Projects

Guyana Outreach Project
Project DataREACH began in the summer of 2015, with a trip to Guyana, presenting at hospitals, universities, medical schools, and government entities (Ministry of Health and National Cancer Institute) about the potential for the application of data science in medicine, including machine learning/predictive analytics for risk assessment, as well as exploring techniques to utilize surveillance data. Project DataREACH was commended by Guyana's Ministry of Health and National Cancer Institute for this outreach effort, and fostered numerous connections from which to explore future projects.
Cameroon World Health Organization (WHO) Smart Surveillance Platform
In December 2016, Project DataREACH partnered with the World Health Organization (WHO) country office in Cameroon, to develop a data science platform for effectively analyzing surveillance data for infectious disease data in Cameroon. A two step smart surveillance platform was developed, the first step being the implementation of outbreak detection algorithms in health districts in order to enable alerts/flags for outbreaks in a timely manner. The second step of the smart surveillance platform involves a novel application of multivariate time series analysis to analyze the correlations between regions by examining cross correlations between their modeled time series, and using these correlations to extrapolate impact response conclusions, and understand disease spread and result in efficient resource allocation to alleviate the casualties resulting from these infectious diseases.
DataREACH EMR Software
In the spring of 2017, Project DataREACH developed custom software for data collection, to be used in health systems without electronic data collection capabilities. Data stored into the server connected to this platform will be designed before to feed into machine learning pipeline, developed in partnerships with doctors and other medical professionals. Advancements in medical data collection can also lead to cardiovascular risk assessment, through ASCVD algorithm. A demo of an ML model deployed from this platform to predict artery narrowing from an assortment of risk factors can be used at db.datareach.org. Through this software, Project DataREACH hopes to build the first from scratch machine learning pipelines for medicine in Cameroon.