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MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records.

Abstract：

OBJECTIVES:Major adverse cardiac events (MACE) of acute coronary syndrome (ACS) often occur suddenly resulting in high mortality and morbidity. Recently, the rapid development of electronic medical records (EMR) provides the opportunity to utilize the potential of EMR to improve the performance of MACE prediction. In this study, we present a novel data-mining based approach specialized for MACE prediction from a large volume of EMR data. METHODS:The proposed approach presents a new classification algorithm by applying both over-sampling and under-sampling on minority-class and majority-class samples, respectively, and integrating the resampling strategy into a boosting framework so that it can effectively handle imbalance of MACE of ACS patients analogous to domain practice. The method learns a new and stronger MACE prediction model each iteration from a more difficult subset of EMR data with wrongly predicted MACEs of ACS patients by a previous weak model. RESULTS:We verify the effectiveness of the proposed approach on a clinical dataset containing 2930 ACS patient samples with 268 feature types. While the imbalanced ratio does not seem extreme (25.7%), MACE prediction targets pose great challenge to traditional methods. As these methods degenerate dramatically with increasing imbalanced ratios, the performance of our approach for predicting MACE remains robust and reaches 0.672 in terms of AUC. On average, the proposed approach improves the performance of MACE prediction by 4.8%, 4.5%, 8.6% and 4.8% over the standard SVM, Adaboost, SMOTE, and the conventional GRACE risk scoring system for MACE prediction, respectively. CONCLUSIONS:We consider that the proposed iterative boosting approach has demonstrated great potential to meet the challenge of MACE prediction for ACS patients using a large volume of EMR.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Huang Z,Chan TM,Dong W

doi

10.1016/j.jbi.2017.01.001

subject

Has Abstract

pub_date

2017-02-01 00:00:00

pages

161-170

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(17)30001-1

journal_volume

pub_type

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MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records.