Artificial intelligence, machine learning and mental health

Jaime Delgadillo, Dana Atzil-Slonim

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

Abstract

Computers are capable of learning how to solve complex problems. The emergence of machine learning (ML) represents a major advance for the field of mental health. ML algorithms can be trained to recognize subgroups of people with similar symptoms (diagnosis), to estimate the probability of recovery from these symptoms (prognosis), to make a judgment about the best treatment option for a patient (treatment selection), and even to provide feedback and guidance to therapists by learning from recordings of effective therapist-patient interactions (process feedback). This article offers an introduction to ML and the emerging field of precision mental health care.

Original languageEnglish
Title of host publicationEncyclopedia of Mental Health, Third Edition
Subtitle of host publicationVolume 1-3
PublisherElsevier
PagesV1-132-V1-142
Volume1
ISBN (Electronic)9780323914987
ISBN (Print)9780323914970
DOIs
StatePublished - 1 Jan 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Inc. All rights reserved.

Keywords

  • AI
  • Artificial intelligence
  • Clinical prediction models
  • Data mining
  • Data science
  • Deep learning
  • Machine learning
  • Natural language processing
  • Precision medicine
  • Precision mental health care
  • Statistical learning

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