Machine learning-based behavioral diagnostic tools for depression: Advances, challenges, and future directions

Thalia Richter, Barak Fishbain, Gal Richter-Levin, Hadas Okon-Singer

Research output: Contribution to journalReview articlepeer-review

Abstract

The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper’s overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.

Original languageEnglish
Article number957
JournalJournal of Personalized Medicine
Volume11
Issue number10
DOIs
StatePublished - Oct 2021

Bibliographical note

Funding Information:
Funding: This research was funded by the Israel Ministry of Science and Technology award no. 3-14356 to G.R.-L., and by the Ministry of Science & Technology, Israel and the Israeli-French High Council for Scientific & Technological Cooperation, grant awarded to H.O.-S.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Behavioral diagnosis
  • Depression
  • Diagnostic tools
  • Machine-learning

ASJC Scopus subject areas

  • Medicine (miscellaneous)

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