Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review

Abstract

Asthma ascertainment through NLP should be considered in the era of EMRs because it can enable large-scale clinical studies in a more time-efficient manner and improve the recognition and care of childhood asthma in practice.

Expert artificial intelligence-based natural language processing characterises childhood asthma

“…Our study results are based on two asthma criteria,19 33 which have been extensively used for epidemiological investigations for asthma studies. NLP-PAC was validated at both our study setting and another study setting (Sioux Falls, South Dakota) (external validity) 14 17. This suggests that the NLP algorithm can be adapted in a different care setting with comparable performance, which may enable us to define and identify childhood asthma in a timely manner.…”

“…Since most cases of probable asthma became definite asthma over time, both definite and probable asthma were considered as PAC positive 19. Although the API was originally developed to predict asthma among preschoolers, the National Asthma Education and Prevention Program recommended it for identification of asthmatic children for timely asthma treatment (table 1-2) 14 15. We previously reported the details for the development and validation of both NLP algorithms14 15 with a great performance (sensitivity, specificity, positive predictive value and negative predictive value: 97%, 95%, 90% and 98% for NLP-PAC, and 86%, 98%, 88% and 98% for NLP-API).…”

“…Although the API was originally developed to predict asthma among preschoolers, the National Asthma Education and Prevention Program recommended it for identification of asthmatic children for timely asthma treatment (table 1-2) 14 15. We previously reported the details for the development and validation of both NLP algorithms14 15 with a great performance (sensitivity, specificity, positive predictive value and negative predictive value: 97%, 95%, 90% and 98% for NLP-PAC, and 86%, 98%, 88% and 98% for NLP-API). Briefly, both NLP algorithms had the sequential process to determine positivity for asthma criteria: (1) the text extraction that searches evidence concepts for asthma in EHRs, (2) processing the extracted concepts based on rules for asthma criteria and (3) categorising asthma status accordingly.…”

“…Given the growing deployment of electronic health records (EHRs) systems enabling large practice-based longitudinal data mining, advancement in artificial intelligence (AI) approaches such as natural language processing (NLP; expert AI) may potentially enable us to address these challenges as it can extract, process and classify free-text data from EHRs 12–15. For example, we recently developed and validated NLP algorithms for two existing retrospective criteria for childhood asthma (NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) and NLP algorithms for Asthma Predictive Index (NLP-API)) 14 15. The performance of individual NLP algorithm determining asthma status based on comprehensive EHRs including free text was almost close to that by humans (eg, 97% sensitivity and 95% specificity for NLP-PAC) 14.…”

“…For example, we recently developed and validated NLP algorithms for two existing retrospective criteria for childhood asthma (NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) and NLP algorithms for Asthma Predictive Index (NLP-API)) 14 15. The performance of individual NLP algorithm determining asthma status based on comprehensive EHRs including free text was almost close to that by humans (eg, 97% sensitivity and 95% specificity for NLP-PAC) 14. We also demonstrated external validation of our NLP algorithms for these asthma criteria across different study settings despite different population, practice and EHRs systems 16 17.…”