Unsupervised Imputation of Non-Ignorably Missing Data Using Importance-Weighted Autoencoders
Year: 2024
Type: article
Abstract: Deep Learning (DL) methods have dramatically increased in popularity in recent years. While its initial success was demonstrated in the classification and manipulation of image data, there has been significant growth in the application of DL methods to problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of Variational Autoencoders (VAEs), a popular unsupervised DL architecture commonly utilized for dimension reduction, imputation, and learning latent representations of complex data. We propose a new VAE architecture, NIMIWAE, that is one of the first to flexibly account for both ignorable and non-ignorable patterns of missingness in input features at training time. Following training, samples can be drawn from the approximate posterior distribution of the missing data can be used for multiple imputation, facilitating downstream analyses on high dimensional incomplete datasets. We demonstrate through statistical simulation that our method outperforms existing approaches for unsupervised learning tasks and imputation accuracy. We conclude with a case study of an EHR dataset pertaining to 12,000 ICU patients containing a large number of diagnostic measurements and clinical outcomes, where many features are only partially observed.
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: G1047483779, G1569999668, Using Smartphone Assessments for Personalized Prediction of Problematic Alcohol Use, G516079320, G5539643167, RI: Small: From a Machine Detector to a Machine Detective: Decisions and Queries with Uncertain and Incomplete Information, Extrapolative Analyses for Reliable Machine Learning Driven Scientific Discovery, G9000061630, G9000064959, G9000117861, G9000194218, G9000292904
OpenAlex ID: W4399726011