Progress towards an Intelligent Computer‐Aided Risk Evaluation System

1987 ◽  
Vol 4 (2) ◽  
pp. 27-37 ◽  
Author(s):  
J.P. Stead ◽  
J.E. Strutt ◽  
J. Billingham
Keyword(s):  
Risk Evaluation ◽  
Evaluation System ◽  
Computer Aided ◽  
Risk Evaluation System ◽  
Intelligent Computer

Computer-aided risk evaluation system for capital investment

Omega ◽  
1994 ◽  
Vol 22 (4) ◽  
pp. 391-IN1 ◽  
Author(s):  
P Liang ◽  
F Song
Keyword(s):  
Risk Evaluation ◽  
Evaluation System ◽  
Capital Investment ◽  
Computer Aided ◽  
Risk Evaluation System

IMS risk evaluation system for financing and insuring tunnel projects

2004 ◽  
Vol 19 (4-5) ◽  
pp. 334 ◽  
Author(s):  
I McFeat-Smith ◽  
K.W Harman
Keyword(s):  
Risk Evaluation ◽  
Evaluation System ◽  
Risk Evaluation System

scholarly journals MODEL JARINGAN SARAF TIRUAN UNTUK EVALUASI RESIKO KREDIT

2018 ◽  
Vol 2 (1) ◽  
pp. 76
Author(s):  
Novan Wijaya
Keyword(s):  
Neural Network ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Financial Risk ◽  
Banking Sector ◽  
Major Focus ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Risk Evaluation System

Credit risk evaluation is an importanttopic in financial risk management and become a major focus in the banking sector. This research discusses a credit risk evaluation system using an artificial neural network model based on backpropagation algorithm. This system is to train and test the neural network to determine the predictive value of credit risk, whether high riskorlow risk. This neural network uses 14 input layers, nine hidden layers and an output layer, and the data used comes from the bank that has branches in EastJakarta. The results showed that neural network can be used effectively in the evaluation of credit risk with accuracy of 88% from 100 test data

scholarly journals Validation of the Apnea Risk Evaluation System (ARES) Device Against Laboratory Polysomnography in Pregnant Women at Risk for Obstructive Sleep Apnea Syndrome

2014 ◽  
Vol 10 (05) ◽  
pp. 497-502 ◽  
Author(s):  
Katherine M. Sharkey ◽  
Kelly Waters ◽  
Richard P. Millman ◽  
Robin Moore ◽  
Susan M. Martin ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Pregnant Women ◽  
Obstructive Sleep Apnea Syndrome ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Sleep Apnea Syndrome ◽  
Obstructive Sleep ◽  
Apnea Syndrome ◽  
Risk Evaluation System

Forensic Operationalized Therapy/Risk Evaluation System (FOTRES)

2017 ◽  
pp. 243-253
Author(s):  
Leonel C. Gonçalves ◽  
Astrid Rossegger ◽  
Jérôme Endrass
Keyword(s):  
Risk Evaluation ◽  
Evaluation System ◽  
Risk Evaluation System

KM Risk Evaluation System Based on CBR

2009 ◽  
Author(s):  
Changling Li ◽  
Xinjin Fu ◽  
Xuemei Zhai
Keyword(s):  
Risk Evaluation ◽  
Evaluation System ◽  
Risk Evaluation System

scholarly journals Predictive ability of EuroSCORE II integrating cardiactroponin T in patients undergoing OPCABG

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiang Li ◽  
Lingtong Shan ◽  
Mengwei Lv ◽  
Zhi Li ◽  
Chunyan Han ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Myocardial Injury ◽  
Predictive Ability ◽  
Discrimination Power ◽  
Evaluation Systems ◽  
Risk Evaluation System ◽  
Injury Classification

Abstract Background Preoperative risk evaluation systems are significant and important to the allocation of medical resources and the communication between doctors and patients. The European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) is widely used in clinical practice. Cardiac troponin T (cTnT) can specifically and accurately reflect myocardial injury. Whether EuroSCORE II can improve the predictive power after integrating with cTnT is still unclear. This study was a retrospective single center study designed to assess the predictive ability of EuroSCORE II integrated with cTnT for patients undergoing isolated off-pump coronary artery bypass grafting (OPCABG). Methods This retrospective and observational cohort study included 1887 patients who underwent first isolated OPCABG. cTnT was detected within 48 h before operation in each patient. According to myocardial injury, patients were divided by cTnT into 4 stages. A new risk evaluation system was created through logistic regression with EuroSCORE II and myocardial injury classification as covariates. Then the two risk evaluation systems were comparatively assessed by regression analysis, receiver operator characteristic curves, net reclassification index, Bland–Altman plots and decision curve analysis. Results There were 43 in-hospital deaths, with a mortality of 2.30% (43/1887). The logistic regression analysis showed that preoperative myocardial injury classification was a significant risk factor for in-hospital mortality in both total cohort (OR 1.491, 95%CI 1.049–2.119) and subsets (OR 1.761, 95%CI 1.102–2.814). The new risk evaluation system has higher calibration and discrimination power than EuroSCORE II, both for overall cohort and subsets. Especially, the new system has obvious advantages in discrimination power in the subset of acute myocardial infarction (AUC 0.813 vs. 0.772, 0.906 vs. 0.841, and 0.715 vs. 0.646, respectively). Conclusions Both myocardial injury classification and EuroSCORE II are independent risk factors of in-hospital mortality in OPCABG patients. The new risk evaluation system has higher predictive ability than EuroSCORE II, especially in patients with a recent history of AMI.

Description and Validation of the Apnea Risk Evaluation System

CHEST Journal ◽  
2005 ◽  
Vol 128 (4) ◽  
pp. 2166-2175 ◽  
Author(s):  
Philip R. Westbrook ◽  
Daniel J. Levendowski ◽  
Milenko Cvetinovic ◽  
Timothy Zavora ◽  
Vladislav Velimirovic ◽  
...  
Keyword(s):  
Risk Evaluation ◽  
Evaluation System ◽  
Risk Evaluation System
Sign in / Sign up

Export Citation Format

Share Document