Abstract
Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims-based algorithms (CBAs) are highly dependent on the knowledge level and not necessarily optimized for target conditions. We investigated whether machine learning methods can supplement researchers’ knowledge of target conditions in building CBAs. Retrospective cohort study using a claims database combined with annual health check-up results of employees’ health insurance programs for fiscal year 2016–17 in Japan (study population for hypertension, N = 631,289; diabetes, N = 152,368; dyslipidemia, N = 614,434). We constructed CBAs with logistic regression, k-nearest neighbor, support vector machine, penalized logistic regression, tree-based model, and neural network for identifying patients with three common chronic conditions: hypertension, diabetes, and dyslipidemia. We then compared their association measures using a completely hold-out test set (25% of the study population). Among the test cohorts of 157,822, 38,092, and 153,608 enrollees for hypertension, diabetes, and dyslipidemia, 25.4%, 8.4%, and 38.7% of them had a diagnosis of the corresponding condition. The areas under the receiver operating characteristic curve (AUCs) of the logistic regression with/without subject-matter knowledge about the target condition were .923/.921 for hypertension, .957/.938 for diabetes, and .739/.747 for dyslipidemia. The logistic lasso, logistic elastic-net, and tree-based methods yielded AUCs comparable to those of the logistic regression with subject-matter knowledge: .923-.931 for hypertension; .958-.966 for diabetes; .747-.773 for dyslipidemia. We found that machine learning methods can attain AUCs comparable to the conventional knowledge-based method in building CBAs.
Highlights
A growing body of studies using medical and pharmacy claims data has been conducted in various fields of health research [1,2,3,4,5,6,7]
90% of the ICD-10 codes that appeared in the dataset were only observed for less than 1% of enrollees, and more than half of the World Health Organization-Anatomical Therapeutic Chemical (WHO-ATC) codes that appeared in the dataset were observed for less than 5% of enrollees
Using health check-up results as the source of the gold standard, we demonstrated the association measures of the claims-based algorithms (CBAs) derived from machine learning methods without a condition-specific variable selection for identifying patients with three common chronic medical conditions, hypertension, diabetes, and dyslipidemia
Summary
A growing body of studies using medical and pharmacy claims data has been conducted in various fields of health research [1,2,3,4,5,6,7]. Despite its large volume of information and highly standardized format, claims data is frequently criticized for its potential imprecision in the identification of medical conditions mainly because they are primarily issued for reimbursement purpose [8,9,10,11,12] To address these concerns, plenty of studies have proposed a claims-based algorithm (CBA) for identifying patients with their target condition and computed association measures to assess the usability of the algorithm [9, 10, 13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41]. This is imposing challenges to the use of administrative data in the transition from the ICD-9 to the ICD-10 coding scheme in the United States [42, 43]
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