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Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization
Author(s) -
Cai Cheng,
Tafti Ahmad P.,
Ngufor Che,
Zhang Pei,
Xiao Peilin,
Dai Mingyan,
Liu Hongfang,
Noseworthy Peter,
Chen Minglong,
Friedman Paul A.,
Cha YongMei
Publication year - 2021
Publication title -
journal of cardiovascular electrophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.193
H-Index - 138
eISSN - 1540-8167
pISSN - 1045-3873
DOI - 10.1111/jce.15171
Subject(s) - medicine , artificial intelligence , cardiac resynchronization therapy , machine learning , convolutional neural network , receiver operating characteristic , calculator , ensemble learning , deep learning , waveform , artificial neural network , heart failure , computer science , telecommunications , radar , ejection fraction , operating system
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.