Analyzing electronic health records can be a fast and accurate way to predict risk of death from COVID-19

Hossein Estiri, PhD, is lead author on a study that utilized artificial intelligence to leverage the data compiled in electronic health records to compute individual-level risk scores for death after a COVID-19 infection. Among those predictors at the top of the list for those between the ages of 45-65 were age, history of pneumonia, diabetes … Continue reading “Analyzing electronic health records can be a fast and accurate way to predict risk of death from COVID-19”

Novel approach to help predict and diagnose diseases tracks electronic health records over time using machine learning

Harvard Pop Center faculty member Hossein Estiri, PhD, is an author on a study published in Cell Patterns that is receiving attention in the media for its sequential approach to mining meaningful (as it relates to predicting and diagnosing diseases) patient information from complex electronic health records. “In this paper, we propose an algorithm for … Continue reading “Novel approach to help predict and diagnose diseases tracks electronic health records over time using machine learning”

Using a machine learning approach to shed light on relationship between SES and women’s height

Even though height is commonly correlated with socioeconomic status (SES), SES is not known as a reliable predictor of height. In this study, Harvard Pop Center Bell Fellow Adel Daoud, Research Associate Rockli Kim, and faculty member S (Subu) V Subramanian utilized machine learning algorithms to assess whether there were non-linear patterns in the data … Continue reading “Using a machine learning approach to shed light on relationship between SES and women’s height”