TY - CHAP
T1 - Clinical psychoinformatics
T2 - a novel approach to behavioral states and mental health care driven by machine learning
AU - Yamamoto, Tetsuya
AU - Yoshimoto, Junichiro
AU - Alcaraz-Silva, Jocelyne
AU - Murillo-Rodríguez, Eric
AU - Imperatori, Claudio
AU - Machado, Sérgio
AU - Budde, Henning
N1 - Publisher Copyright:
© 2022 Elsevier Inc. All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Machine learning (ML) is a branch of artificial intelligence technology that has received considerable attention in recent years. It is a computational strategy to discover the regularities inherent in multidimensional data sets, allowing us to build predictive models focused on individual states. Therefore, it may help increase the efficiency and sophistication of assessment and aid the selection of optimal intervention methods in clinical practice, including cognitive behavioral therapy. In this paper, we first review the framework of the ML approach, its differences from statistics, and its features. Subsequently, we summarize the main research topics where ML approaches have been applied in the field of mental health and introduce some examples of their applications that may contribute to research in clinical psychology and cognitive behavioral therapy. Finally, the limitations of the ML approach are discussed, as well as its potential for future applications.
AB - Machine learning (ML) is a branch of artificial intelligence technology that has received considerable attention in recent years. It is a computational strategy to discover the regularities inherent in multidimensional data sets, allowing us to build predictive models focused on individual states. Therefore, it may help increase the efficiency and sophistication of assessment and aid the selection of optimal intervention methods in clinical practice, including cognitive behavioral therapy. In this paper, we first review the framework of the ML approach, its differences from statistics, and its features. Subsequently, we summarize the main research topics where ML approaches have been applied in the field of mental health and introduce some examples of their applications that may contribute to research in clinical psychology and cognitive behavioral therapy. Finally, the limitations of the ML approach are discussed, as well as its potential for future applications.
KW - Artificial intelligence
KW - Clinical psychology
KW - Cognitive behavioral therapy
KW - e-health
KW - Machine learning
KW - Mental health
KW - Personalized medicine
KW - Precision medicine
KW - Psychiatry
KW - Psychoinfomatics
UR - http://www.scopus.com/inward/record.url?scp=85139308100&partnerID=8YFLogxK
U2 - 10.1016/B978-0-323-85235-7.00013-2
DO - 10.1016/B978-0-323-85235-7.00013-2
M3 - Capítulo
AN - SCOPUS:85139308100
SN - 9780323903349
SP - 255
EP - 279
BT - Methodological Approaches for Sleep and Vigilance Research
PB - Elsevier
ER -