Publications

@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.},

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@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.},

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@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}

}

@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 = {},

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}

@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}

}

@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}

}

@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. },

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@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 = {},

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@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}

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@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}

}

@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.},

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@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 = {},

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tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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.},

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@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}