Benchmarking AutoML frameworks for disease prediction using medical claims.

ABSTRACT:

Objectives

Ascertain and compare the performances of Automated Machine Learning (AutoML) tools on large, highly imbalanced healthcare datasets.

Materials and methods

We generated a large dataset using historical de-identified administrative claims including demographic information and flags for disease codes in four different time windows prior to 2019. We then trained three AutoML tools on this dataset to predict six different disease outcomes in 2019 and evaluated model performances on several metrics.

Results

The AutoML tools showed improvement from the baseline random forest model but did not differ significantly from each other. All models recorded low area under the precision-recall curve and failed to predict true positives while keeping the true negative rate

SUBMITTER: A Romero RA 

PROVIDER: S-EPMC9327416 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature