Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning.

Journal: Journal of affective disorders
PMID: 31787419

Abstract

BACKGROUND: Tailoring healthcare to patients' individual needs is a central goal of precision medicine. Combining smartphone-based interventions with machine learning approaches may help attaining this goal. The aim of our study was to explore the predictability of the success of smartphone-based psychotherapeutic micro-interventions in eliciting mood changes using machine learning.

Authors

  • Gunther Meinlschmidt
    Division of Clinical Psychology and Cognitive Behavioural Therapy, International Psychoanalytic University (IPU) Berlin, Berlin, Germany. meinlschmidt@uni-trier.de.
  • Marion Tegethoff
    Division of Clinical Psychology and Psychiatry, Department of Psychology, University of Basel, Basel, Switzerland; Institute of Psychology, RWTH Aachen, Aachen, Germany. Electronic address: marion.tegethoff@unibas.ch.
  • Angelo Belardi
    Institute of Psychology, RWTH Aachen, Aachen, Germany.
  • Esther Stalujanis
    Division of Clinical Psychology and Cognitive Behavioral Therapy, International Psychoanalytic University, Berlin, Germany; Division of Clinical Psychology and Psychiatry, Department of Psychology, University of Basel, Basel, Switzerland.
  • Minkyung Oh
    Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
  • Eun Kyung Jung
    Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
  • Hyun-Chul Kim
    Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, 02841, Republic of Korea.
  • Seung-Schik Yoo
    Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Jong-Hwan Lee
    Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea. Electronic address: jonghwan_lee@korea.ac.kr.

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