用多模型协作提升肾功能衰退预测的准确与可解释性
Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework
- 通过视觉知识迁移与溯因推理增强开源大模型性能
- 预测效果媲美专有模型，且生成临床可理解的推理过程
- 适合需要透明决策支持的慢性肾病诊疗场景
准确且可解释的估算肾小球滤过率（eGFR）预测对慢性肾病（CKD）管理及临床决策至关重要。近期大型多模态模型（LMMs）因其处理图像与文本信息的能力，在临床预测任务中展现出强大潜力。然而，部署成本高、数据隐私风险及模型可靠性问题限制了其应用。本研究提出一种协作框架，提升开源LMM在eGFR预测中的表现，并生成具有临床意义的解释。该框架融合视觉知识迁移、溯因推理与短期记忆机制，显著增强预测准确性与可解释性。实验表明，所提方法在预测性能和可解释性上均达到与专有模型相当的水平，且能提供每个预测背后的合理临床推理路径。本方法为构建兼具预测精度与临床可信度的医疗AI系统提供了新思路。
原文摘要 · Abstract (English)
Accurate and interpretable prediction of estimated glomerular filtration rate (eGFR) is essential for managing chronic kidney disease (CKD) and supporting clinical decisions. Recent advances in Large Multimodal Models (LMMs) have shown strong potential in clinical prediction tasks due to their ability to process visual and textual information. However, challenges related to deployment cost, data privacy, and model reliability hinder their adoption. In this study, we propose a collaborative framework that enhances the performance of open-source LMMs for eGFR forecasting while generating clinically meaningful explanations. The framework incorporates visual knowledge transfer, abductive reasoning, and a short-term memory mechanism to enhance prediction accuracy and interpretability. Experimental results show that the proposed framework achieves predictive performance and interpretability comparable to proprietary models. It also provides plausible clinical reasoning processes behind each prediction. Our method sheds new light on building AI systems for healthcare that combine predictive accuracy with clinically grounded interpretability.
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