Using Machine Learning to Predict Primary Care and Advance Workforce Research.

ABSTRACT:

Purpose

To develop and test a machine-learning-based model to predict primary care and other specialties using Medicare claims data.

Methods

We used 2014-2016 prescription and procedure Medicare data to train 3 sets of random forest classifiers (prescription only, procedure only, and combined) to predict specialty. Self-reported specialties were condensed to 27 categories. Physicians were assigned to testing and training cohorts, and random forest models were trained and then applied to 2014-2016 data sets for the testing cohort to generate a series of specialty predictions. Comparing the predicted specialty to self-report, we assessed performance with F1 scores and area under the receiver operating characteristic curve (AUROC) values.

Results

A total of 564,986 physi

SUBMITTER: Wingrove P 

PROVIDER: S-EPMC7358033 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature