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Modern views of machine learning for precision psychiatry


Review

. 2022 Nov 11;3(11):100602.


doi: 10.1016/j.patter.2022.100602.

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Review

Zhe Sage Chen et al.


Patterns (N Y).


.

Abstract

In light of the National Institute of Mental Health (NIMH)’s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.


Keywords:

AI; ML; XAI; artificial intelligence; causality; computational psychiatry; deep learning; digital phenotyping; digital psychiatry; explainable AI; machine learning; molecular biomarker; multi-modal data fusion; neurobiomarker; neuroimaging; neuromodulation; precision psychiatry; teletherapy.

Conflict of interest statement

The authors declare no competing financial interests.



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