Prediction using patient comparison vs. modeling: A case study for mortality prediction
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Szolovits_Prediction_Using_Patient_Comparison_Ghassemi_Marzyeh_EMBC_2016.pdf
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Author(s)
Hoogendoorn, Mark
el Hassouni, Ali
Mok, Kwongyen
Ghassemi, Marzyeh
Szolovits, Peter
Date Issued
October 2016
Journal
38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
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.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/EMBC.2016.7591229
