Who Is in Your Data? Data and Machine Learning Limitations in Health Research
May 9, 2019
Elaine Nsoesie, PhD
Assistant Professor of Global Health
Data Science Faculty Fellow
About the Webinar:
Data from a variety of sources, including social media and electronic health records, Nsoesie headshotcan provide insights into population health trends. These data can be used to track and measure health outcomes across geographic regions, time and complex social networks. However, these data also has the potential to exacerbate health inequalities if not properly used in public health research and practice. This talk will cover data and algorithmic bias, and potential unintended impacts on health services and equity.
About Elaine Nsoesie:
Dr. Elaine Nsoesie is an Assistant Professor of Global Health at Boston University (BU). She is also a BU Data Science Faculty Fellow, as part of the BU Data Science Initiative at the Hariri Institute for Computing. Dr. Nsoesie aims to use digital data and machine learning to improve population health. She applies data science methodologies to global health problems, particularly in the realm of surveillance of chronic and infectious diseases. She completed her PhD in Computational Epidemiology from the Genetics, Bioinformatics and Computational Biology program at Virginia Tech. She also has an MS in Statistics and a BS in Mathematics. She has written for NPR, The Conversation, Quartz and Public Health Post. Dr. Nsoesie was born and raised in Cameroon. More about Elaine Nsoesie.