Benchmarking AutoML frameworks for disease prediction using medical claims

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 dise...

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Bibliographic Details
Published in:	BioData mining Vol. 15; no. 1; pp. 1 - 13
Main Authors:	A. Romero, Roland Albert, Y. Deypalan, Mariefel Nicole, Mehrotra, Suchit, Jungao, John Titus, Sheils, Natalie E., Manduchi, Elisabetta, Moore, Jason H.
Format:	Journal Article
Language:	English
Published:	London BioMed Central 26.07.2022 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Algorithms Analysis Automated machine learning AutoML Benchmarks Big Data Bioinformatics Biomedical and Life Sciences Class imbalance Codes Computational Biology/Bioinformatics Computer Appl. in Life Sciences Data Mining and Knowledge Discovery Datasets Diabetes Disease Health care Healthcare Life Sciences Lung cancer Machine learning Medical claims Medical research Medicine, Experimental Performance prediction Population Resampling Short Report Subject specialists Automated machine learning Healthcare AutoML Medical claims Class imbalance Machine learning
ISSN:	1756-0381, 1756-0381
Online Access:	Get full text
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