Judy Wawira Gichoya},
doi = {10.1016/j.jacr.2020.11.008},
year = {2021},
date = {2021-03-01},
journal = {Journal of the American College of Radiology},
volume = {18},
number = {3},
pages = {517-519},
abstract = {Reports of computer algorithms outperforming radiologists have persisted over the last 15 years, starting with the 2005 publication by Rubin et al on detecting pulmonary nodules from CT scans. Back then, these technologies were referred to as computer-aided diagnosis, which could be considered as a precursor, of sorts, to what is now referred to broadly as artificial intelligence (AI). Technology gains in hardware over the past 5 years have facilitated the training of deep neural networks with millions of parameters, exponentially accelerating the pace of AI publications. However, like every other scientific field, the successes of AI in radiology are published and publicized with much fanfare, and failures are not discussed or made public. In fact, most AI failures are discovered anecdotally from personal experience or when shared in social media as tweets or blog posts. In this article, we discuss some pitfalls frequently encountered in reporting the success of AI in radiology, which might be considered failures when considered differently. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Oluwalade, Bolu; Neela, Sunil; Wawira, Judy; Adejumo, Tobiloba; Purkayastha, Saptarshi
Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data Conference
Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021), vol. 5, Science and Technology Publications, 2021, ISBN: 978-989-758-490-9.
@conference{oluwalade2021human,
title = {Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data},
author = {Bolu Oluwalade and Sunil Neela and Judy Wawira and Tobiloba Adejumo and Saptarshi Purkayastha},
doi = {10.5220/0010325906450650},
isbn = {978-989-758-490-9},
year = {2021},
date = {2021-02-11},
booktitle = {Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021)},
volume = {5},
pages = {645-650},
publisher = {Science and Technology Publications},
abstract = {In recent years, human activity recognition has garnered considerable attention both in industrial and academic research because of the wide deployment of sensors, such as accelerometers and gyroscopes, in products such as smartphones and smartwatches. Activity recognition is currently applied in various fields where valuable information about an individual’s functional ability and lifestyle is needed. In this study, we used the popular WISDM dataset for activity recognition. Using multivariate analysis of covariance (MANCOVA), we established a statistically significant difference (p< 0.05) between the data generated from the sensors embedded in smartphones and smartwatches. By doing this, we show that smartphones and smartwatches don’t capture data in the same way due to the location where they are worn. We deployed several neural network architectures to classify 15 different hand and non-hand-oriented activities. These models include Long short-term memory (LSTM), Bi-directional Long short-term memory (BiLSTM), Convolutional Neural Network (CNN), and Convolutional LSTM (ConvLSTM). The developed models performed best with watch accelerometer data. Also, we saw that the classification precision obtained with the convolutional input classifiers (CNN and ConvLSTM) was higher than the end-to-end LSTM classifier in 12 of the 15 activities. Additionally, the CNN model for the watch accelerometer was better able to classify non-hand oriented activities when compared to hand-oriented activities.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Purkayastha, Saptarshi
Electronic Patient Records as a Substrate for Collaboration for Distributed Care in Low-Resource Contexts Inproceedings
In: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pp. 289–295, IEEE 2021.
@inproceedings{purkayastha2021electronic,
title = {Electronic Patient Records as a Substrate for Collaboration for Distributed Care in Low-Resource Contexts},
author = {Saptarshi Purkayastha},
doi = {10.1109/ICHI52183.2021.00052},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)},
pages = {289--295},
organization = {IEEE},
abstract = {Collaboration to provide patient care in low-resource contexts has been a challenge due to heavy patient load, limited connectivity, and knowledge-gap between primary and tertiary care. Through the design, development, and implementation of a private social network-connected, large-scale hospital information system, which has scaled to several zonal and district hospitals in a small hilly country in South East Asia, we present the case study of a system that has enabled collaboration. Using coordination mechanisms as a theoretical framework, we discuss some methods of collaboration. In the paper, we present electronic patient records (EPR) as the substrate that enables collaboration between providers, departments, developers throughout the health systems. In our analysis, we present useful learnings of collaboration between provider-provider, developer-developer, provider-patient, implementer-provider, and how the balance of these is a necessary condition to create a useful substrate for collaboration.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2020
Tariq, Amara; Purkayastha, Saptarshi; Padmanaban, Geetha Priya; Krupinski, Elizabeth; Trivedi, Hari; Banerjee, Imon; Gichoya, Judy Wawira
Current clinical applications of artificial intelligence in radiology and their best supporting evidence Journal Article
In: Journal of the American College of Radiology, vol. 17, no. 11, pp. 1371-1381, 2020.
@article{tariq2020current,
title = {Current clinical applications of artificial intelligence in radiology and their best supporting evidence},
author = {Amara Tariq and Saptarshi Purkayastha and Geetha Priya Padmanaban and Elizabeth Krupinski and Hari Trivedi and Imon Banerjee and Judy Wawira Gichoya},
doi = {10.1016/j.jacr.2020.08.018},
year = {2020},
date = {2020-11-02},
urldate = {2020-11-02},
journal = {Journal of the American College of Radiology},
volume = {17},
number = {11},
pages = {1371-1381},
publisher = {Elsevier},
abstract = {Purpose
Despite tremendous gains from deep learning and the promise of artificial intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large translational gap to implement and use AI products in real-world clinical situations. Adoption of standards such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Consolidated Standards of Reporting Trials, and the Checklist for Artificial Intelligence in Medical Imaging is increasing to improve the peer-review process and reporting of AI tools. However, no such standards exist for product-level review.
Methods
A review of clinical trials showed a paucity of evidence for radiology AI products; thus, the authors developed a 10-question assessment tool for reviewing AI products with an emphasis on their validation and result dissemination. The assessment tool was applied to commercial and open-source algorithms used for diagnosis to extract evidence on the clinical utility of the tools.
Results
There is limited technical information on methodologies for FDA-approved algorithms compared with open-source products, likely because of intellectual property concerns. Furthermore, FDA-approved products use much smaller data sets compared with open-source AI tools, because the terms of use of public data sets are limited to academic and noncommercial entities, which precludes their use in commercial products.
Conclusions
Overall, this study reveals a broad spectrum of maturity and clinical use of AI products, but a large gap exists in exploring the actual performance of AI tools in clinical practice.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Despite tremendous gains from deep learning and the promise of artificial intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large translational gap to implement and use AI products in real-world clinical situations. Adoption of standards such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Consolidated Standards of Reporting Trials, and the Checklist for Artificial Intelligence in Medical Imaging is increasing to improve the peer-review process and reporting of AI tools. However, no such standards exist for product-level review.
