Title
Learning Electronic Health Records through Hyperbolic Embedding of Medical Ontologies.
Abstract
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.
Year
DOI
Venue
2019
10.1145/3307339.3342148
BCB
Field
DocType
ISBN
Ontology (information science),Embedding,Information retrieval,Computer science,Artificial intelligence,Machine learning
Conference
978-1-4503-6666-3
Citations 
PageRank 
References 
0
0.34
0
Authors
10
Name
Order
Citations
PageRank
Qiuhao Lu100.68
Nisansa de Silva2223.53
Sabin Kafle300.68
Jiazhen Cao400.34
Dejing Dou589290.86
Thuy Thanh Nguyen623632.55
Prithviraj Sen783738.24
Brent Hailpern8515100.51
Berthold Reinwald990179.37
Yunyao Li1053037.81