  Multimodal electronic health record (EHR) data are widely used in clinical
applications. Conventional methods usually assume that each sample (patient) is
associated with the unified observed modalities, and all modalities are
available for each sample. However, missing modality caused by various clinical
and social reasons is a common issue in real-world clinical scenarios. Existing
methods mostly rely on solving a generative model that learns a mapping from
the latent space to the original input space, which is an unstable ill-posed
inverse problem. To relieve the underdetermined system, we propose a model
solving a direct problem, dubbed learning with Missing Modalities in Multimodal
healthcare data (M3Care). M3Care is an end-to-end model compensating the
missing information of the patients with missing modalities to perform clinical
analysis. Instead of generating raw missing data, M3Care imputes the
task-related information of the missing modalities in the latent space by the
auxiliary information from each patient's similar neighbors, measured by a
task-guided modality-adaptive similarity metric, and thence conducts the
clinical tasks. The task-guided modality-adaptive similarity metric utilizes
the uncensored modalities of the patient and the other patients who also have
the same uncensored modalities to find similar patients. Experiments on
real-world datasets show that M3Care outperforms the state-of-the-art
baselines. Moreover, the findings discovered by M3Care are consistent with
experts and medical knowledge, demonstrating the capability and the potential
of providing useful insights and explanations.
