Peer-Reviewed Publication
Lancet Digit Health2020;2(9):e489-e492.September 1, 2020Journal Article
The myth of generalisability in clinical research and machine learning in health care.
Joseph Futoma1, Morgan Simons2, Trishan Panch3,4, Finale Doshi-Velez1, Leo Anthony Celi5,6,7
1School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA.
2Department of Medicine, NYU Langone Health, New York, NY, USA.
3Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
4Wellframe, Boston, MA, USA.
5Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
6Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
7Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
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.
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