Methods
A review of clinical trials showed a paucity of evidence for radiology AI products; thus, the authors developed a 10-question assessment tool for reviewing AI products with an emphasis on their validation and result dissemination. The assessment tool was applied to commercial and open-source algorithms used for diagnosis to extract evidence on the clinical utility of the tools.
Results
There is limited technical information on methodologies for FDA-approved algorithms compared with open-source products, likely because of intellectual property concerns. Furthermore, FDA-approved products use much smaller data sets compared with open-source AI tools, because the terms of use of public data sets are limited to academic and noncommercial entities, which precludes their use in commercial products.
Conclusions
Overall, this study reveals a broad spectrum of maturity and clinical use of AI products, but a large gap exists in exploring the actual performance of AI tools in clinical practice.
Bucher, Sherri; Hoilett, Orlando; Bluhm, Nicholas; Walters, Benjamin; Pickering, Alyson; Purkayastha, Saptarshi; Linnes, Jacqueline C; Esamai, Fabian; Ummel, Jason; Ekhaguere, Osayame
Wireless vital signs monitoring of opioid-exposed newborns during skin-to-skin care: Biomedical device innovation Presentation
28.10.2020.
@misc{bucher2020wireless,
title = {Wireless vital signs monitoring of opioid-exposed newborns during skin-to-skin care: Biomedical device innovation},
author = {Sherri Bucher and Orlando Hoilett and Nicholas Bluhm and Benjamin Walters and Alyson Pickering and Saptarshi Purkayastha and Jacqueline C Linnes and Fabian Esamai and Jason Ummel and Osayame Ekhaguere},
url = {https://apha.confex.com/apha/2020/meetingapp.cgi/Paper/477733},
year = {2020},
date = {2020-10-28},
booktitle = {APHA's 2020 VIRTUAL Annual Meeting and Expo (Oct. 24-28)},
publisher = {American Public Health Association},
abstract = {Background/Significance: Indiana has one of the highest rates of infant mortality in the United States. Premature birth, low birthweight, and in utero exposure to drugs of abuse are leading causes of neonatal complications. In 2016, 22% of umbilical cord blood samples from Indiana newborns tested positive for exposure to opiates. Health care providers are increasingly utilizing non-pharmacological strategies to manage the symptoms of opioid-exposed babies with neonatal abstinence syndrome (NAS). NAS babies require frequent vital signs monitoring, which can be a barrier to prolonged skin-to-skin care (STS).
Methods: Our multidisciplinary, cross-institutional team utilized human-centered and participatory design, agile development, qualitative (focus group discussions; key informant interviews), and quantitative assessments to design, develop, build, and evaluate an integrated biomedical device/digital health solution.
Results: Our 3-part, bundled innovation includes: (1) Built prototype of a wearable biomedical device with two integrated components: a carrier (worn by an adult caregiver) and a “pouch” (worn by the infant); (2) Wireless sensor technology which automatically, continuously, and accurately monitors key infant vital signs including body temperature, breathing, and heart rate, across three device use modes, including during STS care among adult caregiver-newborn dyads; (3) Android app which collects and displays device and infant vital signs monitoring information, and provides a platform for digitized educational resources. Feasibility assessments indicate broad acceptability of the device and app among health care providers, parents, and family stakeholders. Engineering verification has confirmed that our wireless sensor technology for measuring temperature, heart and respiratory rate during STS compares to the gold standard (impedance pneumography).
Conclusion: To facilitate non-pharmacological management of opioid-exposed babies, we have developed a prototype of a wearable biomedical device with built-in sensor technology for continuous wireless vital signs monitoring. Infant vital signs information is collected, and displayed, on an Android app. This innovative device potentially equips health care providers with additional tools to support non-pharmacological management of opioid-exposed babies.},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Methods: Our multidisciplinary, cross-institutional team utilized human-centered and participatory design, agile development, qualitative (focus group discussions; key informant interviews), and quantitative assessments to design, develop, build, and evaluate an integrated biomedical device/digital health solution.
Results: Our 3-part, bundled innovation includes: (1) Built prototype of a wearable biomedical device with two integrated components: a carrier (worn by an adult caregiver) and a “pouch” (worn by the infant); (2) Wireless sensor technology which automatically, continuously, and accurately monitors key infant vital signs including body temperature, breathing, and heart rate, across three device use modes, including during STS care among adult caregiver-newborn dyads; (3) Android app which collects and displays device and infant vital signs monitoring information, and provides a platform for digitized educational resources. Feasibility assessments indicate broad acceptability of the device and app among health care providers, parents, and family stakeholders. Engineering verification has confirmed that our wireless sensor technology for measuring temperature, heart and respiratory rate during STS compares to the gold standard (impedance pneumography).
Conclusion: To facilitate non-pharmacological management of opioid-exposed babies, we have developed a prototype of a wearable biomedical device with built-in sensor technology for continuous wireless vital signs monitoring. Infant vital signs information is collected, and displayed, on an Android app. This innovative device potentially equips health care providers with additional tools to support non-pharmacological management of opioid-exposed babies.
Goyal, Shreya; Purkayastha, Saptarshi; Phillips, Tyler; Quick, Rob; Britt, Alexis
Enabling Secure and Effective Biomedical Data Sharing through Cyberinfrastructure Gateways Conference
Gateways 2020, 2020.
@conference{goyal2020enabling,
title = {Enabling Secure and Effective Biomedical Data Sharing through Cyberinfrastructure Gateways},
author = {Shreya Goyal and Saptarshi Purkayastha and Tyler Phillips and Rob Quick and Alexis Britt},
doi = {10.17605/OSF.IO/6Y8WG},
year = {2020},
date = {2020-10-19},
urldate = {2020-10-19},
booktitle = {Gateways 2020},
journal = {arXiv preprint arXiv:2012.12835},
abstract = {Dynaswap project reports on developing a coherently integrated and trustworthy holistic secure workflow protection architecture for cyberinfrastructures which can be used on virtual machines deployed through cyberinfrastructure (CI) services such as JetStream. This service creates a user-friendly cloud environment designed to give researchers access to interactive computing and data analysis resources on demand. The Dynaswap cybersecurity architecture supports roles, role hierarchies, and data hierarchies, as well as dynamic changes of roles and hierarchical relations within the scientific infrastructure. Dynaswap combines existing cutting-edge security frameworks (including an Authentication Authorization-Accounting framework, Multi-Factor Authentication, Secure Digital Provenance, and Blockchain) with advanced security tools (e.g., Biometric-Capsule, Cryptography-based Hierarchical Access Control, and Dual-level Key Management). The CI is being validated in life-science research environments and in the education settings of Health Informatics. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Bucher, Sherri L.; Cardellichio, Peter; Muinga, Naomi; Patterson, Jackie K.; Thukral, Anu; Deorari, Ashok K.; Data, Santorino; Umoren, Rachel; Purkayastha, Saptarshi
Digital health innovations, tools, and resources to support Helping Babies Survive programs Journal Article
In: Pediatrics, vol. 146, no. Supplement 2, pp. S165-S182, 2020, ISSN: 0031-4005.
