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.

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.

scholarly journals Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1909
Author(s):  
Dougho Park ◽  
Eunhwan Jeong ◽  
Haejong Kim ◽  
Hae Wook Pyun ◽  
Haemin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

scholarly journals Development and Validation of Interpretable Machine Learning for Stroke Occurrence in Older, Community Chinese Dwellers

2021 ◽  
Author(s):  
Yafei Wu ◽  
Zhongquan Jiang ◽  
Shaowu Lin ◽  
Ya Fang
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Logistic Regression ◽  
High Risk ◽  
Prediction Models ◽  
Curve Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Interpretable Machine Learning ◽  
Extreme Gradient Boosting

Abstract Background: Prediction of stroke based on individuals’ risk factors, especially for a first stroke event, is of great significance for primary prevention of high-risk populations. Our study aimed to investigate the applicability of interpretable machine learning for predicting a 2-year stroke occurrence in older adults compared with logistic regression.Methods: A total of 5960 participants consecutively surveyed from July 2011 to August 2013 in the China Health and Retirement Longitudinal Study were included for analysis. We constructed a traditional logistic regression (LR) and two machine learning methods, namely random forest (RF) and extreme gradient boosting (XGBoost), to distinguish stroke occurrence versus non-stroke occurrence using data on demographics, lifestyle, disease history, and clinical variables. Grid search and 10-fold cross validation were used to tune the hyperparameters. Model performance was assessed by discrimination, calibration, decision curve and predictiveness curve analysis.Results: Among the 5960 participants, 131 (2.20%) of them developed stroke after an average of 2-year follow-up. Our prediction models distinguished stroke occurrence versus non-stroke occurrence with excellent performance. The AUCs of machine learning methods (RF, 0.823[95% CI, 0.759-0.886]; XGBoost, 0.808[95% CI, 0.730-0.886]) were significantly higher than LR (0.718[95% CI, 0.649, 0.787], p<0.05). No significant difference was observed between RF and XGBoost (p>0.05). All prediction models had good calibration results, and the brier score were 0.022 (95% CI, 0.015-0.028) in LR, 0.019 (95% CI, 0.014-0.025) in RF, and 0.020 (95% CI, 0.015-0.026) in XGBoost. XGBoost had much higher net benefits within a wider threshold range in terms of decision curve analysis, and more capable of recognizing high risk individuals in terms of predictiveness curve analysis. A total of eight predictors including gender, waist-to-height ratio, dyslipidemia, glycated hemoglobin, white blood cell count, blood glucose, triglycerides, and low-density lipoprotein cholesterol ranked top 5 in three prediction models.Conclusions: Machine learning methods, especially for XGBoost, had the potential to predict stroke occurrence compared with traditional logistic regression in the older adults.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

scholarly journals Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

2021 ◽  
Author(s):  
Kazutaka Uchida ◽  
Junichi Kouno ◽  
Shinichi Yoshimura ◽  
Norito Kinjo ◽  
Fumihiro Sakakibara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Vessel Occlusion ◽  
Predictive Values ◽  
Training Cohort ◽  
Sensitivity Specificity

AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

scholarly journals Prediction of Hourly Effect of Land Use on Crime

2018 ◽  
Vol 8 (1) ◽  
pp. 16 ◽  
Author(s):  
Irina Matijosaitiene ◽  
Peng Zhao ◽  
Sylvain Jaume ◽  
Joseph Gilkey Jr
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Logistic Regression ◽  
Hot Spots ◽  
Urban Areas ◽  
Prediction Models ◽  
Hot Spot ◽  
Time Range ◽  
Land Uses ◽  
Police Departments

Predicting the exact urban places where crime is most likely to occur is one of the greatest interests for Police Departments. Therefore, the goal of the research presented in this paper is to identify specific urban areas where a crime could happen in Manhattan, NY for every hour of a day. The outputs from this research are the following: (i) predicted land uses that generates the top three most committed crimes in Manhattan, by using machine learning (random forest and logistic regression), (ii) identifying the exact hours when most of the assaults are committed, together with hot spots during these hours, by applying time series and hot spot analysis, (iii) built hourly prediction models for assaults based on the land use, by deploying logistic regression. Assault, as a physical attack on someone, according to criminal law, is identified as the third most committed crime in Manhattan. Land use (residential, commercial, recreational, mixed use etc.) is assigned to every area or lot in Manhattan, determining the actual use or activities within each particular lot. While plotting assaults on the map for every hour, this investigation has identified that the hot spots where assaults occur were ‘moving’ and not confined to specific lots within Manhattan. This raises a number of questions: Why are hot spots of assaults not static in an urban environment? What makes them ‘move’—is it a particular urban pattern? Is the ‘movement’ of hot spots related to human activities during the day and night? Answering these questions helps to build the initial frame for assault prediction within every hour of a day. Knowing a specific land use vulnerability to assault during each exact hour can assist the police departments to allocate forces during those hours in risky areas. For the analysis, the study is using two datasets: a crime dataset with geographical locations of crime, date and time, and a geographic dataset about land uses with land use codes for every lot, each obtained from open databases. The study joins two datasets based on the spatial location and classifies data into 24 classes, based on the time range when the assault occurred. Machine learning methods reveal the effect of land uses on larceny, harassment and assault, the three most committed crimes in Manhattan. Finally, logistic regression provides hourly prediction models and unveils the type of land use where assaults could occur during each hour for both day and night.

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