Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism.
Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) is particularly difficult to identify using existing approaches. This study aims to develop and evaluate a Large Language Model (LLM)-based framework for detecting PEAE from unstructured narrative data in EMRs.
Author(s): Cheligeer, Cheligeer, Southern, Danielle A, Yan, Jun, Wu, Guosong, Pan, Jie, Lee, Seungwon, Martin, Elliot A, Jafarpour, Hamed, Eastwood, Cathy A, Zeng, Yong, Quan, Hude
DOI: 10.1093/jamia/ocaf048