@article{bucher2020digital,
title = {Digital health innovations, tools, and resources to support Helping Babies Survive programs},
author = {Sherri L. Bucher and Peter Cardellichio and Naomi Muinga and Jackie K. Patterson and Anu Thukral and Ashok K. Deorari and Santorino Data and Rachel Umoren and Saptarshi Purkayastha},
url = {https://pediatrics.aappublications.org/content/146/Supplement_2/S165},
doi = {10.1542/peds.2020-016915I},
issn = {0031-4005},
year = {2020},
date = {2020-10-01},
journal = {Pediatrics},
volume = {146},
number = {Supplement 2},
pages = {S165-S182},
abstract = {The Helping Babies Survive (HBS) initiative features a suite of evidence-based curricula and simulation-based training programs designed to provide health workers in low- and middle-income countries (LMICs) with the knowledge, skills, and competencies to prevent, recognize, and manage leading causes of newborn morbidity and mortality. Global scale-up of HBS initiatives has been rapid. As HBS initiatives rolled out across LMIC settings, numerous bottlenecks, gaps, and barriers to the effective, consistent dissemination and implementation of the programs, across both the pre- and in-service continuums, emerged. Within the first decade of expansive scale-up of HBS programs, mobile phone ownership and access to cellular networks have also concomitantly surged in LMICs. In this article, we describe a number of HBS digital health innovations and resources that have been developed from 2010 to 2020 to support education and training, data collection for monitoring and evaluation, clinical decision support, and quality improvement. Helping Babies Survive partners and stakeholders can potentially integrate the described digital tools with HBS dissemination and implementation efforts in a myriad of ways to support low-dose high-frequency skills practice, in-person refresher courses, continuing medical and nursing education, on-the-job training, or peer-to-peer learning, and strengthen data collection for key newborn care and quality improvement indicators and outcomes. Thoughtful integration of purpose-built digital health tools, innovations, and resources may assist HBS practitioners to more effectively disseminate and implement newborn care programs in LMICs, and facilitate progress toward the achievement of Sustainable Development Goal health goals, targets, and objectives.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bhimireddy, Ananth; Sinha, Priyanshu; Oluwalade, Bolu; Gichoya, Judy Wawira; Purkayastha, Saptarshi
Blood Glucose Level Prediction as Time-Series Modeling using Sequence-to-Sequence Neural Networks Inproceedings
In: Bach, Kerstin; Bunescu, Razvan; Marling, Cindy; Wiratunga, Nirmalie (Ed.): Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), pp. 125-130, CEUR-WS Workshop Proceedings, 2020.
@inproceedings{bhimireddy2020blood,
title = {Blood Glucose Level Prediction as Time-Series Modeling using Sequence-to-Sequence Neural Networks},
author = {Ananth Bhimireddy and Priyanshu Sinha and Bolu Oluwalade and Judy Wawira Gichoya and Saptarshi Purkayastha},
editor = {Kerstin Bach and Razvan Bunescu and Cindy Marling and Nirmalie Wiratunga},
url = {http://ceur-ws.org/Vol-2675/paper22.pdf},
year = {2020},
date = {2020-09-17},
booktitle = {Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data
co-located with 24th European Conference on Artificial Intelligence (ECAI 2020)},
volume = {Vol-2675},
pages = {125-130},
publisher = {CEUR-WS Workshop Proceedings},
abstract = {The management of blood glucose levels is critical in the care of Type 1 diabetes subjects. In extremes, high or low levels of blood glucose are fatal. To avoid such adverse events, there is the development and adoption of wearable technologies that continuously monitor blood glucose and administer insulin. This technology allows subjects to easily track their blood glucose levels with early intervention, preventing the need for hospital visits. The data collected from these sensors is an excellent candidate for the application of machine learning algorithms to learn patterns and predict future values of blood glucose levels. In this study, we developed artificial neural network algorithms based on the OhioT1DM training dataset that contains data on 12 subjects. The dataset contains features such as subject identifiers, continuous glucose monitoring data obtained in 5 minutes intervals, insulin infusion rate, etc. We developed individual models, including LSTM, BiLSTM, Convolutional LSTMs, TCN, and sequence-to-sequence models. We also developed transfer learning models based on the most important features of the data, as identified by a gradient boosting algorithm. These models were evaluated on the OhioT1DM test dataset that contains 6 unique subjects data. The model with the lowest RMSE values for the 30- and 60-minutes was selected as the best performing model. Our result shows that sequence-to-sequence BiLSTM performed better than the other models. This work demonstrates the potential of artificial neural networks algorithms in the management of Type 1 diabetes.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rajapuri, Anushri S; Ravindran, Radhika; Horan, Kevin; Bucher, Sherri; Purkayastha, Saptarshi
Essential Care for Every Baby: Neonatal Clinical Decision Support Tool Conference
International Conference on Applied Human Factors and Ergonomics, vol. 1205, 2020, ISBN: 978-3-030-50837-1.
@conference{rajapuri2020essential,
title = {Essential Care for Every Baby: Neonatal Clinical Decision Support Tool},
author = {Anushri S Rajapuri and Radhika Ravindran and Kevin Horan and Sherri Bucher and Saptarshi Purkayastha},
editor = {Kalra J., Lightner N},
doi = {https://doi.org/10.1007/978-3-030-50838-8_26},
isbn = {978-3-030-50837-1},
year = {2020},
date = {2020-07-16},
booktitle = {International Conference on Applied Human Factors and Ergonomics},
volume = {1205},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Phillips, Tyler; Yu, Xiaoyuan; Haakenson, Brandon; Goyal, Shreya; Zou, Xukai; Purkayastha, Saptarshi; Wu, Huanmei
AuthN-AuthZ: Integrated, User-Friendly and Privacy-Preserving Authentication and Authorization Inproceedings
In: 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 189–198, IEEE 2020.
