P5014The NPAC score for predicting survival after incident acute myocardial infarction

Abstract Background Risk stratification in acute myocardial infarction (AMI) is important for guiding clinical management. Current risk scores are mostly derived from clinical trials with stringent patient selection. We aimed to establish and evaluate a composite scoring system to predict short-term mortality after index episodes of AMI, independent of electrocardiography (ECG) pattern, in a large real-world cohort. Methods Using electronic health records, patients admitted to our regional teaching hospital (derivation cohort, n=2127) and an independent tertiary care center (validation cohort, n=1276) with index acute myocardial infarction between January 2013 and December 2017 as confirmed by principal diagnosis and laboratory findings, were identified retrospectively. Results Univariate logistic regression was used as the primary model to identify potential contributors to mortality. Stepwise forward likelihood ratio logistic regression revealed that neutrophil-to-lymphocyte ratio, peripheral vascular disease, age, and serum creatinine (NPAC) were significant predictors for 90-day mortality (Hosmer-Lemeshow test, P=0.21). Each component of the NPAC score was weighted by beta-coefficients in multivariate analysis. The C-statistic of the NPAC score was 0.75, which was higher than the conventional Charlson's score (C-statistic=0.63). Application of a deep learning model to our dataset improved the accuracy of classification with a C-statistic of 0.81. Multivariate binary logistic regression Variable β Adjusted Odds ratio (95% CI) P-value Points Age ≥65 years 1.304 3.68 (2.63–5.17) <0.001 2 Peripheral vascular disease 1.109 3.03 (1.52–6.04) 0.002 2 NLRt ≥9.51 1.100 2.73 (2.12–3.51) <0.001 1 Creatinine≥109 μmol/L 1.003 3.00 (2.35–3.85) <0.001 2 NPAC deep learning model Conclusions The NPAC score comprised of four items from routine laboratory parameters and basic clinical information and can facilitate early identification of cases at risk of short-term mortality following index myocardial infarction. Deep learning model can serve as a gate-keeper to provide more accurate prediction to facilitate clinical decision making.

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AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666200325104317 ◽

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.

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Very short-term forecasting of wind power generation using hybrid deep learning model

Journal of Cleaner Production ◽

10.1016/j.jclepro.2021.126564 ◽

2021 ◽

Vol 296 ◽

pp. 126564

Author(s):

Md Alamgir Hossain ◽

Ripon K. Chakrabortty ◽

Sondoss Elsawah ◽

Michael J. Ryan

Keyword(s):

Power Generation ◽

Deep Learning ◽

Wind Power ◽

Learning Model ◽

Wind Power Generation ◽

Short Term ◽

Short Term Forecasting ◽

Deep Learning Model

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Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽

10.3390/atmos12070924 ◽

2021 ◽

Vol 12 (7) ◽

pp. 924

Author(s):

Moslem Imani ◽

Hoda Fakour ◽

Wen-Hau Lan ◽

Huan-Chin Kao ◽

Chi Ming Lee ◽

...

Keyword(s):

Deep Learning ◽

Wind Speed ◽

Set Theory ◽

Rough Set ◽

Fuzzy Set Theory ◽

Short Term Memory ◽

Learning Model ◽

Short Term ◽

Long Short Term Memory ◽

Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

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