mlsanityData to 14 Oct 2026

Modern Views of Machine Learning for Precision Psychiatry

arXiv 2204.01607 ↗First posted 4 Apr 2022cs.LG cs.AI q-bio.NCcited by 5

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Abstract (arXiv)

In light of the 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 the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 28 of its 438 references that are in the map. First posted on arXiv 4 Apr 2022.

Builds on

28 of 438 references.

References · 28

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16 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 3.

5 papers.

  1. 2024A Comprehensive Evaluation of Large Language Models on Mental Illnessescited by 7
  2. 2023Large-scale Foundation Models and Generative AI for BigData Neurosciencecited by 0
  3. 2023The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Reviewcited by 0
  4. 2024Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a reviewcited by 0
  5. 2024Evaluating the Economic Implications of Using Machine Learning in Clinical Psychiatrycited by 0