scholarly journals Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database (Preprint)

10.2196/27405 ◽  
2021 ◽  
Author(s):  
Arom Choi ◽  
Min Joung Kim ◽  
Ji Min Sung ◽  
Sunhee Kim ◽  
Jayong Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Prediction Models ◽  
Nationwide Database

Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database (Preprint)

2021 ◽  
Author(s):  
Arom Choi ◽  
Min Joung Kim ◽  
Ji Min Sung ◽  
Hyuk-Jae Chang ◽  
Jayong Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Statistical Methods ◽  
Prediction Models ◽  
National Database ◽  
Type Model ◽  
Prehospital Management ◽  
Extreme Gradient Boosting

BACKGROUND Since acute myocardial infarction (AMI) is a leading cause of mortality worldwide, the accurate evaluation of risk factors of AMI at prehospital stage provides appropriate prehospital management and rapid transportation to the most appropriate hospital for treatment. Prediction of AMI derived from national database can accelerate early recognition and timely management to improve the survival rate. OBJECTIVE This study was conducted to develop and compare the efficacy of models for the prediction of AMI at the prehospital stage based on variables identified from a nationwide systematic emergency medical service (EMS) registry using conventional statistical methods and machine learning algorithms. METHODS From among patients transferred from the public EMS to emergency departments in Korea from January 2016 to December 2018, the patients aged >15 years in the EMS cardiovascular registry were enrolled. Two datasets were constructed according to the hierarchical structure of the EMS cardiovascular registry. For each dataset, several predictive models for AMI were derived and compared using conventional statistical methods and machine learning. RESULTS In total, 184,577 patients (Dataset 1) in the EMS cardiovascular registry were included in the final analysis. Among them, 72,439 patients (Dataset 2) were suspected to have AMI at the prehospital stage (as assessed by paramedics). Between the models derived using the conventional logistic regression method, the B-type model incorporated AMI-specific variables from the A-type model, and exhibited a superior discriminative ability (P = 0.02). The models that used extreme gradient boosting and multilayer perceptron yielded a higher predictive performance than the model derived based on conventional logistic regression for all analyses that used both datasets. Each machine learning algorithm yielded different classification lists regarding the 10 most important features. CONCLUSIONS This study demonstrates that prediction models, which use nationwide prehospital data and are developed with appropriate structures, can improve the identification of patients who need timely AMI management.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

P1755Plasma proteomics in healthy individuals predict myocardial infarction

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
R Hoogeveen ◽  
J P Belo Pereira ◽  
V Zampoleri ◽  
M J Bom ◽  
W Koenig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Plasma Proteins ◽  
Risk Model ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Traditional Risk Factors

Abstract Background Currently used models to predict cardiovascular event risk have limited value. It has been shown repetitively that the addition of single biomarkers has modest impact. Recently we observed that a model consisting of a larger array of plasma proteins performed very well in predicting the presence of vulnerable plaques in primary prevention patients. However, the validation of this protein panel in predicting cardiovascular outcomes remains to be established. Purpose This study investigated the ability of a 384 preselected protein biomarkers to predict acute myocardial infarction, using state-of-the-art machine learning techniques. Secondly, we compared the performance of this multi-protein risk model to traditional risk engines. Methods We selected 822 subjects from the EPIC-Norfolk prospective cohort study, of whom 411 suffered a myocardial infarction during follow-up (median 15 years) compared to 411 controls who remained event-free (median follow-up 20 years). The 384 proteins were measured using proximity extension assay technology. Machine learning algorithms (random forests) were used for the prediction of acute myocardial infarction (ICD code I21–22). Performance of the model was tested against and on top of traditional risk factors for cardiovascular disease (refit Framingham). All performance measurements were averaged over several stability selection routines. Results Prediction of myocardial infarction using a machine-learning model consisting of 50 plasma proteins resulted in a ROC AUC of 0.74±0.14, in comparison to 0.69±0.17 using traditional risk factors (refit Framingham. Combining the proteins and refit Framingham resulted in a ROC AUC of 0.74±0.15. Focussing on events occurring within 3 years after baseline blood withdrawal, the ROC AUC increased to 0.80±0.09 using 50 plasma proteins, as opposed to 0.67±0.22 using refit Framingham (figure). Combining the protein model with refit Framingham resulted in a ROC AUC of 0.82±0.11 for these events. Diagnostic performance events <3yrs Conclusion High-throughput proteomics outperforms traditional risk factors in prediction of acute myocardial infarction. Prediction of myocardial infarction occurring within 3 years after inclusion showed highest performance. Availability of affordable proteomic approaches and developed machine learning pave the path for clinical implementation of these models in cardiovascular risk prediction. Acknowledgement/Funding This study was funded by an ERA-CVD grant (JTC2017) and EU Horizon 2020 grant (REPROGRAM, 667837)

scholarly journals Machine-learning to Improve Prediction of Mortality following Acute Myocardial Infarction: An Assessment in the NCDR-Chest Pain-Myocardial Infarction Registry

2019 ◽  
Author(s):  
Rohan Khera ◽  
Julian Haimovich ◽  
Nate Hurley ◽  
Robert McNamara ◽  
John A Spertus ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Chest Pain ◽  
High Risk ◽  
Hospital Mortality ◽  
Gradient Descent ◽  
Machine Learning Methods ◽  
C Statistic

ABSTRACTIntroductionAccurate prediction of risk of death following acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making. Contemporary machine-learning may improve risk-prediction by identifying complex relationships between predictors and outcomes.Methods and ResultsWe studied 993,905 patients in the American College of Cardiology Chest Pain-MI Registry hospitalized with AMI (mean age 64 ± 13 years, 34% women) between January 2011 and December 2016. We developed and validated three machine learning models to predict in-hospital mortality and compared the performance characteristics with a logistic regression model. In an independent validation cohort, we compared logistic regression with lasso regularization (c-statistic, 0.891 [95% CI, 0.890-0.892]), gradient descent boosting (c-statistic, 0.902 [0.901-0.903]), and meta-classification that combined gradient descent boosting with a neural network (c-statistic, 0.904 [0.903-0.905]) with traditional logistic regression (c-statistic, 0.882 [0.881-0.883]). There were improvements in classification of individuals across the spectrum of patient risk with each of the three methods; the meta-classifier model – our best performing model - reclassified 20.9% of individuals deemed high-risk for mortality in logistic regression appropriately as low-to-moderate risk, and 8.2% of deemed low-risk to moderate-to-high risk based consistent with the actual event rates.ConclusionsMachine-learning methods improved the prediction of in-hospital mortality for AMI compared with logistic regression. Machine learning methods enhance the utility of risk models developed using traditional statistical approaches through additional exploration of the relationship between variables and outcomes.

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