ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
Quick Take
ChatHealthAI is a multimodal framework that aligns EHR representations with LLMs to enhance clinical reasoning. Evaluated on EHRSHOT benchmark, it improves reasoning quality and interpretability while maintaining competitive predictive performance across three clinical tasks.
Key Points
- ChatHealthAI integrates EHR representations with LLMs for improved clinical decision support.
- The framework uses a task-aware resampler to align structured and semantic data.
- Results show enhanced reasoning quality while preserving predictive accuracy.
- Evaluated on three tasks from the EHRSHOT benchmark.
- Highlights the potential of combining EHR models with pretrained LLMs.
Article Excerpt
From source RSS / original summaryarXiv:2606. 02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs). In contrast, EHR foundation models can learn predictive patient representations, yet lack interpretable language-based reasoning.
To bridge this gap, we propose ChatHealthAI, a multimodal reasoning framework that aligns structured EHR representations from a pretrained EHR foundation model with the semantic space of a frozen LLM through a task-aware resampler. By integrating longitudinal patient representations with refined clinical event descriptions, ChatHealthAI enables clinically grounded natural-language reasoning while maintaining accurate patient prediction.
We evaluated ChatHealthAI on three clinical predictive tasks from the EHRSHOT benchmark. Results show that ChatHealthAI improves reasoning quality and interpretability while preserving competitive predictive performance. These findings highlight the potential of integrating EHR foundation models with pretrained LLMs for interpretable clinical prediction.
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