用大模型自动从多个电子病历库中提取研究队列，省时省力。
Leveraging Foundation Language Models (FLMs) for Automated Cohort Extraction from Large EHR Databases
- 将自然语言查询转为数据库可理解的语句，驱动跨库匹配
- 在两个大型病历库上实现92%的前三准确率，13个目标字段匹配12个
- 适用于不熟悉数据的科研人员，尤其适合多中心研究
队列提取是队列研究中的关键步骤，但耗时且复杂，尤其当研究者面对陌生数据集或需整合多个数据源时。本文提出一种部分自动化方法，用于从多个电子健康记录（EHR）数据库中提取研究队列。首先将筛选条件转化为自然语言查询，并将其转换为映射到数据库实体的语言；随后利用基础语言模型（FLMs）自动匹配不同数据库中感兴趣的字段；最后在所有数据库上运行生成的查询以提取队列。我们在两个大型公开可访问的EHR数据库——MIMIC-III和eICU上评估该方法。使用小型预训练通用语言模型，该方法在13个目标字段中成功匹配12个，达到92%的前三位准确率，且在数据库规模扩大时仍保持稳定性能。
原文摘要 · Abstract (English)
A crucial step in cohort studies is to extract the required cohort from one or more study datasets. This step is time-consuming, especially when a researcher is presented with a dataset that they have not previously worked with. When the cohort has to be extracted from multiple datasets, cohort extraction can be extremely laborious. In this study, we present an approach for partially automating cohort extraction from multiple electronic health record (EHR) databases. We formulate the guided multi-dataset cohort extraction problem in which selection criteria are first converted into queries, translating them from natural language text to language that maps to database entities. Then, using FLMs, columns of interest identified from the queries are automatically matched between the study databases. Finally, the generated queries are run across all databases to extract the study cohort. We propose and evaluate an algorithm for automating column matching on two large, popular and publicly-accessible EHR databases -- MIMIC-III and eICU. Our approach achieves a high top-three accuracy of $92\%$, correctly matching $12$ out of the $13$ columns of interest, when using a small, pre-trained general purpose language model. Furthermore, this accuracy is maintained even as the search space (i.e., size of the database) increases.
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