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Continuous, Learned Imputation of Missing Values in Parkinson’s Disease
Christopher Gundler, Monika Pötter-Nerger
654 - 658
10.3233/SHTI240499
Research Article
Studies in Health Technology and Informatics
Volume 316: Digital Health and Informatics Innovations for Sustainable Health Care Systems
Abstract

Parkinson’s disease management requires accurate clinical scores but suffers from missing data. Leveraging self-supervised learning, we demonstrate superior generalization capabilities across populations compared to other well-established imputation techniques (MIWAE, MissForest, MICE). With the ability to employ the method already during the data collection and not afterward, the technology allows more robust data collection in clinical reality.

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