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Title: Learning Electronic Health Records through Hyperbolic Embedding of Medical Ontologies
Unplanned intensive care units (ICU) readmissions and in-hospital mortality of patients are two important metrics for evaluating the quality of hospital care. Identifying patients with higher risk of readmission to ICU or of mortality can not only protect those patients from potential dangers, but also reduce the high costs of healthcare. In this work, we propose a new method to incorporate information from the Electronic Health Records (EHRs) of patients and utilize hyperbolic embeddings of a medical ontology (i.e., ICD-9) in the prediction model. The results prove the effectiveness of our method and show that hyperbolic embeddings of ontological concepts give promising performance.  more » « less
Award ID(s):
1747798
NSF-PAR ID:
10131156
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
Page Range / eLocation ID:
338 to 346
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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