Identifying Asthma-Related Symptoms From Electronic Health Records Using a Hybrid Natural Language Processing Approach Within a Large Integrated Health Care System: Retrospective Study - Fagen Xie; Robert S Zeiger; Mary Marycania Saparudin; Sahar Al-Salman; Eric Puttock; William Crawford; Michael Schatz; Stanley Xu; William M Vollmer; Wansu Chen (JMIR AI, 2025)
Abstract BackgroundAsthma-related symptoms are significant predictors of asthma exacerbation. Most of these symptoms are documented in clinical notes in a free-text format, and effective methods for capturing asthma-related symptoms from unstructured data are lacking. ObjectiveThe study aims to develop a natural language processing (NLP) algorithm for identifying symptoms associated with asthma from clinical notes within a large integrated health care system. MethodsWe analyzed unstructured clinical notes within 2 years before a visit with asthma diagnosis in 2013‐2018 and 2021‐2022 to identify 4 common asthma-related symptoms. Related terms and phrases were initially compiled from publicly available resources and then refined through clinician input and chart review. A rule-based NLP algorithm was iteratively developed and refined via multiple rounds of chart review followed by adjudication. Subsequently, transformer-based deep learning algorithms were trained using the same manually annotated datasets. A hybrid NLP algorithm was then generated by combining rule-based and transformer-based algorithms. The hybrid NLP algorithm was finally applied to the implementation notes. ResultsA total of 11,374,552 eligible clinical notes with 128,211,793 sentences were analyzed. After applying the hybrid algorithm to implementation notes, at least 1 asthma-related symptom was identified in 1,663,450 out of 127,763,086 (1.3%) sentences and 858,350 out of 11,364,952 (7.55%) notes, respectively. Cough was the most frequently identified at both the sentence (1,363,713/127,763,086, 1.07%) and note (660,685/11,364,952, 5.81%) levels, while chest tightness was the least frequent at both the sentence (141,733/127,763,086, 0.11%) and note (64,251/11,364,952, 0.57%) levels. The frequency of multiple symptoms ranged from 0.03% (36,057/127,763,086) to 0.38% (484,050/127,763,086) at the sentence level and 0.10% (10,954/11,364,952) to 1.85% (209,805/11,364,952) at the note level. Validation against 1600 manually annotated clinical notes yielded a positive predictive value ranging from 96.53% (wheezing) to 97.42% (chest tightness) at the sentence level and 96.76% (wheezing) to 97.42% (chest tightness) at the note level. Sensitivity ranged from 93.9% (dyspnea) to 95.95% (cough) at the sentence level and 96% (chest tightness) to 99.07% (cough) at the note level. All 4 symptoms had F1 ConclusionsThe developed NLP algorithms could effectively capture asthma-related symptoms from unstructured clinical notes. These algorithms could be used to facilitate early asthma detection and predict exacerbation risk.