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This content will become publicly available on September 21, 2022

Title: Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction
Electronic health records (EHRs) have been heavily used in modern healthcare systems for recording patients' admission information to health facilities. Many data-driven approaches employ temporal features in EHR for predicting specific diseases, readmission times, and diagnoses of patients. However, most existing predictive models cannot fully utilize EHR data, due to an inherent lack of labels in supervised training for some temporal events. Moreover, it is hard for the existing methods to simultaneously provide generic and personalized interpretability. To address these challenges, we propose Sherbet, a self-supervised graph learning framework with hyperbolic embeddings for temporal health event prediction. We first propose a hyperbolic embedding method with information flow to pretrain medical code representations in a hierarchical structure. We incorporate these pretrained representations into a graph neural network (GNN) to detect disease complications and design a multilevel attention method to compute the contributions of particular diseases and admissions, thus enhancing personalized interpretability. We present a new hierarchy-enhanced historical prediction proxy task in our self-supervised learning framework to fully utilize EHR data and exploit medical domain knowledge. We conduct a comprehensive set of experiments on widely used publicly available EHR datasets to verify the effectiveness of our model. Our results demonstrate the proposed more » model's strengths in both predictive tasks and interpretable abilities. « less
Authors:
; ;
Award ID(s):
2047843 1948432
Publication Date:
NSF-PAR ID:
10318647
Journal Name:
IEEE Transactions on Cybernetics
ISSN:
2168-2267
Sponsoring Org:
National Science Foundation
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