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Abstract: Natural Language Processing (NLP) holds potential for patient care and clinical research, but a gap exists between promise and reality. While some studies have demonstrated portability of NLP systems across multiple sites, challenges remain. Strategies to mitigate these challenges can strive for complex NLP problems using advanced methods (hard-to-reach fruit), or focus on simple NLP problems using practical methods (low-hanging fruit). This paper investigates a practical strategy for NLP portability using extraction of left ventricular ejection fraction (LVEF) as a use case. We used a tool developed at the Department of Veterans Affair (VA) to extract the LVEF values from free-text echocardiograms in the MIMIC-III database. The approach showed an accuracy of 98.4%, sensitivity of 99.4%, a positive predictive value of 98.7%, and F-score of 99.0%. This experience, in which a simple NLP solution proved highly portable with excellent performance, illustrates the point that simple NLP applications may be easier to disseminate and adapt, and in the short term may prove more useful, than complex applications.

Learning Objective 1: This paper describes a case study illustrating a practical strategy for portability of Natural Language Processing across institutions.

Authors:

Stephen Johnson (Presenter)
Weill Cornell Medicine

Prakash Adekkanattu, Weill Cornell Medicine
Thomas Campion, Weill Cornell Medicine
James Flory, Weill Cornell Medicine
Jyotishman Pathak, Weill Cornell Medicine
Olga Patterson, VA Salt Lake City Health Care System
Scott DuVall, VA Salt Lake City Health Care System
Vincent Major, NYU Langone Health
Yindalon Aphinyanaphongs, NYU Langone Health

Presentation Materials:

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