MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records.
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...
| Published in: | Journal of Biomedical Informatics Vol. 66; pp. 161 - 171 |
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| Main Authors: | , , |
| Format: | research Journal Article |
| Published: |
Academic Press Inc.
Feb2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121276685&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121276685 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Feb2017 vid: 66 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 121276685 121276685 NLM28065840 121276685 10.1016/j.jbi.2017.01.001 NLM28065840 121276685 ppf: 161 ppct: 10 formats: tig: atl: MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records. aug: au: Huang, Zhengxing Chan, Tak-Ming Dong, Wei affil: College of Biomedical Engineering and Instrument Science, Zhejiang University, China sug: subj: Acute Coronary Syndrome Diagnosis Algorithms Resource Databases Data Mining Human ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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