scholarly journals Identification of risk factors for mortality associated with COVID-19

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9885
Author(s):  
Yuetian Yu ◽  
Cheng Zhu ◽  
Luyu Yang ◽  
Hui Dong ◽  
Ruilan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Hospital Admission ◽  
Regression Models ◽  
Troponin I ◽  
Statistical Significance ◽  
Network Models ◽  
Medical Decision ◽  
Neural Network Models ◽  
Variable Model

Objectives Coronavirus Disease 2019 (COVID-19) has become a pandemic outbreak. Risk stratification at hospital admission is of vital importance for medical decision making and resource allocation. There is no sophisticated tool for this purpose. This study aimed to develop neural network models with predictors selected by genetic algorithms (GA). Methods This study was conducted in Wuhan Third Hospital from January 2020 to March 2020. Predictors were collected on day 1 of hospital admission. The primary outcome was the vital status at hospital discharge. Predictors were selected by using GA, and neural network models were built with the cross-validation method. The final neural network models were compared with conventional logistic regression models. Results A total of 246 patients with COVID-19 were included for analysis. The mortality rate was 17.1% (42/246). Non-survivors were significantly older (median (IQR): 69 (57, 77) vs. 55 (41, 63) years; p < 0.001), had higher high-sensitive troponin I (0.03 (0, 0.06) vs. 0 (0, 0.01) ng/L; p < 0.001), C-reactive protein (85.75 (57.39, 164.65) vs. 23.49 (10.1, 53.59) mg/L; p < 0.001), D-dimer (0.99 (0.44, 2.96) vs. 0.52 (0.26, 0.96) mg/L; p < 0.001), and α-hydroxybutyrate dehydrogenase (306.5 (268.75, 377.25) vs. 194.5 (160.75, 247.5); p < 0.001) and a lower level of lymphocyte count (0.74 (0.41, 0.96) vs. 0.98 (0.77, 1.26) × 109/L; p < 0.001) than survivors. The GA identified a 9-variable (NNet1) and a 32-variable model (NNet2). The NNet1 model was parsimonious with a cost on accuracy; the NNet2 model had the maximum accuracy. NNet1 (AUC: 0.806; 95% CI [0.693–0.919]) and NNet2 (AUC: 0.922; 95% CI [0.859–0.985]) outperformed the linear regression models. Conclusions Our study included a cohort of COVID-19 patients. Several risk factors were identified considering both clinical and statistical significance. We further developed two neural network models, with the variables selected by using GA. The model performs much better than the conventional generalized linear models.

Artificial neural network and regression models for prediction of sewing thread consumption for multilayered fabric assembly at lockstitch 301 seam

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Md Vaseem Chavhan ◽  
M. Ramesh Naidu ◽  
Hayavadana Jamakhandi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Regression Models ◽  
Network Models ◽  
Quadratic Regression ◽  
Neural Network Models ◽  
Content Type ◽  
Sewing Thread ◽  
Artificial Neural

Purpose This paper aims to propose the artificial neural network (ANN) and regression models for the estimation of the thread consumption at multilayered seam assembly stitched with lock stitch 301. Design/methodology/approach In the present study, the generalized regression and neural network models are developed by considering the fabric types: woven, nonwoven and multilayer combination thereof, with basic sewing parameters: sewing thread linear density, stitch density, needle count and fabric assembly thickness. The network with feed-forward backpropagation is considered to build the ANN, and the training function trainlm of MATLAB software is used to adjust weight and basic values according to the optimization of Levenberg Marquardt. The performance of networks measured in terms of the mean squared error and the layer output is set according to the sigmoid transfer function. Findings The proposed ANN and regression model are able to predict the thread consumption with more accuracy for multilayered seam assembly. The predictability of thread consumption from available geometrical models, regression models and industrial empirical techniques are compared with proposed linear regression, quadratic regression and neural network models. The proposed quadratic regression model showed a good correlation with practical thread consumption value and more accuracy in prediction with an overall 4.3% error, as compared to other techniques for given multilayer substrates. Further, the developed ANN network showed good accuracy in the prediction of thread consumption. Originality/value The estimation of thread consumed while stitching is the prerequisite of the garment industry for inventory management especially with the introduction of the costly high-performance sewing thread. In practice, different types of fabrics are stitched at multilayer combinations at different locations of the stitched product. The ANN and regression models are developed for multilayered seam assembly of woven and nonwoven fabric blend composition for better prediction of thread consumption.

