用病历文本训练模型，部署时只用结构化数据，提升诊断准确率。
Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment
- 训练时融合病历文本与结构化数据，部署仅依赖结构化信息。
- 在3466名儿童数据上，模型AUROC达0.705，优于基线0.656。
- 适合医疗场景中需轻量部署但又想利用非结构化数据的团队。
非结构化的电子健康记录（EHR）数据，如临床笔记，包含未直接体现在结构化字段中的临床上下文信息，可显著提升模型学习效果。然而，由于其非结构化特性，这些数据在模型部署阶段通常不可用或不实用。本文提出一种多模态学习框架，在训练阶段利用未结构化EHR数据，同时生成仅使用结构化数据进行部署的模型。基于3,466名评估为语言发育迟缓儿童的队列，我们使用BioClinicalBERT生成笔记嵌入，并编码人口统计学和医疗代码的结构化嵌入。通过对比学习和对比知识蒸馏损失，联合训练一个基于笔记的教师模型与仅依赖结构化数据的学生模型，最终获得高精度分类器（AUROC = 0.985）。所提模型达到AUROC 0.705，优于仅使用结构化数据的基线模型（0.656）。结果表明，在训练中引入未结构化数据可增强模型从结构化EHR中识别任务相关特征的能力，从而实现可部署的纯结构化数据表型模型。
原文摘要 · Abstract (English)
Unstructured Electronic Health Record (EHR) data, such as clinical notes, contain clinical contextual observations that are not directly reflected in structured data fields. This additional information can substantially improve model learning. However, due to their unstructured nature, these data are often unavailable or impractical to use when deploying a model. We introduce a multimodal learning framework that leverages unstructured EHR data during training while producing a model that can be deployed using only structured EHR data. Using a cohort of 3,466 children evaluated for late talking, we generated note embeddings with BioClinicalBERT and encoded structured embeddings from demographics and medical codes. A note-based teacher model and a structured-only student model were jointly trained using contrastive learning and contrastive knowledge distillation loss, producing a strong classifier (AUROC = 0.985). Our proposed model reached an AUROC of 0.705, outperforming the structured-only baseline of 0.656. These results demonstrate that incorporating unstructured data during training enhances the model's capacity to identify task-relevant information within structured EHR data, enabling a deployable structured-only phenotype model.
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