DongYuan: An LLM-Based Framework for Integrative Chinese and Western Medicine Spleen-Stomach Disorders Diagnosis

Abstract

The clinical burden of spleen-stomach disorders is substantial. While large language models (LLMs) offer new potential for medical applications, they face three major challenges in the context of integrative Chinese and Western medicine (ICWM): a lack of high-quality data, the absence of models capable of effectively integrating the reasoning logic of traditional Chinese medicine (TCM) syndrome differentiation with that of Western medical (WM) disease diagnosis, and the shortage of a standardized evaluation benchmark. To address these interrelated challenges, we propose DongYuan, an ICWM spleen-stomach diagnostic framework. Specifically, three ICWM datasets (SSDF-Syndrome, SSDF-Dialogue, and SSDF-PD) were curated to fill the gap in high-quality data for spleen-stomach disorders. We then developed SSDF-Core, a core diagnostic LLM that acquires robust ICWM reasoning capabilities through a two-stage training regimen of supervised fine-tuning. tuning (SFT) and direct preference optimization (DPO), and complemented it with SSDF-Navigator, a pluggable consultation navigation model designed to optimize clinical inquiry strategies. Additionally, we established SSDF-Bench, a comprehensive evaluation benchmark focused on ICWM diagnosis of spleen-stomach disorders. Experimental results demonstrate that SSDF-Core significantly outperforms 12 mainstream baselines on SSDF-Bench. DongYuan lays a solid methodological foundation and provides practical technical references for the future development of intelligent ICWM diagnostic systems.

Figures (6)

• Figure 1: Overview of DongYuan. The framework integrates three specialized datasets (SSDF‑Syndrome, SSDF‑Dialogue, SSDF‑PD), a core diagnostic LLM trained via a two‑stage pipeline (SFT → DPO), a pluggable consultation navigation model (SSDF‑Navigator), and a comprehensive evaluation benchmark (SSDF‑Bench). Application scenarios cover a diverse array of ICWM clinical and research-oriented tasks.
• Figure 2: An example from the SSDF-Syndrome dataset. Each sample is constructed from original medical records, presenting fully structured diagnostic information that explicitly integrates TCM syndrome differentiation, WM disease diagnosis, and other structured clinical information.
• Figure 3: An example from the SSDF‑Dialogue dataset. The dialogue follows a clinically coherent progression: starting from an initial symptom, the model sequentially inquires about severity fluctuation, temporal patterns, and associated impacts on daily activities.
• Figure 4: Overview of SSDF‑Navigator. The model uses a Transformer-based encoder-classifier architecture, with the symptom set from historical dialogue as input, to predict the next symptom. Training combines behavior cloning with offline reinforcement learning. During inference, it collaborates with SSDF‑Core via a candidate-based inquiry selection mechanism to guide multi‑turn proactive consultation.
• Figure 5: Performance Comparison Across Dimensions of the DDSSD Task.