Prediction of Hardness Distribution in Plasma Arc Surface Hardening Using Neural Network

2017 ◽  
pp. 318-333
Author(s):  
Manoj Kumar
Keyword(s):  
Neural Network ◽  
Surface Hardening ◽  
Regression Models ◽  
Network Models ◽  
Hardness Distribution ◽  
Plasma Arc ◽  
Statistical Regression ◽  
Neural Network Models ◽  
The Neural Network ◽  
Trained Neural Network

In this chapter, an attempt has been made to develop neural network models to predict the hardness distribution of hardened zone in plasma arc surface hardening process. The back propagation method with the Levenberg-Marquardt algorithm was used to train the neural network models. Hardness distributions were collected by the experimental setup in the laboratory and the associated data were used to train the neural network models. Furthermore, the prediction of neural network models were compared with those obtained from a statistical regression models. It is confirmed experimentally that the hardness distribution can be accurately predicted by the trained neural network models. The accuracy of hardness distribution prediction using neural network is superior to that using other statistical regression models.

scholarly journals A comparison of artificial neural network models and regression models to predict tree volumes for crimean black pine trees in Cankiri forests

2019 ◽  
Vol 143 (9-10) ◽  
pp. 423-423
Author(s):  
Muammer Şenyurt ◽  
Ilker Ercanli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Basis Function ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Network Models ◽  
Black Pine ◽  
Neural Network Models ◽  
Pine Trees ◽  
Artificial Neural Network Models

Cilj ovog rada je usporediti modele umjetne neuralne mreže (ANN) za predviđanje pojedinih drvnih volumena krimskih borova u šumama Çankirija. Jednoulazne i dvoulazne jednadžbe i kompatibilna volumna jednadžba Fang et al. (2000) temeljena na klasičnim i tradicionalnim metodama primijenjena je na 360 krimskih borova u cilju dobivanja ovih drvnih volumena. Kako bi se odredila najbolja alternativna metoda za predviđanje ANN modela, ukupno je obučeno 320 treniranih mreža u višeslojnom perceptronu (MLP) i ukupno 20 treniranih mreža u arhitekturi Radial Basis Function (RBF). Na temelju statistike goodness-of-fit, ANN u smislu MLP 1-9-1 uključujući dbh kao input varijablu za jednoulazna volumna predviđanja pokazao je bolju fitting sposobnost sa SSE (2.7763), Radj2 (0.9339), MSE (0.00910), RMSE (0.0954), AIC (-823.25) i SBC (-1421.81) nego onaj u ostalim proučavanim volumnim metodama koje uključuju dbh kao eksplanatornu varijablu. Za dvoulazna volumna predviđanja, što uključuju dbh i ukupnu visinu kao input varijable, ANN temeljen na MLP 2-15-1 rezultirao je boljom fitting statistikom sa SSE (0.8354), Radj2 (0.9801), MSE (0.00274), RMSE (0.0523), AIC (-579.55) and SBC (-1788.11).

Prediction of pressure fluctuations on sloping stilling basins

2006 ◽  
Vol 33 (11) ◽  
pp. 1379-1388 ◽  
Author(s):  
A Güven ◽  
M Günal ◽  
A Çevik
Keyword(s):  
Neural Network ◽  
Pressure Fluctuation ◽  
Hydraulic Jump ◽  
Regression Models ◽  
Pressure Fluctuations ◽  
Network Models ◽  
Neural Network Models ◽  
Mean Pressure ◽  
Stilling Basins ◽  
The Mean

