Biomedical Data Analysis


With the recent development in the effort made by the US government to re-image the clinical research infrastructure to overcome some of the challenges we currently face, we make it a point of duty to be engaged in extensive research in Biomedical Analysis. Some of the challenges we currently face is related to the diverseness of the data structure from different data sources. This is why we believe that it is of great importance to research more on the development of Consolidated Clinical Document Architecture (C-CDA) to be able to unify the structure of our data for a more accurate analysis.

We believe that we could build on our researchers past experience as most of them have been actively involved in the development and optimization of data analysis applications such as DHIS2. With new indicators coming up every day, it becomes imperative for newer models to be built in order for us to achieve the purpose of conducting biomedical research, which is ultimately providing a value-based healthcare delivery through precision medicine.

Our aim is to come up with solutions that would form the bases for next generation Integrated Health Information Systems, which would have the capabilities to normalize data from various data sources, identify the important indicators needed for both qualitative and quantitative forecasting to support Clinical Business Intelligence.