TY - JOUR
T1 - Natural Language Processing for Asthma Ascertainment in Different Practice Settings
AU - Wi, Chung Il
AU - Sohn, Sunghwan
AU - Ali, Mir
AU - Krusemark, Elizabeth
AU - Ryu, Euijung
AU - Liu, Hongfang
AU - Juhn, Young J.
N1 - Publisher Copyright:
© 2017 American Academy of Allergy, Asthma & Immunology
PY - 2018/1
Y1 - 2018/1
N2 - Background We developed and validated NLP-PAC, a natural language processing (NLP) algorithm based on predetermined asthma criteria (PAC) for asthma ascertainment using electronic health records at Mayo Clinic. Objective To adapt NLP-PAC in a different health care setting, Sanford Children Hospital, by assessing its external validity. Methods The study was designed as a retrospective cohort study that used a random sample of 2011-2012 Sanford Birth cohort (n = 595). Manual chart review was performed on the cohort for asthma ascertainment on the basis of the PAC. We then used half of the cohort as a training cohort (n = 298) and the other half as a blind test cohort to evaluate the adapted NLP-PAC algorithm. Association of known asthma-related risk factors with the Sanford-NLP algorithm–driven asthma ascertainment was tested. Results Among the eligible test cohort (n = 297), 160 (53%) were males, 268 (90%) white, and the median age was 2.3 years (range, 1.5-3.1 years). NLP-PAC, after adaptation, and the human abstractor identified 74 (25%) and 72 (24%) subjects, respectively, with 66 subjects identified by both approaches. Sensitivity, specificity, positive predictive value, and negative predictive value for the NLP algorithm in predicting asthma status were 92%, 96%, 89%, and 97%, respectively. The known risk factors for asthma identified by NLP (eg, smoking history) were similar to the ones identified by manual chart review. Conclusions Successful implementation of NLP-PAC for asthma ascertainment in 2 different practice settings demonstrates the feasibility of automated asthma ascertainment leveraging electronic health record data with a potential to enable large-scale, multisite asthma studies to improve asthma care and research.
AB - Background We developed and validated NLP-PAC, a natural language processing (NLP) algorithm based on predetermined asthma criteria (PAC) for asthma ascertainment using electronic health records at Mayo Clinic. Objective To adapt NLP-PAC in a different health care setting, Sanford Children Hospital, by assessing its external validity. Methods The study was designed as a retrospective cohort study that used a random sample of 2011-2012 Sanford Birth cohort (n = 595). Manual chart review was performed on the cohort for asthma ascertainment on the basis of the PAC. We then used half of the cohort as a training cohort (n = 298) and the other half as a blind test cohort to evaluate the adapted NLP-PAC algorithm. Association of known asthma-related risk factors with the Sanford-NLP algorithm–driven asthma ascertainment was tested. Results Among the eligible test cohort (n = 297), 160 (53%) were males, 268 (90%) white, and the median age was 2.3 years (range, 1.5-3.1 years). NLP-PAC, after adaptation, and the human abstractor identified 74 (25%) and 72 (24%) subjects, respectively, with 66 subjects identified by both approaches. Sensitivity, specificity, positive predictive value, and negative predictive value for the NLP algorithm in predicting asthma status were 92%, 96%, 89%, and 97%, respectively. The known risk factors for asthma identified by NLP (eg, smoking history) were similar to the ones identified by manual chart review. Conclusions Successful implementation of NLP-PAC for asthma ascertainment in 2 different practice settings demonstrates the feasibility of automated asthma ascertainment leveraging electronic health record data with a potential to enable large-scale, multisite asthma studies to improve asthma care and research.
KW - Algorithm adaptability
KW - Asthma ascertainment
KW - Electronic health records
KW - Epidemiology
KW - Informatics
KW - Natural language processing
KW - Retrospective study
KW - Validation
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U2 - 10.1016/j.jaip.2017.04.041
DO - 10.1016/j.jaip.2017.04.041
M3 - Article
C2 - 28634104
AN - SCOPUS:85020504387
SN - 2213-2198
VL - 6
SP - 126
EP - 131
JO - Journal of Allergy and Clinical Immunology: In Practice
JF - Journal of Allergy and Clinical Immunology: In Practice
IS - 1
ER -