The Myth of Generalisability in Clinical Research and Machine Learning in Health Care
The Myth of Generalisability in Clinical Research and Machine Learning in Health Care

Abstract: 

An emphasis on overly broad notions of generalisability as it pertains to applications of machine learning in health care can overlook situations in which machine learning might provide clinical utility. We believe that this narrow focus on generalisability should be replaced with wider considerations for the ultimate goal of building machine learning systems that are useful at the bedside.

Bio: 

As clinical research director and principal research scientist at the MIT Laboratory for Computational Physiology (LCP), and as a practicing intensive care unit (ICU) physician at the Beth Israel Deaconess Medical Center (BIDMC), Leo brings together clinicians and data scientists to support research using data routinely collected in the process of care. His group built and maintains the publicly-available Medical Information Mart for Intensive Care (MIMIC) database and the Philips-MIT eICU Collaborative Research Database, with more than 15,000 users from around the world. The MIMIC-III paper has been cited more than 2000 times since 2016. In addition, Leo is one of the course directors for HST.936 – global health informatics to improve quality of care, and HST.953 – collaborative data science in medicine, both at MIT. He is an editor of the textbook for each course, both released under an open access license. "Secondary Analysis of Electronic Health Records" has been downloaded more than 900,000 times, and has been translated to Mandarin, Spanish and Korean. Leo has spoken in more than 35 countries across 6 continents about the value of data and learning in health systems. His publications have been cited more than 2500 times during the pandemic.

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