Publication date: Oct 09, 2019
Part of PLOS Medicine’s 15th Anniversary celebration, Academic Editor Steven Shapiro discusses the contributions of the Machine Learning Special Issue to the validation of precision medicine and its potential use in clinical research and health care.
As computational power increases exponentially, the capacity to (more affordably) handle, store, and analyze -big data” using machine learning (ML) will revolutionize science and medicine.
In the special issue, PLOS Medicine editors along with guest editors Suchi Saria, Atul Butte and Aziz Sheikh got ahead of it discussing the opportunities, challenges and laid the groundwork for scientifically robust use of ML.
Criteria used for manuscripts published in this issue were that models derived from ML must be fit for the stated clinical purpose, and researchers must report on their efforts to validate the models with external datasets.
The original articles displayed a broad array of uses that ML will have in medicine including improved diagnosis, predicting disease course (including complications and mortality), and informing population and public health.
Hence, there is a mix of population health that attempts to reduce variation, and precision medicine that aims to add back variation at an individual level to determine one’s disease susceptibility, trajectory, and best treatment for each patient.
Dr. Shapiro received his medical degree in 1983 from the University of Chicago and completed an internal medicine residency, chief residency and fellowship in respiratory and critical care at the Washington University School of Medicine.
In 2001 he was named the Parker B. Francis Professor of Medicine at Harvard Medical School and appointed the Chief of the Division of Pulmonary and Critical Care at Brigham and Women’s Hospital.
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