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A Cross Attention Approach to Diagnostic Explainability Using Clinical Practice Guidelines for Depression.

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    We developed ProcesS knowledgeinfused cross ATtention (PSAT) to explain artificial intelligence classifications of depression data using clinical knowledge. This enhances trust and accuracy for mental health screening tools.

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    Area of Science:

    • Artificial Intelligence
    • Clinical Informatics
    • Mental Health Research

    Background:

    • Explainability is a barrier to AI adoption in analyzing clinical dialogue.
    • Online mental health communities offer vast, untapped data for AI analysis.
    • Clinician expertise can guide AI in understanding patient interactions.

    Purpose of the Study:

    • To develop an AI method that leverages clinical knowledge for classifying and explaining depression-related data.
    • To enhance transformer models for generating understandable explanations for mental health practitioners (MHPs).
    • To improve the trustworthiness and efficiency of AI in mental health screening.

    Main Methods:

    • Proposed ProcesS knowledgeinfused cross ATtention (PSAT), a domain-general architecture incorporating clinical practice guidelines (CPG).
    • Transformed depression-focused CPGs (e.g., PHQ-9) into a machine-readable ontology using SNOMED-CT.
    • Enhanced GPT-3.5's ability to generate application-relevant explanations by integrating clinical knowledge.

    Main Results:

    • PSAT generated application-relevant explanations for depression-related data across four expert-curated datasets.
    • PSAT outperformed twelve baseline models in classification and explanation generation.
    • PSAT provided explanations where other models failed.

    Conclusions:

    • PSAT effectively integrates clinical knowledge into AI models for explainable depression analysis.
    • The PSAT architecture enhances trust and utility of AI tools for mental health screening.
    • This approach has significant potential for both online and offline mental health applications.