Machine Learning Goes Mainstream: PLOS Medicine 15th Anniversary

Machine Learning Goes Mainstream: PLOS Medicine 15th Anniversary

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.

Concepts Keywords
Algorithm Academic disciplines
Altruistic Machine Learning
Big Data Behavior modification
Black Box Medicine
Chicago Artificial neural network
Clinical Trials Cognition
COPD Cybernetics
Critical Care Learning
Deep Learning Machine learning
Genetic Molecular pathways inflammation
Harvard Diseases
Healthcare Physiologic wearable devices
Holistic Trades final integrator
Infectious Diseases Healthcare crossroads
Inflammation
Integrator
Linear Regression
Lung Cancer
Machine Learning
Medicine
Mortality
Neural Networks
Pittsburgh
PLOS
President
Public Health
Pulmonary
Residency
Sheikh
Tsunami
UPMC
Vascular Disease
Vice President
Washington
Wearable Devices

Semantics

Type Source Name
gene UNIPROT LARGE1
disease MESH diagnosis
disease MESH complications
gene UNIPROT DYNAP
drug DRUGBANK Tropicamide
disease MESH development
gene UNIPROT LAT2
disease MESH inflammation
disease MESH chronic obstructive pulmonary disease
disease DOID chronic obstructive pulmonary disease
gene UNIPROT ARCN1
disease MESH infectious diseases
disease MESH vascular disease
disease DOID vascular disease
disease MESH lung cancer
disease DOID lung cancer

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