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Machine Learning (ML) analyzes biosignals to personalize mental healthcare. This review explores ML

Keywords:
affective computingartificial intelligencebiosignalsbrain–computer interfacesmachine learningmental healthneurologyprecision medicine

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Area of Science:

  • Computational psychiatry
  • Digital mental health
  • Applied machine learning

Background:

  • Machine Learning (ML) provides advanced tools for mental health diagnostics and interventions.
  • Biosignal analysis using ML can differentiate typical and atypical psychological functioning.
  • Personalized mental healthcare is achievable through ML-driven insights.

Purpose of the Study:

  • To provide a comprehensive overview of ML algorithms for inferring psychological states from biosignals.
  • To illustrate ML applications in mental health research and clinical practice.
  • To discuss challenges, advantages, and drawbacks of AI in mental healthcare.

Main Methods:

  • Narrative review of existing literature on ML and biosignals in mental health.
  • Description of biosignals like EEG and ECG for inferring cognitive and emotional correlates.
  • Exploration of applications in Diagnostic Precision Medicine, Affective Computing, and brain-computer interfaces.

Main Results:

  • ML algorithms can effectively infer psychological states from various biosignals.
  • Applications span precision diagnostics, affective computing, and brain-computer interfaces.
  • Identified key challenges and research questions for ML in mental health.

Conclusions:

  • Integrating ML and mental health research drives personalized and effective medicine.
  • Cross-disciplinary collaboration between clinicians and data scientists is crucial.
  • ML holds significant potential for advancing mental healthcare, despite challenges.