  The vast amount of health data has been continuously collected for each
patient, providing opportunities to support diverse healthcare predictive tasks
such as seizure detection and hospitalization prediction. Existing models are
mostly trained on other patients data and evaluated on new patients. Many of
them might suffer from poor generalizability. One key reason can be overfitting
due to the unique information related to patient identities and their data
collection environments, referred to as patient covariates in the paper. These
patient covariates usually do not contribute to predicting the targets but are
often difficult to remove. As a result, they can bias the model training
process and impede generalization. In healthcare applications, most existing
domain generalization methods assume a small number of domains. In this paper,
considering the diversity of patient covariates, we propose a new setting by
treating each patient as a separate domain (leading to many domains). We
develop a new domain generalization method ManyDG, that can scale to such
many-domain problems. Our method identifies the patient domain covariates by
mutual reconstruction and removes them via an orthogonal projection step.
Extensive experiments show that ManyDG can boost the generalization performance
on multiple real-world healthcare tasks (e.g., 3.7% Jaccard improvements on
MIMIC drug recommendation) and support realistic but challenging settings such
as insufficient data and continuous learning.
