Cao, Jiazhen2019-06-282019-06-282019-06-14https://hdl.handle.net/1794/2474427 pagesIn-hospital mortality prediction is signi cant for evaluating a patient's severity of illness ahead of the time. The outcome of the evaluation can help physicians to identify which patient is at risk and needs immediate care, it can further increase the e ciency of use of medical resources. In this study, I proposed a method that is similar with the one in our Electronic Health Records (EHRs) research at the CBL Lab and utilized the hyperbolic embedding of ICD-9 medical ontology for the prediction model. The results outperformed the benchmark prediction model and demonstrated that the hyperbolic embedding on ICD-9 is more e ective than other graph embedding methods.enCreative Commons BY-NC-ND 4.0-USMachine learningIn-hospital mortalityRepresentation learningThesis / Dissertation