Identifying chronic disease patients using predictive algorithms in pharmacy administrative claims: an application in rheumatoid arthritis.
Objective: To evaluate the predictive performance of logistic and linear regression versus machine learning (ML) algorithms to identify patients with rheumatoid arthritis (RA) treated with target immunomodulators (TIMs) using only pharmacy administrative claims. Methods: Adults aged 18-64 years with...
| Publicado en: | Journal of Medical Economics Vol. 24; pp. 1272 - 1280 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Taylor & Francis Ltd
Nov2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155118483&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155118483 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13696998 3K1U jtl: Journal of Medical Economics issn: 13696998 maglogo: N pubinfo: dt: Nov2021 vid: 24 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 155118483 153258154 10.1080/13696998.2021.1999132 155118483 ppf: 1272 ppct: 8 formats: tig: atl: Identifying chronic disease patients using predictive algorithms in pharmacy administrative claims: an application in rheumatoid arthritis. aug: au: Hernandez, Ervant J. Maksabedian Tingzon, Isabelle Ampil, Lorenzo Tiu, Jessica affil: Global Health Economics, Amgen Inc, Thousand Oaks, CA, USA sug: ab: Objective: To evaluate the predictive performance of logistic and linear regression versus machine learning (ML) algorithms to identify patients with rheumatoid arthritis (RA) treated with target immunomodulators (TIMs) using only pharmacy administrative claims. Methods: Adults aged 18-64 years with ≥1 TIM claim in the IBM MarketScan commercial database were included in this retrospective analysis. The predictive ability of logistic regression to identify RA patients was compared with supervised ML classification algorithms including random forest (RF), decision trees, linear support vector machines (SVMs), neural networks, naïve Bayes classifier, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and K-nearest neighbors (k-NN). Model performance was evaluated using F1 score, accuracy, precision, sensitivity, area under the receiver operating characteristic curve (AUROC), and Matthews correlation coefficient (MCC). Analyses were conducted in all-patient and etanercept-only samples. Results: In the all-patients sample, ML approaches did not outperform logistic regression. RF showed small improvements versus logistic regression that were not considered remarkable, respectively: F1 score (84.55% vs 83.96%), accuracy (84.05% vs 83.79%), sensitivity (84.53% vs 82.20%), AUROC (84.04% vs 83.85%), and MCC (68.07% vs 67.66%). Findings were similar in the etanercept samples. Conclusion: Logistic regression and ML approaches successfully identified patients with RA in a large pharmacy administrative claims database. The ML algorithms were no better than logistic regression at prediction. RF, SVMs, LDA, and ridge classifier showed comparable performance, while neural networks, decision trees, naïve Bayes classifier, and QDA underperformed compared with logistic regression in identifying patients with RA. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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