Systematic Review of Digital Phenotyping and Machine Learning in Psychosis Spectrum Illnesses.

Journal: Harvard review of psychiatry
PMID: 32796192

Abstract

BACKGROUND: Digital phenotyping is the use of data from smartphones and wearables collected in situ for capturing a digital expression of human behaviors. Digital phenotyping techniques can be used to analyze both passively (e.g., sensor) and actively (e.g., survey) collected data. Machine learning offers a possible predictive bridge between digital phenotyping and future clinical state. This review examines passive digital phenotyping across the schizophrenia spectrum and bipolar disorders, with a focus on machine-learning studies.

Authors

  • James Benoit
    From Harvard Medical School; Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA (Drs. Benoit, Keshavan, and Torous); Cardiac Psychiatry Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA (Dr. Onyeaka).
  • Henry Onyeaka
  • Matcheri Keshavan
    Department of Psychiatry, Harvard University, Cambridge, MA, United States.
  • John Torous
    Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.

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