George Anjaly, Maisa Aashrith, Dreisbach Caitlin, Suba Sukardi
Goergen Institute for Data Science, University of Rochester.
School of Nursing, University of Rochester.
medRxiv. 2024 Dec 31:2024.12.26.24319658. doi: 10.1101/2024.12.26.24319658.
Acute coronary syndrome (ACS) is an acute heart disease that often evolves rapidly. In ACS patients presenting with no-ST-segment elevation (NSTE-ACS), the timing of symptom onset pre-hospital may inform the disease stage and prognosis. We pilot-tested two off-the-shelf natural language processing (NLP) pipelines, namely and (), to extract date and time (DateTime) information of patient-reported chest pain symptoms from electronic health records (EHR) clinical notes. We included three types of clinical notes (N=71): History and Physical (n=49), Emergency Department Screening (n=3), and Triage Notes (n=19). All notes were manually annotated for the true DateTime of symptom onset. returned matching DateTime outputs in 36 notes (50.7%), while returned zero matched outputs. performed better than , although it was still suboptimal. Both pipelines require constant refinement and custom improvements. Methods for a large-scale, automated DateTime extraction from EHR clinical notes further investigation.
急性冠状动脉综合征（ACS）是一种常迅速发展的急性心脏病。在无ST段抬高的ACS患者（NSTE - ACS）中，院前症状发作时间可提示疾病阶段和预后。我们对两个现成的自然语言处理（NLP）管道，即 和 （）进行了试点测试，以从电子健康记录（EHR）临床记录中提取患者报告的胸痛症状的日期和时间（日期时间）信息。我们纳入了三种类型的临床记录（N = 71）：病史和体格检查（n = 49）、急诊科筛查（n = 3）和分诊记录（n = 19）。所有记录均针对症状发作的真实日期时间进行了人工标注。 在36份记录（50.7%）中返回了匹配的日期时间输出，而 返回了零匹配输出。 表现优于 ，尽管仍未达到最佳状态。两个管道都需要不断完善和定制改进。从EHR临床记录中大规模自动提取日期时间的方法有待进一步研究。
