Benchmarking missing-values approaches for predictive models on health databases.

ABSTRACT:

Background

As databases grow larger, it becomes harder to fully control their collection, and they frequently come with missing values. These large databases are well suited to train machine learning models, e.g., for forecasting or to extract biomarkers in biomedical settings. Such predictive approaches can use discriminative-rather than generative-modeling and thus open the door to new missing-values strategies. Yet existing empirical evaluations of strategies to handle missing values have focused on inferential statistics.

Results

Here we conduct a systematic benchmark of missing-values strategies in predictive models with a focus on large health databases: 4 electronic health record datasets, 1 population brain imaging database, 1 health survey, and 2 intensive care surv

SUBMITTER: Perez-Lebel A 

PROVIDER: S-EPMC9012100 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature