A machine learning–based clinical decision support algorithm for reducing unnecessary coronary angiograms

Schwalm, Jon David; Di, Shuang; Sheth, Tej; Natarajan, Madhu K.; O’Brien, Erin; McCready, Tara; Petch, Jeremy

doi:10.1016/j.cvdhj.2021.12.001

Cited by 9 publications

(5 citation statements)

References 20 publications

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“…Our group has developed such predictive algorithms, and if they are implemented within existing triage pathways, diagnostic efficiencies could be significantly optimized. 20 Third, this study was implemented in 2 tertiary care cardiac centres and led by a novel collaboration between 2 specialist groups. This experience is unique, and this approach may not be possible to implement readily in other centres across Ontario and Canada.…”

Section: Discussionmentioning

confidence: 99%

Centralized Triage of Suspected Coronary Artery Disease Using Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography

et al. 2023

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Section: Discussionmentioning

confidence: 99%

Centralized Triage of Suspected Coronary Artery Disease Using Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography

et al. 2023

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“…ML could enable clinicians to provide better diagnosis, risk stratification, and manage CAD patients by improving CAD prediction accuracy. At present, no ML method focuses on clinical applications and analyzes metabolic markers to forecast obstructive CAD in patients who are undergoing invasive coronary angiography (ICA) [ 51 , 52 ].…”

Section: Reviewmentioning

confidence: 99%

A Systematic Review: Do the Use of Machine Learning, Deep Learning, and Artificial Intelligence Improve Patient Outcomes in Acute Myocardial Ischemia Compared to Clinician-Only Approaches?

Panjiyar,

Davydov,

Nashat

et al. 2023

Cureus

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Cardiovascular diseases (CVDs) present a significant global health challenge and remain a primary cause of death. Early detection and intervention are crucial for improved outcomes in acute coronary syndrome (ACS), particularly acute myocardial infarction (AMI) cases. Artificial intelligence (AI) can detect heart disease early by analyzing patient information and electrocardiogram (ECG) data, providing invaluable insights into this critical health issue. However, the imbalanced nature of ECG and patient data presents challenges for traditional machine learning (ML) algorithms in performing unbiasedly. Investigators have proposed various data-level and algorithm-level solutions to overcome these challenges. In this study, we used a systematic literature review (SLR) approach to give an overview of the current literature and to highlight the difficulties of utilizing ML, deep learning (DL), and AI algorithms in predicting, diagnosing, and prognosis of heart diseases. We reviewed 181 articles from reputable journals published between 2013 and June 15, 2023, focusing on eight selected papers for in-depth analysis. The analysis considered factors such as heart disease type, algorithms used, applications, and proposed solutions and compared the benefits of algorithms combined with clinicians versus clinicians alone. This systematic review revealed that the current ML-based diagnostic approaches face several open problems and issues when implementing ML, DL, and AI in real-life settings. Although these algorithms show higher sensitivities, specificities, and accuracies in detecting heart disease, we must address the ethical concerns while implementing these models into clinical practice. The transparency of how these algorithms operate remains a challenge. Nevertheless, further exploration and research in ML, DL, and AI are necessary to overcome these challenges and fully harness their potential to improve health outcomes for patients with AMI.

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“…A light gradient boosting machine (LightGBM)based CDSS was designed [25], which reduces the patient risk and cost to the medicinal system by enhancing the diagnostic field of invasive coronary angiography via an optimized outpatient choice. But, it needs external validation before being executed clinically.…”

Section: Literature Surveymentioning

confidence: 99%

“…Decision-making assistance unit: The welllearned semi-supervised trainer conducts a categorization process, where an unknown data like a patient is allocated via the tag, which estimates the unknown membership in one of the deliberate labels defining probable prognosis of multiple disorders. , Fuzzy_AHP+ANN [17], PSO-DNN [18], HTM+LSTM [21], DBSCAN+SMOTE-ENN+XGBoost [22] and LightGBM [25] regarding the following metrics:…”

Section: Cdss Based On Delm-gan-based Semisupervised Training Algorithmmentioning

confidence: 99%

Deep Ensemble Learning with GAN-based Semi-Supervised Training Algorithm for Medical Decision Support System in Healthcare Applications

2022

IJIES

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Nowadays, clinical decision support system (CDSS) is emerged broadly due to the growth of smart applications. Yet, labeling a massive amount of clinical data is expensive and needs domain expertise. Also, how to design a robust CDSS model in a healthcare platform with a minimal number of annotated clinical data was an essential problem. To tackle this issue, a generative adversarial network (GAN)-based semi-supervised training model was developed, which increases the quantity of annotated data and solves the unbalanced annotated labels. As well, support vector machine (SVM) and K-nearest neighbor (KNN) classifiers were applied as the base learners to predict the label for unannotated data. But, the result from both base learners was not often similar, which impacts the prediction efficiency. So, this article introduces a deep ensemble learner model (DELM) for predicting labels to the unannotated data by stacking multiple machine learning classifiers as base learners. In this model, two levels of prediction: the initial level and the second level. During the initial level, multiple base learners including SVM, KNN, naive bayes (NB) and random forest (RF) learn the dataset with both the annotated and unannotated samples independently to predict the class of the unannotated instances. Then, those prediction outcomes are merged during the second level, which applies the deep neural network (DNN) as an ensemble classifier to get the final predicted class of the unannotated instance. Finally, the experimental results exhibit that the DELM with GAN achieves 86.54 %, 84.83 % and 86.72 % accuracies on SPECT, WDBC and Hallmarks databases, correspondingly, compared to the Fuzzy-AHP+ANN, HTM+LSTM, DBSCAN+SMOTE-ENN+XGBoost and GAN-based semi-supervised models.

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A machine learning–based clinical decision support algorithm for reducing unnecessary coronary angiograms

Cited by 9 publications

References 20 publications

Centralized Triage of Suspected Coronary Artery Disease Using Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography

Centralized Triage of Suspected Coronary Artery Disease Using Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography

A Systematic Review: Do the Use of Machine Learning, Deep Learning, and Artificial Intelligence Improve Patient Outcomes in Acute Myocardial Ischemia Compared to Clinician-Only Approaches?

Deep Ensemble Learning with GAN-based Semi-Supervised Training Algorithm for Medical Decision Support System in Healthcare Applications

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