Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization.
Introduction: The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for ident...
| Published in: | Journal of Cardiovascular Electrophysiology Vol. 32; no. 9; pp. 2504 - 2515 |
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| Main Authors: | , , , , , , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Sep2021
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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=152421952&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152421952 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10453873 GSB jtl: Journal of Cardiovascular Electrophysiology issn: 10453873 maglogo: Y pubinfo: dt: Sep2021 vid: 32 iid: 9 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 152421952 151572714 152421952 152421952 10.1111/jce.15171 152421952 ppf: 2504 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization. aug: au: Cai, Cheng Tafti, Ahmad P. Ngufor, Che Zhang, Pei Xiao, Peilin Dai, Mingyan Liu, Hongfang Noseworthy, Peter Chen, Minglong Friedman, Paul A. Cha, Yong‐Mei affil: Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China sug: subj: Ensemble Learning Methods Treatment Outcomes Cardiac Resynchronization Therapy Human Retrospective Design Electronic Health Records Neural Networks (Computer) Electrocardiography Waveforms ROC Curve Descriptive Statistics Artificial Intelligence ab: Introduction: The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders. Methods: We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017. An ensemble of ensemble (EoE) machine learning (ML) system composed of a supervised and an unsupervised ML layers was developed to generate a prediction model for CRT response. Results: We compared the performance of EoE against traditional ML methods and the state‐of‐the‐art convolutional neural network (CNN) model trained on raw electrocardiographic (ECG) waveforms. We observed that the models exhibited improvement in performance as more features were incrementally used for training. Using the most comprehensive set of predictors, the performance of the EoE model in terms of the area under the receiver operating characteristic curve and F1‐score were 0.76 and 0.73, respectively. Direct application of the CNN model on the raw ECG waveforms did not generate promising results. Conclusion: The proposed CRT risk calculator effectively discriminates which heart failure (HF) patient is likely to respond to CRT significantly better than using clinical guidelines and traditional ML methods, thus suggesting that the tool can enhanced care management of HF patients by helping to identify high‐risk patients. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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