Applications of artificial intelligence-machine learning for detection of stress: a critical overview.
Journal
Molecular psychiatry
ISSN: 1476-5578
Titre abrégé: Mol Psychiatry
Pays: England
ID NLM: 9607835
Informations de publication
Date de publication:
05 Apr 2023
05 Apr 2023
Historique:
received:
12
10
2021
accepted:
20
03
2023
revised:
17
03
2023
entrez:
5
4
2023
pubmed:
6
4
2023
medline:
6
4
2023
Statut:
aheadofprint
Résumé
Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, early detection and management of chronic stress is crucial for the prevention of several diseases. Artificial intelligence (AI) and Machine Learning (ML) have promoted a paradigm shift in several areas of biomedicine including diagnosis, monitoring, and prognosis of disease. Here, our review aims to present some of the AI and ML applications for solving biomedical issues related to psychological stress. We provide several lines of evidence from previous studies highlighting that AI and ML have been able to predict stress and detect the brain normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD)) with accuracy around 90%. Of note, AI/ML-driven technology applied to identify ubiquitously present stress exposure may not reach its full potential, unless future analytics focus on detecting prolonged distress through such technology instead of merely assessing stress exposure. Moving forward, we propose that a new subcategory of AI methods called Swarm Intelligence (SI) can be used towards detecting stress and PTSD. SI involves ensemble learning techniques to efficiently solve a complex problem, such as stress detection, and it offers particular strength in clinical settings, such as privacy preservation. We posit that AI and ML approaches will be beneficial for the medical and patient community when applied to predict and assess stress levels. Last, we encourage additional research to bring AI and ML into the standard clinical practice for diagnostics in the not-too-distant future.
Identifiants
pubmed: 37020048
doi: 10.1038/s41380-023-02047-6
pii: 10.1038/s41380-023-02047-6
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Nature Limited.
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