Uncertainty-aware approach for multiple imputation using conventional and machine learning models: a real-world data study

Uncertainty-aware approach for multiple imputation using conventional and machine learning models: a real-world data study

Abstract Missing data poses a significant challenge in clinical real-world studies, often arising from unplanned data collection, misplacement, patient loss to follow-up, and other factors. While multiple imputation by chained equations (MICE) is a widely used method, its sequential nature introduce...

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Bibliographic Details
Main Authors: Romen Samuel Wabina, Panu Looareesuwan, Suphachoke Sonsilphong, Htun Teza, Wanchana Ponthongmak, Gareth McKay, John Attia, Anuchate Pattanateepapon, Anupol Panitchote, Ammarin Thakkinstian
Format: Article
Language:English
Published: SpringerOpen 2025-04-01
Series:Journal of Big Data
Subjects:
Multiple imputation
Uncertainty-aware models
Uncertainty functions
Real-world data
Missing data
Online Access:https://doi.org/10.1186/s40537-025-01136-3
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https://doi.org/10.1186/s40537-025-01136-3

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