MANNERS: A strategy for representation learning in multivariate datasets with high proportions of missing data

Missing data present a major challenge for deep learning, and various imputation techniques exist. However, imputation quality generally decreases as missing data rates increase. In time-series data from electronic health records, missing rates for individual parameters up to 99% can be observed, posing a problem for mere imputation. In addition, underlying patterns between missing and observed data can contain valuable information that can be learned by deep learning models. In this work, we propose a strategy called missing adjusted normalization and nullity encoding representation strategy (MANNERS), which can be applied to training pipelines for representation learning. MANNERS encodes missingness, masks loss evaluation at missing data points, and applies rebalancing such that the signal from variables with high missing rates is not lost. We evaluated MANNERS on reconstruction and downstream classification, regression, and synthetic data generation tasks. We showed performance improvements in the presence of very high missing rates compared with state-of-the-art imputation-only techniques.