Machine Learning and the Future of Cardiovascular Care

Quer, Giorgio; Arnaout, Ramy; Henne, Michael; Arnaout, Rima

doi:10.1016/j.jacc.2020.11.030

Cited by 233 publications

(153 citation statements)

References 68 publications

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“…Machine learning (ML), the use of mathematical algorithms that address the higher dimensional, nonlinear relationships among many variables, is making significant progress. 8 , 9 , 10 Promising tools for ML in cardiology include the improvement of the automated risk prediction and interpretation of medical imaging that can have a dramatic impact on the practice of cardiology. Currently, several studies have shown that ML outperforms the risk prediction as compared to the traditional logistic models.…”

Section: Introductionmentioning

confidence: 99%

Comparison among random forest, logistic regression, and existing clinical risk scores for predicting outcomes in patients with atrial fibrillation: A report from the J‐RHYTHM registry

Watanabe

Noyama

Kiyono

et al. 2021

Clinical Cardiology

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Background Machine learning (ML) has emerged as a promising tool for risk stratification. However, few studies have applied ML to risk assessment of patients with atrial fibrillation (AF). Hypothesis We aimed to compare the performance of random forest (RF), logistic regression (LR), and conventional risk schemes in predicting the outcomes of AF. Methods We analyzed data from 7406 nonvalvular AF patients (median age 71 years, female 29.2%) enrolled in a nationwide AF registry (J‐RHYTHM Registry) and who were followed for 2 years. The endpoints were thromboembolisms, major bleeding, and all‐cause mortality. Models were generated from potential predictors using an RF model, stepwise LR model, and the thromboembolism (CHADS2 and CHA2DS2‐VASc) and major bleeding (HAS‐BLED, ORBIT, and ATRIA) scores. Results For thromboembolisms, the C‐statistic of the RF model was significantly higher than that of the LR model (0.66 vs. 0.59, p = .03) or CHA2DS2‐VASc score (0.61, p < .01). For major bleeding, the C‐statistic of RF was comparable to the LR (0.69 vs. 0.66, p = .07) and outperformed the HAS‐BLED (0.61, p < .01) and ATRIA (0.62, p < .01) but not the ORBIT (0.67, p = .07). The C‐statistic of RF for all‐cause mortality was comparable to the LR (0.78 vs. 0.79, p = .21). The calibration plot for the RF model was more aligned with the observed events for major bleeding and all‐cause mortality. Conclusions The RF model performed as well as or better than the LR model or existing clinical risk scores for predicting clinical outcomes of AF.

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Section: Introductionmentioning

confidence: 99%

Comparison among random forest, logistic regression, and existing clinical risk scores for predicting outcomes in patients with atrial fibrillation: A report from the J‐RHYTHM registry

Watanabe

Noyama

Kiyono

et al. 2021

Clinical Cardiology

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“…Scientific studies employing machine learning (ML) are becoming increasingly common in cardiology. [1][2][3] And while most FDA and Health Canada approved ML algorithms are focused on the brain, lung or breast, as of the time of writing there are at least 12 approved algorithms with application in cardiology. 4 Yet despite this proliferation, many clinicians remain wary of ML due to concerns about the "black box" nature of many ML models that date back to some of the early applications of artificial neural networks to medicine in the 1990s.…”

Section: Introductionmentioning

confidence: 99%

“…This figure illustrates the marginal effect of age and cholesterol on the probability of having heart disease in a random forest model. We have made several annotations to acclimatize readers who are new to this type of plot: the yellow line(1) shows the non-linear effect of age on the probability of developing heart disease, in the subgroup of patients with high total cholesterol level (>320); the red circle(2) indicates older patients with high cholesterol levels have a relatively higher chance of developing heart disease; and the green circle (3) illustrates that on average, young patients with low cholesterol levels are less likely to develop heart disease.…”

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confidence: 99%

Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology

Petch

Nelson

2022

Canadian Journal of Cardiology

261

109

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…Artificial intelligence has recently shown great potential in various medical fields [ 8 , 9 ]. Machine learning, a subset of artificial intelligence, outperforms other technologies in developing predictive models [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning for Predicting Anastomotic Leakage in Patients with Gastric Adenocarcinoma Who Received Total or Proximal Gastrectomy

Liu

Zhao

et al. 2021

JPM

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Anastomotic leakage is a life-threatening complication in patients with gastric adenocarcinoma who received total or proximal gastrectomy, and there is still no model accurately predicting anastomotic leakage. In this study, we aim to develop a high-performance machine learning tool to predict anastomotic leakage in patients with gastric adenocarcinoma received total or proximal gastrectomy. A total of 1660 cases of gastric adenocarcinoma patients who received total or proximal gastrectomy in a large academic hospital from 1 January 2010 to 31 December 2019 were investigated, and these patients were randomly divided into training and testing sets at a ratio of 8:2. Four machine learning models, such as logistic regression, random forest, support vector machine, and XGBoost, were employed, and 24 clinical preoperative and intraoperative variables were included to develop the predictive model. Regarding the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, random forest had a favorable performance with an AUC of 0.89, a sensitivity of 81.8% and specificity of 82.2% in the testing set. Moreover, we built a web app based on random forest model to achieve real-time predictions for guiding surgeons’ intraoperative decision making.

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Machine Learning and the Future of Cardiovascular Care

Cited by 233 publications

References 68 publications

Comparison among random forest, logistic regression, and existing clinical risk scores for predicting outcomes in patients with atrial fibrillation: A report from the J‐RHYTHM registry

Comparison among random forest, logistic regression, and existing clinical risk scores for predicting outcomes in patients with atrial fibrillation: A report from the J‐RHYTHM registry

Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology

Application of Machine Learning for Predicting Anastomotic Leakage in Patients with Gastric Adenocarcinoma Who Received Total or Proximal Gastrectomy

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