用大模型从不规则病历数据中自动生成临床有用特征。
FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records
- 用大模型理解病历结构，生成能处理时间不规律的特征提取代码。
- 在8个重症监护任务中7个表现最优，最高提升6个百分点。
- 保护隐私且适合医疗数据建模新手快速上手。
电子健康记录（EHR）特征工程因观测时间间隔不规则、测量频率可变及结构稀疏性而复杂。现有自动化方法或缺乏临床知识，或假设输入为规整采样数据，难以适用于真实EHR。我们提出FeatEHR-LLM框架，利用大语言模型（LLM）从不规则采样的EHR时序数据中生成具有临床意义的表格特征。为保护患者隐私，LLM仅访问数据模式和任务描述，不接触原始病历。通过工具增强的生成机制，LLM具备查询不规则时间数据的专用能力，生成显式处理非均匀观测与信息性稀疏的可执行特征提取代码。该框架支持单变量与多变量特征生成，采用迭代验证闭环流程。在四个ICU数据集上的八个临床预测任务中，平均AUROC在7项任务中领先，最高较强基线提升6个百分点。代码已开源于github.com/hojjatkarami/FeatEHR-LLM。
原文摘要 · Abstract (English)
Feature engineering for Electronic Health Records (EHR) is complicated by irregular observation intervals, variable measurement frequencies, and structural sparsity inherent to clinical time series. Existing automated methods either lack clinical domain awareness or assume clean, regularly sampled inputs, limiting their applicability to real-world EHR data. We present \textbf{FeatEHR-LLM}, a framework that leverages Large Language Models (LLMs) to generate clinically meaningful tabular features from irregularly sampled EHR time series. To limit patient privacy exposure, the LLM operates exclusively on dataset schemas and task descriptions rather than raw patient records. A tool-augmented generation mechanism equips the LLM with specialized routines for querying irregular temporal data, enabling it to produce executable feature-extraction code that explicitly handles uneven observation patterns and informative sparsity. FeatEHR-LLM supports both univariate and multivariate feature generation through an iterative, validation-in-the-loop pipeline. Evaluated on eight clinical prediction tasks across four ICU datasets, our framework achieves the highest mean AUROC on 7 out of 8 tasks, with improvements of up to 6 percentage points over strong baselines. Code is available at github.com/hojjatkarami/FeatEHR-LLM.
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