DiaLLM: EHR-Enhanced Clinical Conversational System for Clinical Test Recommendation and Diagnosis Prediction

ACL ARR 2025 February Submission5593 Authors

Abstract:

Recent advances in Large Language Models (LLMs) have led to remarkable progresses in medical consultation.
However, existing medical LLMs overlook the essential role of Electronic Health Records (EHR) and focus primarily on diagnosis recommendation, limiting their clinical applicability. We propose DiaLLM, the first medical LLM that integrates heterogeneous EHR data into clinically grounded dialogues, enabling clinical test recommendation, result interpretation, and diagnosis prediction to better align with real-world medical practice.
To construct clinically grounded dialogues from EHR, we design a Clinical Test Reference (CTR) strategy that maps each clinical code to its corresponding description and classifies test results as "normal" or "abnormal".
Additionally, DiaLLM employs a reinforcement learning framework for evidence acquisition and automated diagnosis.
To handle the large action space, we introduce a reject sampling strategy to reduce redundancy and improve exploration efficiency. Furthermore, a confirmation reward and a class-sensitive diagnosis reward are designed to guide accurate diagnosis prediction.
Extensive experimental results demonstrate that DiaLLM outperforms baselines in clinical test recommendation and diagnosis prediction.
Our anonymous code is here\footnote{\url{https://anonymous.4open.science/r/DiaLLM-E3B3}}.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: Healthcare, dialogue system, diagnosis prediction

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 5593