| Literature DB >> 32308812 |
Prakash Adekkanattu1, Guoqian Jiang2, Yuan Luo3, Paul R Kingsbury2, Zhenxing Xu1, Luke V Rasmussen3, Jennifer A Pacheco3, Richard C Kiefer3, Daniel J Stone2, Pascal S Brandt4, Liang Yao3, Yizhen Zhong3, Yu Deng3, Fei Wang1, Jessica S Ancker1, Thomas R Campion1, Jyotishman Pathak1.
Abstract
While natural language processing (NLP) of unstructured clinical narratives holds the potential for patient care and clinical research, portability of NLP approaches across multiple sites remains a major challenge. This study investigated the portability of an NLP system developed initially at the Department of Veterans Affairs (VA) to extract 27 key cardiac concepts from free-text or semi-structured echocardiograms from three academic edical centers: Weill Cornell Medicine, Mayo Clinic and Northwestern Medicine. While the NLP system showed high precision and recall easurements for four target concepts (aortic valve regurgitation, left atrium size at end systole, mitral valve regurgitation, tricuspid valve regurgitation) across all sites, we found moderate or poor results for the remaining concepts and the NLP system performance varied between individual sites. ©2019 AMIA - All rights reserved.Entities:
Mesh:
Year: 2020 PMID: 32308812 PMCID: PMC7153064
Source DB: PubMed Journal: AMIA Annu Symp Proc ISSN: 1559-4076