Continuous, Learned Imputation of Missing Values in Parkinson's Disease

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.