Prediction using patient comparison vs. modeling: A case study for mortality prediction
Author(s)Hoogendoorn, Mark; el Hassouni, Ali; Mok, Kwongyen; Ghassemi, Marzyeh; Szolovits, Peter
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Information in Electronic Medical Records (EMRs) can be used to generate accurate predictions for the occurrence of a variety of health states, which can contribute to more pro-active interventions. The very nature of EMRs does make the application of off-the-shelf machine learning techniques difficult. In this paper, we study two approaches to making predictions that have hardly been compared in the past: (1) extracting high-level (temporal) features from EMRs and building a predictive model, and (2) defining a patient similarity metric and predicting based on the outcome observed for similar patients. We analyze and compare both approaches on the MIMIC-II ICU dataset to predict patient mortality and find that the patient similarity approach does not scale well and results in a less accurate model (AUC of 0.68) compared to the modeling approach (0.84). We also show that mortality can be predicted within a median of 72 hours.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Institute of Electrical and Electronics Engineers (IEEE)
Hoogendoorn, Mark, et al. "Prediction Using Patient Comparison vs. Modeling: A Case Study for Mortality Prediction." 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 16-20 August, 2016, Orlando, FL, IEEE, 2016, pp. 2464–67.
Author's final manuscript