@inproceedings{phillips2020authn,
title = {AuthN-AuthZ: Integrated, User-Friendly and Privacy-Preserving Authentication and Authorization},
author = {Tyler Phillips and Xiaoyuan Yu and Brandon Haakenson and Shreya Goyal and Xukai Zou and Saptarshi Purkayastha and Huanmei Wu},
year = {2020},
date = {2020-01-01},
booktitle = {2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)},
pages = {189--198},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Balthazar, Patricia; Tajmir, Shahein H; Ortiz, Daniel A; Herse, Catherine C; Shea, Lindsey AG; Seals, Kevin F; Cohen-Addad, Dan; Purkayastha, Saptarshi; Gichoya, Judy W
The Artificial Intelligence Journal Club (# RADAIJC): A Multi-Institutional Resident-Driven Web-Based Educational Initiative Journal Article
In: Academic radiology, vol. 27, no. 1, pp. 136–139, 2020.
@article{balthazar2020artificial,
title = {The Artificial Intelligence Journal Club (# RADAIJC): A Multi-Institutional Resident-Driven Web-Based Educational Initiative},
author = {Patricia Balthazar and Shahein H Tajmir and Daniel A Ortiz and Catherine C Herse and Lindsey AG Shea and Kevin F Seals and Dan Cohen-Addad and Saptarshi Purkayastha and Judy W Gichoya},
year = {2020},
date = {2020-01-01},
journal = {Academic radiology},
volume = {27},
number = {1},
pages = {136--139},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purkayastha, Saptarshi; Addepally, Siva Abhishek; Bucher, Sherri
Engagement and Usability of a Cognitive Behavioral Therapy Mobile App Compared With Web-Based Cognitive Behavioral Therapy Among College Students: Randomized Heuristic Trial Journal Article
In: JMIR Human Factors, vol. 7, no. 1, pp. e14146, 2020.
@article{purkayastha2020engagement,
title = {Engagement and Usability of a Cognitive Behavioral Therapy Mobile App Compared With Web-Based Cognitive Behavioral Therapy Among College Students: Randomized Heuristic Trial},
author = {Saptarshi Purkayastha and Siva Abhishek Addepally and Sherri Bucher},
year = {2020},
date = {2020-01-01},
journal = {JMIR Human Factors},
volume = {7},
number = {1},
pages = {e14146},
publisher = {JMIR Publications Inc., Toronto, Canada},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Singh, AK; Guntu, Mounika; Bhimireddy, Ananth Reddy; Gichoya, Judy W; Purkayastha, Saptarshi
Multi-label natural language processing to identify diagnosis and procedure codes from MIMIC-III inpatient notes Journal Article
In: arXiv preprint arXiv:2003.07507, 2020.
@article{singh2020multi,
title = {Multi-label natural language processing to identify diagnosis and procedure codes from MIMIC-III inpatient notes},
author = {AK Singh and Mounika Guntu and Ananth Reddy Bhimireddy and Judy W Gichoya and Saptarshi Purkayastha},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2003.07507},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kathiravelu, Pradeeban; Sharma, Ashish; Purkayastha, Saptarshi; Sinha, Priyanshu; Cadrin-Chenevert, Alexandre; Banerjee, Imon; Gichoya, Judy Wawira
Developing and Deploying Machine Learning Pipelines against Real-Time Image Streams from the PACS Journal Article
In: arXiv preprint arXiv:2004.07965, 2020.
@article{kathiravelu2020developing,
title = {Developing and Deploying Machine Learning Pipelines against Real-Time Image Streams from the PACS},
author = {Pradeeban Kathiravelu and Ashish Sharma and Saptarshi Purkayastha and Priyanshu Sinha and Alexandre Cadrin-Chenevert and Imon Banerjee and Judy Wawira Gichoya},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2004.07965},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mathur, Varoon; Purkayashtha, Saptarshi; Gichoya, Judy Wawira
Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care Journal Article
In: arXiv preprint arXiv:2005.12378, 2020.
@article{mathur2020artificial,
title = {Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care},
author = {Varoon Mathur and Saptarshi Purkayashtha and Judy Wawira Gichoya},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2005.12378},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Umoren, Rachel A; Bucher, Sherri; Purkayastha, Saptarshi; Ezeaka, Chinyere; Esamai, Fabian; Mairami, Amsa; Asangansi, Ime; Bresnahan, Brian; Paton, Chris
eHBB/mHBS-DHIS2: Mobile Virtual Reality Provider Training in Helping Babies Breathetextregistered Miscellaneous
2020.
@misc{umoren2020ehbb,
title = {eHBB/mHBS-DHIS2: Mobile Virtual Reality Provider Training in Helping Babies Breathetextregistered},
author = {Rachel A Umoren and Sherri Bucher and Saptarshi Purkayastha and Chinyere Ezeaka and Fabian Esamai and Amsa Mairami and Ime Asangansi and Brian Bresnahan and Chris Paton},
year = {2020},
date = {2020-01-01},
publisher = {American Academy of Pediatrics},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Umoren, Rachel A; Ezeaka, Chinyere; Esamai, Fabian; Kshatriya, Bhavani Agnikula; Avanigadda, Prem; Clopp, Bailey; Ezenwa, Beatrice; Fajolu, Iretiola; Feltner, John; Makokha, Felicitas; others,
Pre-Training Cognitive and Psychomotor Gaps in Healthcare Worker Neonatal Resuscitation Skills for Helping Babies Breathe--A Report from the eHBB/mHBS Study Miscellaneous
2020.
@misc{umoren2020pre,
title = {Pre-Training Cognitive and Psychomotor Gaps in Healthcare Worker Neonatal Resuscitation Skills for Helping Babies Breathe--A Report from the eHBB/mHBS Study},
author = {Rachel A Umoren and Chinyere Ezeaka and Fabian Esamai and Bhavani Agnikula Kshatriya and Prem Avanigadda and Bailey Clopp and Beatrice Ezenwa and Iretiola Fajolu and John Feltner and Felicitas Makokha and others},
year = {2020},
date = {2020-01-01},
publisher = {American Academy of Pediatrics},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Banerjee, Imon; Sinha, Priyanshu; Purkayastha, Saptarshi; Mashhaditafreshi, Nazanin; Tariq, Amara; Jeong, Jiwoong; Trivedi, Hari; Gichoya, Judy W
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications Journal Article
In: arXiv preprint arXiv:2006.13262, 2020.
