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Start Date

16-8-2018 12:00 AM

Description

We extend a similarity measure for medical event sequences (MESs) and evaluate its performance on mortality prediction using a substantial trauma data set. We extend the Optimal Temporal Common Subsequence for MESs (OTCS-MES) measure by generalizing the event-matching component with user-defined weights. In the empirical evaluation of classification performance, we provide a more complete evaluation than previous studies. We compare the predictive performance of the Trauma Mortality Prediction Model (TMPM), an accepted regression approach for mortality prediction in trauma data, to nearest neighbor algorithms using similarity measures for MESs. Using a data set from the National Trauma Data Bank, our results indicate improved predictive performance for an ensemble of nearest neighbor classifiers over TMPM. Our analysis demonstrates a superior Receiver Operating Characteristics (ROC) curve, larger AUC, and improved operating points on a ROC curve. Predictive performance improves for the ensemble for a variety of sensitivity weights and false positive constraints.

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Aug 16th, 12:00 AM

Mortality Prediction Performance using Similarity Measures for Medical Event Sequences

We extend a similarity measure for medical event sequences (MESs) and evaluate its performance on mortality prediction using a substantial trauma data set. We extend the Optimal Temporal Common Subsequence for MESs (OTCS-MES) measure by generalizing the event-matching component with user-defined weights. In the empirical evaluation of classification performance, we provide a more complete evaluation than previous studies. We compare the predictive performance of the Trauma Mortality Prediction Model (TMPM), an accepted regression approach for mortality prediction in trauma data, to nearest neighbor algorithms using similarity measures for MESs. Using a data set from the National Trauma Data Bank, our results indicate improved predictive performance for an ensemble of nearest neighbor classifiers over TMPM. Our analysis demonstrates a superior Receiver Operating Characteristics (ROC) curve, larger AUC, and improved operating points on a ROC curve. Predictive performance improves for the ensemble for a variety of sensitivity weights and false positive constraints.