Published on in Vol 21, No 4 (2019): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12910, first published .
Using Machine Learning to Derive Just-In-Time and Personalized Predictors of Stress: Observational Study Bridging the Gap Between Nomothetic and Ideographic Approaches

Using Machine Learning to Derive Just-In-Time and Personalized Predictors of Stress: Observational Study Bridging the Gap Between Nomothetic and Ideographic Approaches

Using Machine Learning to Derive Just-In-Time and Personalized Predictors of Stress: Observational Study Bridging the Gap Between Nomothetic and Ideographic Approaches

Alan Rozet 1, BA;  Ian M Kronish 1, MPH, MD;  Joseph E Schwartz 1, MS, PhD;  Karina W Davidson 2, MASc, PhD

1 Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center , New York, NY, US

2 Feinstein Institute for Medical Research, Northwell Health , New York, NY, US

Corresponding Author:

  • Alan Rozet, BA
  • Center for Behavioral Cardiovascular Health
  • Columbia University Irving Medical Center
  • Presbyterian Hospital Building, 9th Floor
  • 622 W 168th Street
  • New York, NY
  • US
  • Phone: 1 212-342-4493
  • Email: ar3793@cumc.columbia.edu