@article{banerjee2020there,
title = {Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications},
author = {Imon Banerjee and Priyanshu Sinha and Saptarshi Purkayastha and Nazanin Mashhaditafreshi and Amara Tariq and Jiwoong Jeong and Hari Trivedi and Judy W Gichoya},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2006.13262},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2019
Nuthakki, Siddhartha; Bucher, Sherri; Purkayastha, Saptarshi
The Development and Usability Testing of a Decision Support Mobile App for the Essential Care for Every Baby (ECEB) Program Conference
International Conference on Human-Computer Interaction Springer, Cham, 2019.
@conference{Nuthakki2019,
title = {The Development and Usability Testing of a Decision Support Mobile App for the Essential Care for Every Baby (ECEB) Program},
author = {Siddhartha Nuthakki and Sherri Bucher and Saptarshi Purkayastha},
year = {2019},
date = {2019-07-26},
pages = {259-263},
publisher = {Springer, Cham},
organization = {International Conference on Human-Computer Interaction},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Purkayastha, Saptarshi; Buddi, Surendra Babu; Nuthakki, Siddhartha; Yadav, Bhawana; Gichoya, Judy W
Evaluating the Implementation of Deep Learning in LibreHealth Radiology on Chest X-Rays Conference
Computer Vision Conference, Springer, Cham, 2019.
@conference{Purkayastha2019,
title = {Evaluating the Implementation of Deep Learning in LibreHealth Radiology on Chest X-Rays},
author = {Saptarshi Purkayastha and Surendra Babu Buddi and Siddhartha Nuthakki and Bhawana Yadav and Judy W Gichoya},
url = {https://scholarworks.iupui.edu/bitstream/handle/1805/18297/paper_258.pdf},
year = {2019},
date = {2019-04-25},
booktitle = {Computer Vision Conference},
pages = {648-657},
publisher = {Springer, Cham},
abstract = {Respiratory diseases are the dominant cause of deaths worldwide. In the US, the number of deaths due to chronic lung infections (mostly pneumonia and tuberculosis), lung cancer and chronic obstructive pulmonary disease has increased. The timely and accurate diagnosis of the disease is highly imperative to diminish the deaths. A chest X-ray is a vital diagnostic tool used for diagnosing lung diseases. Delay in X-Ray diagnosis is run-of-the-mill milieu and the reasons for the impediment are mostly because the X-ray reports are arduous to interpret, due to the complex visual contents of radiographs containing superimposed anatomical structures. A shortage of trained radiologists is another cause of increased workload and thus delay. We integrated CheXNet, a neural network algorithm into the LibreHealth Radiology Information System, which allows physicians to upload Chest X-rays and identify diagnosis probabilities. The uploaded images are evaluated from labels for 14 thoracic diseases. The turnaround time for each evaluation is about 30 seconds, which does not affect clinical workflow. A Python Flask application hosted web service is used to upload radiographs into a GPU server containing the algorithm. Thus, the use of this system is not limited to clients having their GPU server, but instead, we provide a web service. To evaluate the model, we randomly split the dataset into training (70%), validation (10%) and test (20%) sets. With over 86% accuracy and turnaround time under 30 seconds, the application demonstrates the feasibility of a web service for machine learning-based diagnosis of 14-lung pathologies from Chest X-rays.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Purkayastha, Saptarshi; Allam, Roshini; Maity, Pallavi; Gichoya, Judy W
Comparison of Open-Source Electronic Health Record Systems Based on Functional and User Performance Criteria Journal Article
In: Healthcare informatics research, vol. 25, no. 2, pp. 89-98, 2019.
@article{Purkayastha2019b,
title = {Comparison of Open-Source Electronic Health Record Systems Based on Functional and User Performance Criteria},
author = {Saptarshi Purkayastha and Roshini Allam and Pallavi Maity and Judy W Gichoya},
url = {https://synapse.koreamed.org/DOIx.php?id=10.4258/hir.2019.25.2.89},
doi = {doi.org/10.4258/hir.2019.25.2.89},
year = {2019},
date = {2019-04-01},
journal = {Healthcare informatics research},
volume = {25},
number = {2},
pages = {89-98},
abstract = {Objectives: Open-source Electronic Health Record (EHR) systems have gained importance. The main aim of our research is to guide organizational choice by comparing the features, functionality, and user-facing system performance of the five most popular open-source EHR systems.
Methods: We performed a qualitative content analysis with a directed approach on recently published literature (2012\textendash2017) to develop an integrated set of criteria to compare the EHR systems. The functional criteria are an integration of the literature, meaningful use criteria, and the Institute of Medicine's functional requirements of EHR, whereas the user-facing system performance is based on the time required to perform basic tasks within the EHR system.
Results: Based on the Alexa web ranking and Google Trends, the five most popular EHR systems at the time of our study were OSHERA VistA, GNU Health, the Open Medical Record System (OpenMRS), Open Electronic Medical Record (OpenEMR), and OpenEHR. We also found the trends in popularity of the EHR systems and the locations where they were more popular than others. OpenEMR met all the 32 functional criteria, OSHERA VistA met 28, OpenMRS met 12 fully and 11 partially, OpenEHR-based EHR met 10 fully and 3 partially, and GNU Health met the least with only 10 criteria fully and 2 partially.
Conclusions: Based on our functional criteria, OpenEMR is the most promising EHR system, closely followed by VistA. With regard to user-facing system performance, OpenMRS has superior performance in comparison to OpenEMR.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Methods: We performed a qualitative content analysis with a directed approach on recently published literature (2012–2017) to develop an integrated set of criteria to compare the EHR systems. The functional criteria are an integration of the literature, meaningful use criteria, and the Institute of Medicine's functional requirements of EHR, whereas the user-facing system performance is based on the time required to perform basic tasks within the EHR system.
Results: Based on the Alexa web ranking and Google Trends, the five most popular EHR systems at the time of our study were OSHERA VistA, GNU Health, the Open Medical Record System (OpenMRS), Open Electronic Medical Record (OpenEMR), and OpenEHR. We also found the trends in popularity of the EHR systems and the locations where they were more popular than others. OpenEMR met all the 32 functional criteria, OSHERA VistA met 28, OpenMRS met 12 fully and 11 partially, OpenEHR-based EHR met 10 fully and 3 partially, and GNU Health met the least with only 10 criteria fully and 2 partially.
