PKFAR: psychiatry knowledge-fused augmented reasoning with large language models

Psychiatric diagnosis faces significant challenges due to subjective symptom reporting and complex diagnostic criteria. While Large Language Models (LLMs) offer potential clinical decision support, their implementation is hindered by privacy constraints on commercial models (e.g., GPT-o3, Gemini-2.5) and computational demands of massive-scale open-source alternatives (e.g., DeepSeek-R1). These constraints necessitate knowledge-enhanced approaches with smaller-scale LLMs as the primary research direction. However, existing methods fail to adequately address psychiatric complexities, necessitating a specialized solution for accurate diagnostic support.

We propose PKFAR (psychiatry knowledge-fused augmented reasoning), which incorporates two features: (1) PsychKG, a semantically-augmented psychiatric knowledge graph integrating psychiatric criteria with both node and relation descriptors, and (2) a three-stage hierarchical reasoning framework comprising symptom comprehension, disorder retrieval, and diagnosis reasoning. The system is evaluated on Mentat, MedQA_psychiatry, and MIMIC benchmarks using Qwen3-8B.

Compared to standard one-shot CoT reasoning baselines, PKFAR achieves 12.4, 7.5, and 10.0% accuracy improvements using Qwen3-8B in zero-shot settings on Mentat, MedQA_psychiatry, and MIMIC respectively. Notably, our approach outperforms one-shot CoT performance of both GPT-o3 and DeepSeek-V3, while approaching the accuracy of DeepSeek-R1.

Our knowledge-fused approach establishes an effective balance between computational efficiency and diagnostic precision in psychiatric applications. PKFAR's structured reasoning pathway and semantically-augmented knowledge method address critical limitations in current LLM-based clinical support systems, offering a practical solution for psychiatric diagnostics.