Generative adversarial networks for imputing missing data for big data clinical research.

ABSTRACT:

Background

Missing data is a pervasive problem in clinical research. Generative adversarial imputation nets (GAIN), a novel machine learning data imputation approach, has the potential to substitute missing data accurately and efficiently but has not yet been evaluated in empirical big clinical datasets.

Objectives

This study aimed to evaluate the accuracy of GAIN in imputing missing values in large real-world clinical datasets with mixed-type variables. The computation efficiency of GAIN was also evaluated. The performance of GAIN was compared with other commonly used methods, MICE and missForest.

Methods

Two real world clinical datasets were used. The first was that of a cohort study on the long-term outcomes of patients with diabetes (50,000 complete cases), and th

SUBMITTER: Dong W 

PROVIDER: S-EPMC8059005 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature