@article{496482797db240b692a8694466c87bb9,
title = "Automated chart review utilizing natural language processing algorithm for asthma predictive index",
abstract = "Background: Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language processing (NLP) algorithm to identify patients that meet API criteria. Methods: This is a cross-sectional study nested in a birth cohort study in Olmsted County, MN. Asthma status ascertained by manual chart review based on API criteria served as gold standard. NLP-API was developed on a training cohort (n=87) and validated on a test cohort (n=427). Criterion validity was measured by sensitivity, specificity, positive predictive value and negative predictive value of the NLP algorithm against manual chart review for asthma status. Construct validity was determined by associations of asthma status defined by NLP-API with known risk factors for asthma. Results: Among the eligible 427 subjects of the test cohort, 48% were males and 74% were White. Median age was 5.3years (interquartile range 3.6-6.8). 35 (8%) had a history of asthma by NLP-API vs. 36 (8%) by abstractor with 31 by both approaches. NLP-API predicted asthma status with sensitivity 86%, specificity 98%, positive predictive value 88%, negative predictive value 98%. Asthma status by both NLP and manual chart review were significantly associated with the known asthma risk factors, such as history of allergic rhinitis, eczema, family history of asthma, and maternal history of smoking during pregnancy (p value <0.05). Maternal smoking [odds ratio: 4.4, 95% confidence interval 1.8-10.7] was associated with asthma status determined by NLP-API and abstractor, and the effect sizes were similar between the reviews with 4.4 vs 4.2 respectively. Conclusion: NLP-API was able to ascertain asthma status in children mining from EHR and has a potential to enhance asthma care and research through population management and large-scale studies when identifying children who meet API criteria.",
keywords = "API, Asthma, Epidemiology, Informatics, NLP",
author = "Harsheen Kaur and Sunghwan Sohn and Wi, {Chung Il} and Euijung Ryu and Park, {Miguel A.} and Kay Bachman and Hirohito Kita and Ivana Croghan and Castro-Rodriguez, {Jose A.} and Voge, {Gretchen A.} and Hongfang Liu and Juhn, {Young J.}",
note = "Funding Information: Demographic characteristics of the population of Rochester and Olmsted County were similar to those of the U.S. Caucasian population, with the exception of a higher proportion of the working population of this community being employed in the health care industry [19]. Olmsted County has a few important epidemiological advantages for conducting retrospective studies such as this because medical care is virtually self-contained within the community. In addition, research authorization for using medical records for research purposes is obtained from the patients the first time they ever register with a provider in the community. The rate of granting this authorization is about 95% in Olmsted County [25]. Once this permission is granted, each patient is assigned a unique identifier under the auspices of the Rochester Epidemiology Project, which has been continuously funded by the National Institute of Health (NIH) since 1966 [26]. Using this unique identifier, all clinical diagnoses and events, and detailed information from every interaction among the patients and providers are retrieved from detailed patient-based medical records [26]. As this resource has been electronically available since 1997 (i.e., the inception of the EHR at Mayo Clinic), it enables us to retrieve all asthma-related events and associated free-text information (e.g., symptoms, visits, and medications) electronically to ascertain asthma status based on API [15]. Funding Information: Dr. Young Juhn is the Principal Investigator (PI) of the Innovative Asthma Research Methods Award from Genentech and the PI of Real World Evidence Pediatric Asthma Study supported by Roche/Genentech. Otherwise, the authors have nothing to disclose that pose a conflict of interest. Funding Information: This was supported by National Institute of Health (NIH)-funded R01 grant (R01 HL126667). This study also utilized the resources of the Rochester Epidemiology Project, which is supported by the National Institute on Aging of the National Institutes of Health under Award Number R01 AG034676. Publisher Copyright: {\textcopyright} 2018 The Author(s).",
year = "2018",
month = feb,
day = "13",
doi = "10.1186/s12890-018-0593-9",
language = "English (US)",
volume = "18",
journal = "BMC Pulmonary Medicine",
issn = "1471-2466",
publisher = "BioMed Central",
number = "1",
}