Various types of hydraulic jump occurring on horizontal and sloping channels have been analyzed experimentally, theoretically, and numerically and the results are available in the literature. In this study, artificial neural network models were developed to simulate the mean pressure fluctuations beneath a hydraulic jump occurring on sloping stilling basins. Multilayers feed a forward neural network with a back-propagation learning algorithm to model the pressure fluctuations beneath such a type of hydraulic jump (B-jump). An explicit formula that predicts the mean pressure fluctuation in terms of the characteristics that contribute most to the hydraulic jump occurring on the sloping basins is presented. The proposed neural network models are compared with linear and nonlinear regression models that were developed using considered physical parameters. The results of the neural network modelling are found to be superior to the regression models and are in good agreement with the experimental results due to relatively small values of error (mean absolute percentage error).Key words: neural networks, pressure fluctuation, hydraulic jump, sloping stilling basin, explicit NN formulation, regression analysis.

scholarly journals Artificial Neural Network Models for Material Classification by Photon Scattering Analysis

2020 ◽  
Vol 9 (03) ◽  
pp. 01-04
Author(s):  
Dr. Naveen Jain
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Artificial Neural Network ◽  
Network Models ◽  
Photon Scattering ◽  
Neural Network Models ◽  
Material Classification ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
The Impact

This article explains the risk factors involved in a business. In each type of business, there are certain risk factors for the implementation of anything in the business. The type of risks involved can depend upon many factors. It also depends on the type of business an organisation is doing. But it is very important that the risk analyst does all the analysis of the risks that might arise in future and must take necessary actions in order to avoid those risks. The risk analyst can also try to reduce the impact of the risks on the business. Therefore, it is very important that the risk analyst should have the knowledge of how to analyse risk and then can act upon them.

scholarly journals Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City

2018 ◽  
Vol 20 (2) ◽  
pp. 281-290 ◽  
Keyword(s):  
Neural Network ◽  
Air Pollutants ◽  
Regression Models ◽  
Network Models ◽  
Linear Regression Models ◽  
Neural Network Models ◽  
Prediction Ability ◽  
Topographic Features ◽  
Ann Models

In this study, potential of neural network to estimate daily mean PM10 concentration levels in Sakarya city, Turkey as a case study was examined to achieve improved prediction ability. The level and distribution of air pollutants in a particular region is associated with changes in meteorological conditions affecting air movements and topographic features. Thus, meteorological variables data for a two-year period for Sakarya city which is located in most industrialized and crowded part of Turkey were selected as input. Neural network models and multiple linear regression models have been statistically evaluated. The results of the study showed that ANN models were accurate enough for prediction of PM10 levels

scholarly journals Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

10.2196/16374 ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. e16374
Author(s):  
Subendhu Rongali ◽  
Adam J Rose ◽  
David D McManus ◽  
Adarsha S Bajracharya ◽  
Alok Kapoor ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Intensive Care ◽  
Logistic Regression Model ◽  
Outcome Prediction ◽  
Network Models ◽  
Neural Network Models ◽  
Latent Space ◽  
Different Types

Background Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications). Objective This study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient’s mortality using their longitudinal EHR data. Methods We built a set of neural network models that we collectively called as long short-term memory (LSTM) outcome prediction using comprehensive feature relations or in short, CLOUT. Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinical features during a patient’s encounter and integrate the latent representation into an LSTM-based predictive model framework. In addition, we designed an ablation experiment to identify risk factors from our CLOUT models. Using physicians’ input as the gold standard, we compared the risk factors identified by both CLOUT and logistic regression models. Results Experiments on the Medical Information Mart for Intensive Care-III dataset (selected patient population: 7537) show that CLOUT (area under the receiver operating characteristic curve=0.89) has surpassed logistic regression (0.82) and other baseline NN models (<0.86). In addition, physicians’ agreement with the CLOUT-derived risk factor rankings was statistically significantly higher than the agreement with the logistic regression model. Conclusions Our results support the applicability of CLOUT for real-world clinical use in identifying patients at high risk of mortality.

Sign in / Sign up

Export Citation Format

Share Document