Delgadillo, J. orcid.org/0000-0001-5349-230X and Atzil-Slonim, D. (2022) Artificial intelligence, machine learning and mental health. In: Reference Module in Neuroscience and Biobehavioral Psychology. Elsevier Reference Collection . Elsevier
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.
Metadata
Item Type: | Book Section |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier Inc. |
Keywords: | Artificial intelligence; AIMachine learning; Statistical learning; Deep learning; Data mining; Data science; Clinical prediction models; Natural language processing; Precision medicine; Precision mental health care |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Apr 2023 16:02 |
Last Modified: | 05 Apr 2023 16:02 |
Status: | Published |
Publisher: | Elsevier |
Series Name: | Elsevier Reference Collection |
Refereed: | Yes |
Identification Number: | 10.1016/b978-0-323-91497-0.00177-6 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197827 |