用对比学习融合病历数据与临床文本，提升预测准确性。
Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks
- 通过对比学习对齐结构化病历与非结构化病历摘要的表征
- 在30天再入院预测中比XGBoost提升4.1% AUROC
- 适合需要上下文理解的临床决策支持场景
传统机器学习模型，尤其是树模型，在电子健康记录（EHR）数据上对多种临床预测任务表现出良好性能。然而，这些模型在需要深层语境理解的任务（如30天再入院预测）中表现不佳，主要因结构化EHR数据语义信息有限。为此，我们提出一种深度多模态对比学习（CL）框架，将结构化EHR数据与非结构化出院摘要文本的潜在表征对齐：拉近配对样本的嵌入，推远未配对样本。微调该框架预训练的EHR编码器显著提升下游任务表现，例如在30天再入院预测中较XGBoost提升4.1% AUROC。结果表明，将临床笔记中的领域知识融入基于EHR的流程，可实现更准确、更具上下文感知的临床决策支持系统。
原文摘要 · Abstract (English)
Conventional machine learning models, particularly tree-based approaches, have demonstrated promising performance across various clinical prediction tasks using electronic health record (EHR) data. Despite their strengths, these models struggle with tasks that require deeper contextual understanding, such as predicting 30-day hospital readmission. This can be primarily due to the limited semantic information available in structured EHR data. To address this limitation, we propose a deep multimodal contrastive learning (CL) framework that aligns the latent representations of structured EHR data with unstructured discharge summary notes. It works by pulling together paired EHR and text embeddings while pushing apart unpaired ones. Fine-tuning the pretrained EHR encoder extracted from this framework significantly boosts downstream task performance, e.g., a 4.1% AUROC enhancement over XGBoost for 30-day readmission prediction. Such results demonstrate the effect of integrating domain knowledge from clinical notes into EHR-based pipelines, enabling more accurate and context-aware clinical decision support systems.
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