An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain

Wu, Chieh-Chen; Hsu, Wen-Ding; Islam, Mohaimenul; Poly, Tahmina Nasrin; Yang, Hsuan-Chia; Nguyen, Phung‐Anh; Wang, Yao Chin; Li, Yu-Chuan

doi:10.1016/j.cmpb.2019.01.013

Cited by 48 publications

(32 citation statements)

References 19 publications

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“…Although most of these ML classifiers report accuracy that ranges from 93.5 to 98.8%, the generalizability of such models to real-world clinical settings remain questionable, and it is likely these algorithm were overfitting the data contained in the PTB dataset On the other hand, there were few studies that used clinical datasets to build ML classifiers. However, most of these studies combined classical ECG features (e.g., diagnostic ST-T amplitude changes) with a full range of other clinical data elements (e.g., patient history, physical exam abnormalities, laboratory values, and/or diagnostic tests) 10,15,17,35 . Despite the high accuracy achieved by these models (≥0.90), classifiers that incorporate such extensive findings from patient clinical profiles have limited utility during early patient triage decisions.…”

Section: Discussionmentioning

confidence: 99%

“…Analysis of the high-dimensional, highly correlated ECG features requires sophisticated machine learning (ML) classifiers. A number of ML classifiers to predict ACS using ECG data have been reported in the literature [10][11][12][13][14][15][16][17][18] . However, most studies either used small and limited public datasets (e.g., MIT-BIH, Physikalisch-Technische Bundesanstalt (PTB), etc.)…”

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confidence: 99%

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Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

et al. 2020

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Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

et al. 2020

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“…Their prediction model showed higher accuracy (i.e., 92.86%) for identifying NSTEMI patients. 24 Additionally, the sensitivity, specificity, positive predictive value, and negative predictive value of their model were 90.91, 93.33, 76.92, and 97.67%, respectively.…”

Section: Ai Techniques and ML Algorithms For Prediction Of Myocardialmentioning

confidence: 95%

“…[24][25][26][27][28][29][30][31][32] Recently, a group developed an ANN model to predict non-ST elevation myocardial infarction (NSTEMI) patients. 24 The model was trained for several risk attributes such as cardiac risk factor, systolic blood pressure, hemoglobin, corrected QT interval (QTc), PR interval, aspartate aminotransferase, alanine aminotransferase, and cardiac troponin that are independently associated with stable NSTEMI. Their prediction model showed higher accuracy (i.e., 92.86%) for identifying NSTEMI patients.…”

Section: Ai Techniques and ML Algorithms For Prediction Of Myocardialmentioning

confidence: 99%

Using Artificial Intelligence to Manage Thrombosis Research, Diagnosis, and Clinical Management

Mishra

Ashraf

2019

Semin Thromb Hemost

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Thrombosis development in either arterial or venous system remains a major cause of death and disability worldwide. This poorly controlled in vivo clotting could result in many severe complications including myocardial infarction, venous thromboembolism, stroke, and cerebral venous thrombosis, to name a few. These conditions are collectively known as thromboembolic disorders (TEDs). Appropriate understanding of TEDs is challenging, as they are multifactorial and involve several and often different risk factors. Hence, it requires a collective effort and data from numerous research studies to fully comprehend molecular mechanisms for prediction, prevention, treatment, and overall management of these conditions. To accomplish this arduous feat, a comprehensive approach is required that can compile thousands of available experimental data and transform these into more applicable and purposeful findings. Thus, large datasets could be utilized to generate models that could be predictive of how an individual would respond when subjected to any kind of additional risk factors or surgery, hospitalization, etc., or in the presence of some susceptible genetic variations. Artificial intelligence-based methods harness the capabilities of computer software to imitate human behaviors such as language translation, visual perception, and, most importantly, decision making. These emerging tools, if appropriately explored, might assist in processing of large data and tackle the complexities of identifying novel or interesting pathways that could otherwise be hidden due to their enormity. This narrative review attempts to compile the applications of various subfields of artificial intelligence and machine learning in the context of thrombosis research to date. It further reflects on the potential of artificial intelligence in transforming enormous research data into translational application in the form of predictive computational models.

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“…ECG and serum cardiac biomarkers are well-known predictors for diagnosing AIHD [54,55]. Both variables can achieve a high level of performance, and prediction models and PLOS ONE stratification tools tend to utilize these variables [10,56].…”

Section: Plos Onementioning

confidence: 99%

Emergency department routine data and the diagnosis of acute ischemic heart disease in patients with atypical chest pain

Kim

Park

et al. 2020

PLoS ONE

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Background Due to an aging population and the increasing proportion of patients with various comorbidities, the number of patients with acute ischemic heart disease (AIHD) who present to the emergency department (ED) with atypical chest pain is increasing. The aim of this study was to develop and validate a prediction model for AIHD in patients with atypical chest pain. Methods and results A chest pain workup registry, ED administrative database, and clinical data warehouse database were analyzed and integrated by using nonidentifiable key factors to create a comprehensive clinical dataset in a single academic ED from 2014 to 2018. Demographic findings, vital signs, and routine laboratory test results were assessed for their ability to predict AIHD. An extreme gradient boosting (XGB) model was developed and evaluated, and its performance was compared to that of a single-variable model and logistic regression model. The area under the receiver operating characteristic curve (AUROC) was calculated to assess discrimination. A calibration plot and partial dependence plots were also used in the analyses. Overall, 4,978 patients were analyzed. Of the 3,833 patients in the training cohort, 453 (11.8%) had AIHD; of the 1,145 patients in the validation cohort, 166 (14.5%) had AIHD. XGB, troponin (single-variable), and logistic regression models showed similar discrimination power (AUROC [95% confidence interval]: XGB model, 0.75 [0.71–0.79]; troponin model, 0.73 [0.69–0.77]; logistic regression model, 0.73 [0.70–0.79]). Most patients were classified as non-AIHD; calibration was good in patients with a low predicted probability of AIHD in all prediction models. Unlike in the logistic regression model, a nonlinear relationship-like threshold and U-shaped relationship between variables and the probability of AIHD were revealed in the XGB model. Conclusion We developed and validated an AIHD prediction model for patients with atypical chest pain by using an XGB model.

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An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain

Cited by 48 publications

References 19 publications

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

Using Artificial Intelligence to Manage Thrombosis Research, Diagnosis, and Clinical Management

Emergency department routine data and the diagnosis of acute ischemic heart disease in patients with atypical chest pain

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