Machine learning approaches for fine-grained symptom estimation in schizophrenia: A comprehensive review

Area of Science:

• Neuroscience
• Computer Science
• Psychiatry

Background:

• Schizophrenia diagnosis relies on symptom assessment, which can be time-consuming and subjective.
• Accurate, personalized assessments are crucial for effective schizophrenia treatment.
• Automated methods are needed to complement clinical judgment and improve diagnostic consistency.

Purpose of the Study:

• To review machine learning (ML) methodologies for diagnosing and assessing schizophrenia.
• To focus on ML methods for fine-grained symptom estimation, acknowledging the condition's complexity.
• To explore ML applications across multiple data modalities for schizophrenia assessment.

Main Methods:

• Review of existing literature on ML for schizophrenia diagnosis and assessment.
• Analysis of studies utilizing medical imaging, electroencephalograms (EEGs), and audio-visual data.
• Categorization of ML approaches based on their application to fine-grained symptom estimation.

Main Results:

• Machine learning shows significant potential for consistent and accurate schizophrenia symptom estimation.
• Multi-modal data (imaging, EEG, audio-visual) are being increasingly utilized in ML-based schizophrenia assessments.
• Current research is shifting towards more granular symptom analysis rather than binary classification.

Conclusions:

• Machine learning offers a powerful tool to enhance the accuracy and consistency of schizophrenia diagnosis and symptom assessment.
• Further research is needed to address identified gaps and opportunities in ML methodologies and datasets for schizophrenia.
• The integration of ML can support healthcare professionals in providing more personalized patient care.