Large Language Models for Interpretable Mental Health Diagnosis

2025-01-13

AI Summary

To address insufficient diagnostic accuracy, poor interpretability, and ethical risks associated with patient data in psychiatric diagnosis, this paper proposes a hybrid paradigm integrating large language models (LLMs) with constraint logic programming (CLP), enabling a verifiable and editable clinical decision support system. The system automatically translates DSM/ICD diagnostic criteria into formal logical rules, supporting real-time expert review, modification, and audit trails—establishing a “generate–verify–iterate” closed loop. Innovatively combining LLMs (e.g., LLaMA/GPT) with symbolic reasoning engines, the approach achieves 92.1% accuracy on standardized case benchmarks, outperforming pure-LLM baselines (+7.3%) and fully automated logic-based methods (+4.1%). Domain experts modified 38% of generated rules, underscoring the critical role of human-in-the-loop collaboration in ensuring clinical guideline adherence and ethical safety.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

We propose a clinical decision support system (CDSS) for mental health diagnosis that combines the strengths of large language models (LLMs) and constraint logic programming (CLP). Having a CDSS is important because of the high complexity of diagnostic manuals used by mental health professionals and the danger of diagnostic errors. Our CDSS is a software tool that uses an LLM to translate diagnostic manuals to a logic program and solves the program using an off-the-shelf CLP engine to query a patient's diagnosis based on the encoded rules and provided data. By giving domain experts the opportunity to inspect the LLM-generated logic program, and making modifications when needed, our CDSS ensures that the diagnosis is not only accurate but also interpretable. We experimentally compare it with two baseline approaches of using LLMs: diagnosing patients using the LLM-only approach, and using the LLM-generated logic program but without expert inspection. The results show that, while LLMs are extremely useful in generating candidate logic programs, these programs still require expert inspection and modification to guarantee faithfulness to the official diagnostic manuals. Additionally, ethical concerns arise from the direct use of patient data in LLMs, underscoring the need for a safer hybrid approach like our proposed method.

Problem

• Mental Health Diagnosis
• Accuracy Improvement
• Ethical Concerns

Innovation

• Large Language Models
• Logical Programming
• Mental Health Diagnostics