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Home
Pkfar: Psychiatry Knowledge-fused Augmented Reasoning With Large Language Models.

Home
Pkfar: Psychiatry Knowledge-fused Augmented Reasoning With Large Language Models.

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

Rongzheng Wang¹, Cheng Yu², Qian Dong¹

¹Institute of Intelligent Computing, University of Electronic Science and Technology of China, No.2006 Xiyuan Avenue, Chengdu, 611731 China.

Health Information Science and Systems

|April 15, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces PKFAR, a novel approach using a psychiatric knowledge graph and hierarchical reasoning to improve Large Language Model (LLM) diagnostic accuracy in psychiatry. PKFAR enhances LLM performance efficiently, addressing key limitations in current clinical support systems.

Keywords:

Knowledge graph Large language model Psychiatric diagnosis Retrieval augmented generation

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Area of Science:

Artificial Intelligence in Medicine
Clinical Decision Support Systems
Computational Psychiatry

Background:

Psychiatric diagnosis is challenged by subjective reporting and complex criteria.
Current Large Language Models (LLMs) face privacy or computational hurdles for clinical use.
Existing LLM approaches inadequately address psychiatric complexities.

Purpose of the Study:

To develop a specialized, knowledge-enhanced LLM approach for accurate psychiatric diagnostics.
To overcome limitations of commercial and large-scale open-source LLMs in psychiatric applications.
To improve clinical decision support in psychiatry through advanced AI.

Main Methods:

Proposed PKFAR (psychiatry knowledge-fused augmented reasoning) system.

Developed PsychKG: a semantically-augmented psychiatric knowledge graph.

Implemented a three-stage hierarchical reasoning framework: symptom comprehension, disorder retrieval, and diagnosis reasoning.

Evaluated on Mentat, MedQA_psychiatry, and MIMIC benchmarks using Qwen3-8B.

Main Results:

PKFAR achieved 12.4%, 7.5%, and 10.0% accuracy improvements over standard baselines on Mentat, MedQA_psychiatry, and MIMIC, respectively.
Demonstrated superior performance compared to one-shot CoT of GPT-o3 and DeepSeek-V3.
Approached the accuracy of the large-scale DeepSeek-R1 model.

Conclusions:

PKFAR offers an effective balance between computational efficiency and diagnostic precision.
The knowledge-fused approach and structured reasoning address critical LLM limitations in psychiatric diagnostics.
PKFAR presents a practical solution for enhancing psychiatric diagnostic accuracy using AI.