Robert Stephen Zeiger

Background: Asthma-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. Objective: The 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. Methods: We 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. Results: A 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-scores greater than 0.95 at both the sentence and note levels, regardless of NLP algorithms. Conclusions: The 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.

Associations of omega-3 fatty acids (n-3) with allergic diseases are inconsistent, perhaps in part due to genetic variation. We sought to identify and validate genetic variants that modify associations of n-3 with childhood asthma or atopy in participants in the Vitamin D Antenatal Asthma Reduction Trial (VDAART) and the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC). Dietary n-3 was derived from food frequency questionnaires and plasma n-3 was measured via untargeted mass spectrometry in early childhood and children aged 6 years old. Interactions of genotype with n-3 in association with asthma or atopy at age 6 years were sought for six candidate genes/gene regions and genome-wide. Two SNPs in the region of DPP10 (rs958457 and rs1516311) interacted with plasma n-3 at age 3 years in VDAART (p = 0.007 and 0.003, respectively) and with plasma n-3 at age 18 months in COPSAC (p = 0.01 and 0.02, respectively) in associationwith atopy. Another DPP10 region SNP, rs1367180, interacted with dietary n-3 at age 6 years in VDAART (p = 0.009) and with plasma n-3 at age 6 years in COPSAC (p = 0.004) in association with atopy. No replicated interactions were identified for asthma. The effect of n-3 on reducing childhood allergic disease may differ by individual factors, including genetic variation in the DPP10 region.