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

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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 (with complications), and cancer (breast and prostate). Learn more about the findings of this study…

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

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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 exploiting the temporal information in the EHRs that is distorted by layers of administrative and…

U.S. “Silver Tsunami” negatively impacting (and suffering from) climate change

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A study finds that aging Americans (a growing population) consume more energy. Learn more about the research findings of Harvard Pop Center faculty member Hossein Estiri and his colleague Emilio Zagheni in The New York Times, the New Scientist, and The Harvard Gazette.

Informatics and electronic health records can help integrate context into health care

Social determinants of health (complex contextual factors) are considered to have a profound impact on our health, yet are not part of our health care system. Harvard Pop Center faculty member Hossein Estiri, PhD, is an author of a Perspective published in JAMIA Open that makes a case for utilizing informatics and electronic health records as a way to “…give tomorrow’s clinicians the tools and the data they need to…