scholarly journals Construction project risk prediction model based on EW-FAHP and one dimensional convolution neural network

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246539
Author(s):  
Yawen Zhong ◽  
Hailing Li ◽  
Leilei Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Risk Prediction ◽  
Construction Project ◽  
Risk Prediction Model ◽  
Convolution Neural Network ◽  
Project Risk ◽  
Construction Period ◽  
One Dimensional ◽  
Model Based

In order to solve the problem of low accuracy of traditional construction project risk prediction, a project risk prediction model based on EW-FAHP and 1D-CNN(One Dimensional Convolution Neural Network) is proposed. Firstly, the risk evaluation index value of construction project is selected by literature analysis method, and the comprehensive weight of risk index is obtained by combining entropy weight method (EW) and fuzzy analytic hierarchy process (FAHP). The risk weight is input into the 1D-CNN model for training and learning, and the prediction values of construction period risk and cost risk are output to realize the risk prediction. The experimental results show that the average absolute error of the construction period risk and cost risk of the risk prediction model proposed in this paper is below 0.1%, which can meet the risk prediction of construction projects with high accuracy.

Prediction of ventricular arrhythmias in patients with a spontaneous Brugada type 1 pattern: the key is in the electrocardiogram

EP Europace ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 1400-1409 ◽  
Author(s):  
Antoine Delinière ◽  
Adrian Baranchuk ◽  
Joris Giai ◽  
Francis Bessiere ◽  
Delphine Maucort-Boulch ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Univariate Analysis ◽  
Sudden Cardiac Arrest ◽  
International Study ◽  
Risk Prediction Model ◽  
Adult Patients ◽  
Penalized Spline ◽  
Model Based

Abstract Aims There is currently no reliable tool to quantify the risks of ventricular fibrillation or sudden cardiac arrest (VF/SCA) in patients with spontaneous Brugada type 1 pattern (BrT1). Previous studies showed that electrocardiographic (ECG) markers of depolarization or repolarization disorders might indicate elevated risk. We aimed to design a VF/SCA risk prediction model based on ECG analyses for adult patients with spontaneous BrT1. Methods and results This retrospective multicentre international study analysed ECG data from 115 patients (mean age 45.1 ± 12.8 years, 105 males) with spontaneous BrT1. Of these, 45 patients had experienced VF/SCA and 70 patients did not experience VF/SCA. Among 10 ECG markers, a univariate analysis showed significant associations between VF/SCA and maximum corrected Tpeak–Tend intervals ≥100 ms in precordial leads (LMaxTpec) (P < 0.001), BrT1 in a peripheral lead (pT1) (P = 0.004), early repolarization in inferolateral leads (ER) (P < 0.001), and QRS duration ≥120 ms in lead V2 (P = 0.002). The Cox multivariate analysis revealed four predictors of VF/SCA: the LMaxTpec [hazard ratio (HR) 8.3, 95% confidence interval (CI) 2.4–28.5; P < 0.001], LMaxTpec + ER (HR 14.9, 95% CI 4.2–53.1; P < 0.001), LMaxTpec + pT1 (HR 17.2, 95% CI 4.1–72; P < 0.001), and LMaxTpec + pT1 + ER (HR 23.5, 95% CI 6–93; P < 0.001). Our multidimensional penalized spline model predicted the 1-year risk of VF/SCA, based on age and these markers. Conclusion LMaxTpec and its association with pT1 and/or ER indicated elevated VF/SCA risk in adult patients with spontaneous BrT1. We successfully developed a simple risk prediction model based on age and these ECG markers.

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