title: Prediction using patient comparison vs. modeling: a case study for mortality prediction.
abstract: 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.
authors.forename: Mark
authors.initials: M
authors.lastname: Hoogendoorn
authors.forename: Ali
authors.initials: A
authors.lastname: El Hassouni
authors.forename: Kwongyen
authors.initials: K
authors.lastname: Mok
authors.forename: Marzyeh
authors.initials: M
authors.lastname: Ghassemi
authors.forename: Peter
authors.initials: P
authors.lastname: Szolovits
mesh_terms: D000465:Algorithms; D057286:Electronic Health Records; D006801:Humans; D008962:Models, Theoretical; D009026:Mortality; D013997:Time Factors