Benchmarking AutoML frameworks for disease prediction using medical claims

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...

Full description

Bibliographic Details
Main Authors: Roland Albert A. Romero, Mariefel Nicole Y. Deypalan, Suchit Mehrotra, John Titus Jungao, Natalie E. Sheils, Elisabetta Manduchi, Jason H. Moore
Format: Article
Language:English
Published: BMC 2022-07-01
Series:BioData Mining
Subjects:
Automated machine learning
AutoML
Machine learning
Healthcare
Medical claims
Class imbalance
Online Access:https://doi.org/10.1186/s13040-022-00300-2

Internet

https://doi.org/10.1186/s13040-022-00300-2

Similar Items