Identifying chronic disease patients using predictive algorithms in pharmacy administrative claims: an application in rheumatoid arthritis.
Ervant J Maksabedian HernandezIsabelle TingzonLorenzo AmpilJessica TiuPublished in: Journal of medical economics (2021)
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.
Keyphrases
- rheumatoid arthritis
- neural network
- machine learning
- disease activity
- end stage renal disease
- health insurance
- ejection fraction
- newly diagnosed
- deep learning
- chronic kidney disease
- ankylosing spondylitis
- prognostic factors
- interstitial lung disease
- emergency department
- systemic lupus erythematosus
- systemic sclerosis
- idiopathic pulmonary fibrosis