Conclusions: Based on our functional criteria, OpenEMR is the most promising EHR system, closely followed by VistA. With regard to user-facing system performance, OpenMRS has superior performance in comparison to OpenEMR.
Kasthurirathne, Suranga N; Biondich, Paul G; Grannis, Shaun J; Purkayastha, Saptarshi; Vest, Joshua R; Jones, Josette F
In: Journal of medical Internet research, vol. 21, no. 7, pp. e13809, 2019.
@article{Kasthurirathne2019,
title = {Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach},
author = {Suranga N Kasthurirathne and Paul G Biondich and Shaun J Grannis and Saptarshi Purkayastha and Joshua R Vest and Josette F Jones},
url = {https://www.jmir.org/2019/7/e13809/},
doi = {doi:10.2196/13809},
year = {2019},
date = {2019-01-02},
journal = {Journal of medical Internet research},
volume = {21},
number = {7},
pages = {e13809},
publisher = {JMIR Publications Inc., Toronto, Canada},
abstract = {Background: As the most commonly occurring form of mental illness worldwide, depression poses significant health and economic burdens to both the individual and community. Different types of depression pose different levels of risk. Individuals who suffer from mild forms of depression may recover without any assistance or be effectively managed by primary care or family practitioners. However, other forms of depression are far more severe and require advanced care by certified mental health providers. However, identifying cases of depression that require advanced care may be challenging to primary care providers and health care team members whose skill sets run broad rather than deep.
Objective: This study aimed to leverage a comprehensive range of patient-level diagnostic, behavioral, and demographic data, as well as past visit history data from a statewide health information exchange to build decision models capable of predicting the need of advanced care for depression across patients presenting at Eskenazi Health, the public safety net health system for Marion County, Indianapolis, Indiana.
Methods: Patient-level diagnostic, behavioral, demographic, and past visit history data extracted from structured datasets were merged with outcome variables extracted from unstructured free-text datasets and were used to train random forest decision models that predicted the need of advanced care for depression across (1) the overall patient population and (2) various subsets of patients at higher risk for depression-related adverse events; patients with a past diagnosis of depression; patients with a Charlson comorbidity index of ≥1; patients with a Charlson comorbidity index of ≥2; and all unique patients identified across the 3 above-mentioned high-risk groups.
Results: The overall patient population consisted of 84,317 adult (aged ≥18 years) patients. A total of 6992 (8.29%) of these patients were in need of advanced care for depression. Decision models for high-risk patient groups yielded area under the curve (AUC) scores between 86.31% and 94.43%. The decision model for the overall patient population yielded a comparatively lower AUC score of 78.87%. The variance of optimal sensitivity and specificity for all decision models, as identified using Youden J Index, is as follows: sensitivity=68.79% to 83.91% and specificity=76.03% to 92.18%.
Conclusions: This study demonstrates the ability to automate screening for patients in need of advanced care for depression across (1) an overall patient population or (2) various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors, and past visit history. Furthermore, these results show considerable potential to enable preventative care and can be easily integrated into existing clinical workflows to improve access to wraparound health care services.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective: This study aimed to leverage a comprehensive range of patient-level diagnostic, behavioral, and demographic data, as well as past visit history data from a statewide health information exchange to build decision models capable of predicting the need of advanced care for depression across patients presenting at Eskenazi Health, the public safety net health system for Marion County, Indianapolis, Indiana.
Methods: Patient-level diagnostic, behavioral, demographic, and past visit history data extracted from structured datasets were merged with outcome variables extracted from unstructured free-text datasets and were used to train random forest decision models that predicted the need of advanced care for depression across (1) the overall patient population and (2) various subsets of patients at higher risk for depression-related adverse events; patients with a past diagnosis of depression; patients with a Charlson comorbidity index of ≥1; patients with a Charlson comorbidity index of ≥2; and all unique patients identified across the 3 above-mentioned high-risk groups.
Results: The overall patient population consisted of 84,317 adult (aged ≥18 years) patients. A total of 6992 (8.29%) of these patients were in need of advanced care for depression. Decision models for high-risk patient groups yielded area under the curve (AUC) scores between 86.31% and 94.43%. The decision model for the overall patient population yielded a comparatively lower AUC score of 78.87%. The variance of optimal sensitivity and specificity for all decision models, as identified using Youden J Index, is as follows: sensitivity=68.79% to 83.91% and specificity=76.03% to 92.18%.
Conclusions: This study demonstrates the ability to automate screening for patients in need of advanced care for depression across (1) an overall patient population or (2) various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors, and past visit history. Furthermore, these results show considerable potential to enable preventative care and can be easily integrated into existing clinical workflows to improve access to wraparound health care services.
Yandrapalli, Bhanu Teja; Jones, Josette; Purkayastha, Saptarshi
Development and Implementation of a Dashboard for Diabetes Care Management in OpenMRS Journal Article
In: arXiv preprint arXiv:1910.11437, 2019.
@article{teja2019development,
title = {Development and Implementation of a Dashboard for Diabetes Care Management in OpenMRS},
author = {Bhanu Teja Yandrapalli and Josette Jones and Saptarshi Purkayastha},
year = {2019},
date = {2019-01-01},
journal = {arXiv preprint arXiv:1910.11437},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Obidi, Joyce; Villa, Carlos Hipolito; Storch, Emily; Whitaker, Barbee I; Chada, Kinnera; Williams, Alan; Fowler, Stephanie; Schilling, Lisa; Kahn, Michael G; Edlavitch, Stanley A; others,
Trends in RED Blood CELL Transfusions within the Biologics Effectiveness and Safety (BEST) Initiative Network, 2012-2018 Inproceedings
In: 2019 Annual Meeting, AABB 2019.
@inproceedings{obidi2019trends,
title = {Trends in RED Blood CELL Transfusions within the Biologics Effectiveness and Safety (BEST) Initiative Network, 2012-2018},
author = {Joyce Obidi and Carlos Hipolito Villa and Emily Storch and Barbee I Whitaker and Kinnera Chada and Alan Williams and Stephanie Fowler and Lisa Schilling and Michael G Kahn and Stanley A Edlavitch and others},
year = {2019},
date = {2019-01-01},
booktitle = {2019 Annual Meeting},
organization = {AABB},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Purkayastha, Saptarshi; Guntu, Mounika; Ravindran, Radhika; Surapaneni, Asha Kiranmayee
Learning Gains of Process Oriented Guided Inquiry Learning in an Online Course Setting Inproceedings
In: European Conference on e-Learning, pp. 495–XII, Academic Conferences International Limited 2019.
