Praveen Deorani

Senior Data Scientist, Singapore Ministry of Healthcare

Praveen Deorani is a Senior Data Scientist at the Singapore Ministry of Healthcare, Office of Healthcare Transformation. His responsibilities include using data to generate insights for programs and build models to identify relevant patterns in the Singapore public healthcare system. Deorani has extensive experience in the use of data-driven insights in healthcare delivery systems, the pharmaceutical industry, clinical trial design, and digital behavior change programs. He completed his Ph.D. in 2015 at the National University of Singapore.

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How a U.S. COVID-19 Data Registry Fuels Global Research

In addition to driving COVID-19 understanding within the United States, a national disease registry is informing research beyond U.S. borders. Clinicians with the Singapore Ministry of Healthcare Office for Healthcare Transformation (MOHT) have used Health Catalyst Touchstone® COVID-19 data to develop a machine learning tool that helps predict the likelihood of COVID-19 mortality. With this national data set that leverages deep aggregated EHR data, the MOHT accessed the research-grade data it needed to build a machine-learning algorithm that predicts risk of death from COVID-19. The registry-informed prediction model was accurate enough to stand up to comparisons in the published literature and promises to help inform vaccine research and, ultimately, allocation of vaccines within populations.

How a U.S. COVID-19 Data Registry Fuels Global Research

In addition to driving COVID-19 understanding within the United States, a national disease registry is informing research beyond U.S. borders. Clinicians with the Singapore Ministry of Healthcare Office for Healthcare Transformation (MOHT) have used Health Catalyst Touchstone® COVID-19 data to develop a machine learning tool that helps predict the likelihood of COVID-19 mortality.
With this national data set that leverages deep aggregated EHR data, the MOHT accessed the research-grade data it needed to build a machine-learning algorithm that predicts risk of death from COVID-19.
The registry-informed prediction model was accurate enough to stand up to comparisons in the published literature and promises to help inform vaccine research and, ultimately, allocation of vaccines within populations.

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