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

Journal: Journal of medical Internet research
Published Date:

Abstract

BACKGROUND: Investigations into person-specific predictors of stress have typically taken either a population-level nomothetic approach or an individualized ideographic approach. Nomothetic approaches can quickly identify predictors but can be hindered by the heterogeneity of these predictors across individuals and time. Ideographic approaches may result in more predictive models at the individual level but require a longer period of data collection to identify robust predictors.

Authors

  • Alan Rozet
    Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center, New York, NY, United States.
  • Ian M Kronish
    Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA.
  • Joseph E Schwartz
    Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY, USA.
  • Karina W Davidson
    Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY, USA.