@inproceedings{purkayastha2019learning,
title = {Learning Gains of Process Oriented Guided Inquiry Learning in an Online Course Setting},
author = {Saptarshi Purkayastha and Mounika Guntu and Radhika Ravindran and Asha Kiranmayee Surapaneni},
year = {2019},
date = {2019-01-01},
booktitle = {European Conference on e-Learning},
pages = {495--XII},
organization = {Academic Conferences International Limited},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Nuthakki, Siddhartha; Neela, Sunil; Gichoya, Judy W; Purkayastha, Saptarshi
Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks Journal Article
In: arXiv preprint arXiv:1912.12397, 2019.
@article{nuthakki2019natural,
title = {Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks},
author = {Siddhartha Nuthakki and Sunil Neela and Judy W Gichoya and Saptarshi Purkayastha},
year = {2019},
date = {2019-01-01},
journal = {arXiv preprint arXiv:1912.12397},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purkayastha, Saptarshi; Surapaneni, Asha K; Maity, Pallavi; Rajapuri, Anushri S; Gichoya, Judy W
Critical Components of Formative Assessment in Process-Oriented Guided Inquiry Learning for Online Labs Journal Article
In: Electronic Journal of e-Learning, vol. 17, no. 2, 2019.
@article{Purkayastha2019c,
title = {Critical Components of Formative Assessment in Process-Oriented Guided Inquiry Learning for Online Labs},
author = {Saptarshi Purkayastha and Asha K Surapaneni and Pallavi Maity and Anushri S Rajapuri and Judy W Gichoya},
url = {https://files.eric.ed.gov/fulltext/EJ1220140.pdf},
doi = {DOI: 10.34190/JEL.17.2.02},
year = {2019},
date = {2019-00-00},
journal = {Electronic Journal of e-Learning},
volume = {17},
number = {2},
abstract = {In the traditional lab setting, it is reasonably straightforward to monitor student learning and provide ongoing feedback. Such formative assessments can help students identify their strengths and weaknesses, and assist faculty to recognize where students are struggling and address problems immediately. But in an online virtual lab setting, formative assessment has challenges that go beyond space-time synchrony of online classroom. As we see increased enrollment in online courses, learning science needs to address the problem of formative assessment in online laboratory sessions. We
developed a student team learning monitor (STLM module) in an electronic health record system to measure student engagement and actualize the social constructivist approach of Process Oriented Guided Inquiry Learning (POGIL). Using iterative Plan-Do-Study-Act cycles in two undergraduate courses over a period of two years, we identified critical components that are required for the online implementation of POGIL. We reviewed published research on POGIL classroom implementations for the last ten years and identified some common elements that affect learning gains. We present the
critical components that are necessary for implementing POGIL in online lab settings, and refer to this as Cyber POGIL. Incorporating these critical components are required to determine when, how and the circumstances under which Cyber POGIL may be successfully implemented. We recommend that more online tools be developed for POGIL classrooms, which evolve from just providing synchronous communication to improved task monitoring and assistive feedback.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
developed a student team learning monitor (STLM module) in an electronic health record system to measure student engagement and actualize the social constructivist approach of Process Oriented Guided Inquiry Learning (POGIL). Using iterative Plan-Do-Study-Act cycles in two undergraduate courses over a period of two years, we identified critical components that are required for the online implementation of POGIL. We reviewed published research on POGIL classroom implementations for the last ten years and identified some common elements that affect learning gains. We present the
critical components that are necessary for implementing POGIL in online lab settings, and refer to this as Cyber POGIL. Incorporating these critical components are required to determine when, how and the circumstances under which Cyber POGIL may be successfully implemented. We recommend that more online tools be developed for POGIL classrooms, which evolve from just providing synchronous communication to improved task monitoring and assistive feedback.
Sinha, Priyanshu; Gichoya, Judy W; Purkayastha, Saptarshi
Full training versus fine tuning for radiology images concept detection task for the Image Conference
2019, ISSN: 1613-0073.
@conference{Sinha2019,
title = {Full training versus fine tuning for radiology images concept detection task for the Image},
author = {Priyanshu Sinha and Judy W Gichoya and Saptarshi Purkayastha },
issn = {1613-0073},
year = {2019},
date = {2019-00-00},
journal = {CEUR Workshop Proceedings,(CEUR-WS. org)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
2018
Asha Kiranmayee Surapaneni Saptarshi Purkayastha, Pallavi Maity
Implementing Guided Inquiry Learning and Measuring Engagement Using an Electronic Health Record System in an Online Setting Conference
European Conference on e-Learning, Academic Conferences International Limited, 2018.
@conference{Purkayastha2018b,
title = {Implementing Guided Inquiry Learning and Measuring Engagement Using an Electronic Health Record System in an Online Setting},
author = {Saptarshi Purkayastha, Asha Kiranmayee Surapaneni, Pallavi Maity},
year = {2018},
date = {2018-11-01},
booktitle = {European Conference on e-Learning},
pages = {481-488},
publisher = {Academic Conferences International Limited},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Parvati Ravindranathan Menon Naliyatthaliyazchayil Saptarshi Purkayastha, Asha Kiranmayee Surapaneni
Improving "Desktop medicine" efficiency using Guided Inquiry Learning in an Electronic Health Records System Conference
Communications in Computer and Information Science, vol 852, Springer, Cham, 2018.
@conference{Purkayastha2018,
title = {Improving "Desktop medicine" efficiency using Guided Inquiry Learning in an Electronic Health Records System},
author = {Saptarshi Purkayastha, Parvati Ravindranathan Menon Naliyatthaliyazchayil, Asha Kiranmayee Surapaneni, Ashwini Kowkutla, Pallavi Maity},
editor = {Stephanidis C. HCI International 2018 \textendash Posters' Extended Abstracts. HCI 2018},
year = {2018},
date = {2018-07-18},
booktitle = {Communications in Computer and Information Science, vol 852},
journal = {Communications in Computer and Information Science, vol 852.},
publisher = {Springer, Cham},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Gichoya, Judy W; Kohli, Marc; Ivange, Larry; Schmidt, Teri S; Purkayastha, Saptarshi
A Platform for Innovation and Standards Evaluation: a Case Study from the OpenMRS Open-Source Radiology Information System Journal Article
In: Journal of digital imaging, pp. 1–10, 2018.
