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Citation

A Review of Challenges and Opportunities in Machine Learning for Health

Author:
Ghassemi, Marzyeh; Naumann, Tristan; Schulam, Peter; Beam, Andrew L.; Chen, Irene Y.; Ranganath, Rajesh
Publication:
AMIA Summits on Translational Science Proceedings
Year:
2020

Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example, diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented. This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare.