MERA通过记忆与排序提升大模型临床诊断能力
Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction
- 用分层对比学习缩小疾病候选空间，优化诊断排序
- 在MIMIC-III和IV数据集上达到当前最优性能
- 适合医疗AI研究者与临床辅助系统开发者
临床诊断预测模型旨在根据患者病史早期发现潜在疾病，促进及时干预并改善预后。然而，患者数据稀缺与庞大的疾病候选空间常导致模型性能受限。现有研究对利用大语言模型（LLM）捕捉临床决策过程的探索有限。本文提出MERA，一种将自然语言知识与医学实践相融合的临床诊断预测模型。通过在疾病候选排序列表上应用分层对比学习，缓解大规模决策空间问题；并通过微调实现概念记忆，连接自然语言临床知识与医学编码。在MIMIC-III和IV数据集上的实验表明，MERA达到了当前最优的诊断预测性能，并显著提升生成式大模型的诊断能力。
原文摘要 · Abstract (English)
Clinical diagnosis prediction models, when provided with a patient's medical history, aim to detect potential diseases early, facilitating timely intervention and improving prognostic outcomes. However, the inherent scarcity of patient data and large disease candidate space often pose challenges in developing satisfactory models for this intricate task. The exploration of leveraging Large Language Models (LLMs) for encapsulating clinical decision processes has been limited. We introduce MERA, a clinical diagnosis prediction model that bridges pertaining natural language knowledge with medical practice. We apply hierarchical contrastive learning on a disease candidate ranking list to alleviate the large decision space issue. With concept memorization through fine-tuning, we bridge the natural language clinical knowledge with medical codes. Experimental results on MIMIC-III and IV datasets show that MERA achieves the state-of-the-art diagnosis prediction performance and dramatically elevates the diagnosis prediction capabilities of generative LMs.
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