@article{gichoya2018platform,
title = {A Platform for Innovation and Standards Evaluation: a Case Study from the OpenMRS Open-Source Radiology Information System},
author = {Judy W Gichoya and Marc Kohli and Larry Ivange and Teri S Schmidt and Saptarshi Purkayastha},
year = {2018},
date = {2018-01-01},
journal = {Journal of digital imaging},
pages = {1--10},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gichoya, Judy W; Nuthakki, Siddhartha; Maity, Pallavi G; Purkayastha, Saptarshi
Phronesis of AI in radiology: Superhuman meets natural stupidity Journal Article
In: arXiv preprint arXiv:1803.11244, 2018.
@article{gichoya2018phronesis,
title = {Phronesis of AI in radiology: Superhuman meets natural stupidity},
author = {Judy W Gichoya and Siddhartha Nuthakki and Pallavi G Maity and Saptarshi Purkayastha},
year = {2018},
date = {2018-01-01},
journal = {arXiv preprint arXiv:1803.11244},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2017
Holden, Richard J; Kulanthaivel, Anand; Purkayastha, Saptarshi; Goggins, Kathryn M; Kripalani, Sunil
Know thy eHealth user: Development of biopsychosocial personas from a study of older adults with heart failure Journal Article
In: International Journal of Medical Informatics, vol. 108, pp. 158-167, 2017, ISSN: 1386-5056.
@article{holden2017know,
title = {Know thy eHealth user: Development of biopsychosocial personas from a study of older adults with heart failure},
author = { Richard J Holden and Anand Kulanthaivel and Saptarshi Purkayastha and Kathryn M Goggins and Sunil Kripalani},
doi = {10.1016/j.ijmedinf.2017.10.006},
issn = {1386-5056},
year = {2017},
date = {2017-12-01},
journal = {International Journal of Medical Informatics},
volume = {108},
pages = {158-167},
publisher = {Elsevier},
abstract = {Background
Personas are a canonical user-centered design method increasingly used in health informatics research. Personas\textemdashempirically-derived user archetypes\textemdashcan be used by eHealth designers to gain a robust understanding of their target end users such as patients.
Objective
To develop biopsychosocial personas of older patients with heart failure using quantitative analysis of survey data.
Method
Data were collected using standardized surveys and medical record abstraction from 32 older adults with heart failure recently hospitalized for acute heart failure exacerbation. Hierarchical cluster analysis was performed on a final dataset of n = 30. Nonparametric analyses were used to identify differences between clusters on 30 clustering variables and seven outcome variables.
Results
Six clusters were produced, ranging in size from two to eight patients per cluster. Clusters differed significantly on these biopsychosocial domains and subdomains: demographics (age, sex); medical status (comorbid diabetes); functional status (exhaustion, household work ability, hygiene care ability, physical ability); psychological status (depression, health literacy, numeracy); technology (Internet availability); healthcare system (visit by home healthcare, trust in providers); social context (informal caregiver support, cohabitation, marital status); and economic context (employment status). Tabular and narrative persona descriptions provide an easy reference guide for informatics designers.
Discussion
Personas development using approaches such as clustering of structured survey data is an important tool for health informatics professionals. We describe insights from our study of patients with heart failure, then recommend a generic ten-step personas development process. Methods strengths and limitations of the study and of personas development generally are discussed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Personas are a canonical user-centered design method increasingly used in health informatics research. Personas—empirically-derived user archetypes—can be used by eHealth designers to gain a robust understanding of their target end users such as patients.
Objective
To develop biopsychosocial personas of older patients with heart failure using quantitative analysis of survey data.
Method
Data were collected using standardized surveys and medical record abstraction from 32 older adults with heart failure recently hospitalized for acute heart failure exacerbation. Hierarchical cluster analysis was performed on a final dataset of n = 30. Nonparametric analyses were used to identify differences between clusters on 30 clustering variables and seven outcome variables.
Results
Six clusters were produced, ranging in size from two to eight patients per cluster. Clusters differed significantly on these biopsychosocial domains and subdomains: demographics (age, sex); medical status (comorbid diabetes); functional status (exhaustion, household work ability, hygiene care ability, physical ability); psychological status (depression, health literacy, numeracy); technology (Internet availability); healthcare system (visit by home healthcare, trust in providers); social context (informal caregiver support, cohabitation, marital status); and economic context (employment status). Tabular and narrative persona descriptions provide an easy reference guide for informatics designers.
Discussion
Personas development using approaches such as clustering of structured survey data is an important tool for health informatics professionals. We describe insights from our study of patients with heart failure, then recommend a generic ten-step personas development process. Methods strengths and limitations of the study and of personas development generally are discussed.
Kasiiti, N; Wawira, J; Purkayastha, S; Were, MC
Comparative Performance Analysis of Different Fingerprint Biometric Scanners for Patient Matching. Conference
MedInfo 2017, vol. 245, 2017.
@conference{kasiiti2017comparative,
title = {Comparative Performance Analysis of Different Fingerprint Biometric Scanners for Patient Matching.},
author = {N Kasiiti and J Wawira and S Purkayastha and MC Were},
year = {2017},
date = {2017-11-01},
booktitle = {MedInfo 2017},
journal = {Studies in health technology and informatics},
volume = {245},
pages = {1053--1057},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Gichoya, Judy Wawira; Alarifi, Mohammad; Bhaduri, Ria; Tahir, Bilal; Purkayastha, Saptarshi
Using cognitive fit theory to evaluate patient understanding of medical images Inproceedings
In: Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE, pp. 2430-2433, IEEE 2017.
@inproceedings{gichoya2017using,
title = {Using cognitive fit theory to evaluate patient understanding of medical images},
author = { Judy Wawira Gichoya and Mohammad Alarifi and Ria Bhaduri and Bilal Tahir and Saptarshi Purkayastha},
doi = {10.1109/EMBC.2017.8037347},
year = {2017},
date = {2017-07-11},
booktitle = {Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE},
pages = {2430-2433},
organization = {IEEE},
abstract = {Patients are increasingly presented with their health data through patient portals in an attempt to engage patients in their own care. Due to the large amounts of data generated during a patient visit, the medical information when shared with patients can be overwhelming and cause anxiety due to lack of understanding. Health care organizations are attempting to improve transparency by providing patients with access to visit information. In this paper, we present our findings from a research study to evaluate patient understanding of medical images. We used cognitive fit theory to evaluate existing tools and images that are shared with patients and analyzed the relevance of such sharing. We discover that medical images need a lot of customization before they can be shared with patients. We suggest that new tools for medical imaging should be developed to fit the cognitive abilities of patients.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}