scholarly journals Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Tatjana Gligorijević ◽  
Zoran Ševarac ◽  
Branislav Milovanović ◽  
Vlado Đajić ◽  
Marija Zdravković ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Baroreflex Sensitivity ◽  
Training Dataset ◽  
Survival Prediction ◽  
Classification Problems ◽  
Risk Of Death ◽  
Increased Risk ◽  
Artificial Neural

Artificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. This technique has been used for prediction and classification problems in many areas of medical signal processing. The aim of this paper was to identify individuals with high risk of death after acute myocardial infarction using ANN. A training dataset for ANN was 1705 consecutive patients who underwent 24-hour ECG monitoring, short ECG analysis, noninvasive beat-to-beat heart-rate variability, and baroreflex sensitivity that were followed for 3 years. The proposed neural network classifier showed good performance for survival prediction: 88% accuracy, 81% sensitivity, 93% specificity, 0.85 F-measure, and area under the curve value of 0.77. These findings support the theory that patients with high sympathetic activity (reduced baroreflex sensitivity) have an increased risk of mortality independent of other risk factors and that artificial neural networks can indicate the individuals with a higher risk.

Acute Myocardial Infarction Detected in the 12-Lead ECG by Artificial Neural Networks

Circulation ◽  
1997 ◽  
Vol 96 (6) ◽  
pp. 1798-1802 ◽  
Author(s):  
Bo Hedén ◽  
Hans Öhlin ◽  
Ralf Rittner ◽  
Lars Edenbrandt
Keyword(s):  
Myocardial Infarction ◽  
Neural Networks ◽  
Acute Myocardial Infarction ◽  
Artificial Neural Networks ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1101
Author(s):  
Elena M. Tosca ◽  
Roberta Bartolucci ◽  
Paolo Magni
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Molecular Descriptor ◽  
Biological Properties ◽  
Training Dataset ◽  
Structure Property ◽  
Feed Forward Neural Network ◽  
Qspr Model ◽  
Artificial Neural ◽  
Intrinsic Solubility

Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure–property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (logS0) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with logS0 value experimentally determined was gathered from the literature. Then, a standard three-layer feed-forward neural network was defined by using 10 ChemGPS physico-chemical descriptors as input features. The developed ANN showed adequate predictive performances on both of the SC-2 test datasets. Benefits and limitations of ML approaches have been highlighted and discussed, starting from this case-study. The main findings confirmed that ML approaches are an attractive and promising tool to predict logS0; however, many aspects, such as data quality, molecular descriptor computation and selection, and assessment of applicability domain, are crucial but often neglected, and should be carefully considered to improve predictions based on ML.

Artificial Neural Networks Applied to Survival Prediction in Breast Cancer

Oncology ◽  
1999 ◽  
Vol 57 (4) ◽  
pp. 281-286 ◽  
Author(s):  
M. Lundin ◽  
J. Lundin ◽  
H.B. Burke ◽  
S. Toikkanen ◽  
L. Pylkkänen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Survival Prediction ◽  
Artificial Neural

scholarly journals PREDICTION OF FINANCIAL DISTRESS: CASE OF MINING ENTERPRISES IN CZECH REPUBLIC

2020 ◽  
Vol 15 (1) ◽  
pp. 1-14
Author(s):  
Zuzana Rowland ◽  
Alla Kasych ◽  
Petr Suler
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Czech Republic ◽  
Financial Stability ◽  
Grey Zone ◽  
Financial Health ◽  
Classification Problems ◽  
Mining Companies ◽  
Artificial Neural

The ability to predict a company's financial health is a challenge for many researchers and scientists. It is also a distracting topic, as many other new approaches to financial health predictions have emerged in recent years. In this paper, we focused on identifying the financial health of mining companies in the Czech Republic. We chose the neural network method because, based on various instances of related research, neural networks represent a more reliable financial forecast than mathematical-statistical methods such as discriminant analysis and logistic regression. The concept of a neural network emerged with the first artificial neural networks, inspired by biological systems. The existence of prediction and classification problems directly predetermines artificial neural networks with respect to a given issue. We used the Amadeus database for processing, including financial indicators, SPSS, and Visual Gene Developer software. In total, we analyzed sixty-four mining companies. Complete data on financial stability were available for fifty-three companies, which we explored, and based on these results, identified financial situations for the other thirteen. Based on the available information, we processed a neural network and regression analysis. We managed to classify thirteen companies as solvent, insolvent, and in the grey zone, with the help of prediction.

The association between reconstructed phase space and Artificial Neural Networks for vectorcardiographic recognition of myocardial infarction

2018 ◽  
Vol 51 (3) ◽  
pp. 443-449 ◽  
Author(s):  
Cecília M. Costa ◽  
Ittalo S. Silva ◽  
Rafael D. de Sousa ◽  
Renato A. Hortegal ◽  
Carlos Danilo M. Regis
Keyword(s):  
Myocardial Infarction ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Phase Space ◽  
Reconstructed Phase Space ◽  
Artificial Neural
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