MetaCare++: Meta-Learning with Hierarchical Subtyping for Cold-Start Diagnosis Prediction in Healthcare Data

Abstract

Cold-start diagnosis prediction is a challenging task for AI in healthcare, where often only a few visits per patient and a few observations per disease can be exploited. Although meta-learning is widely adopted to address the data sparsity problem in general domains, directly applying it to healthcare data is less effective, since it is unclear how to capture both the temporal relations in clinical visits and the complicated relations among syndromic diseases for precise personalized diagnosis. To this end, we first propose a novel Meta-learning framework for cold-start diagnosis prediction in healthCare data (MetaCare). By explicitly encoding the effects of disease progress over time as a generalization prior, MetaCare dynamically predicts future diagnosis and timestamp for infrequent patients. Then, to model complicated relations among rare diseases, we propose to utilize domain knowledge of hierarchical relations among diseases, and further perform diagnosis subtyping to mine the latent syndromic relations among diseases. Finally, to tailor the generic meta-learning framework with personalized parameters, we design a hierarchical patient subtyping mechanism and bridge the modeling of both infrequent patients and rare diseases. We term the joint model as MetaCare++. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by MetaCare++, yielding average improvements of 7.71% for diagnosis prediction and 13.94% for diagnosis time prediction over the state-of-the-art baselines.

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