NARX Neural Networks Model for Forecasting Daily Patient Arrivals in the Emergency Department

2020 ◽  
pp. 1-18
Author(s):  
Melih Yucesan ◽  
Suleyman Mete ◽  
Faruk Serin ◽  
Erkan Celik ◽  
Muhammet Gul
Keyword(s):  
Emergency Department ◽  
Network Architecture ◽  
Waiting Times ◽  
Bayesian Regularization ◽  
Mean Square ◽  
Data Set ◽  
Levenberg Marquardt ◽  
Narx Models ◽  
Nonlinear Autoregressive ◽  
Narx Model

Regarding measuring of service quality at the emergency departments (ED), essential parameters are length of stay (LOS) and waiting times. Patient arrivals, which is related to LOS and waiting times, is hard to forecast and is affected by many parameters. Therefore, authors employed a Nonlinear Autoregressive Exogenous (NARX) model for forecasting of ED arrivals. NARX models are used extensively in many applications that show non-linear and dynamic behavior, but as far as authors know, the NARX method has not yet been used in the forecast of ED arrivals before. In this study, calendar and climatic variables are defined as input parameters. Patient Arrivals is defined as output parameter. A commercial software, MATLAB, was used to train and test the data set. To find the best network architecture Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms, different lags, and number of neurons were tested. R-squared and mean square error (MSE) are used to evaluate the accuracy of the tested networks.

Evaluation of Kriging-NARX Modeling for Uncertainty Quantification of Nonlinear SDOF Systems with Degradation

2021 ◽  
pp. 2150060
Author(s):  
Xiaoshu Gao ◽  
Hetao Hou ◽  
Liang Huang ◽  
Guangquan Yu ◽  
Cheng Chen
Keyword(s):  
Uncertainty Quantification ◽  
Performance Indicator ◽  
Kriging Model ◽  
Least Angle Regression ◽  
Ground Motion Model ◽  
Narx Models ◽  
Form Model ◽  
Input Form ◽  
Nonlinear Autoregressive ◽  
Narx Model

Structural assessment for collapse is commonly approached by observing the failure or collapse of systems fully incorporating degradation. Challenges however exist in the performance indicator or damage measure due to compound impacts of uncertainties of external (seismic excitation) and internal (structural properties) characteristics with degradation behavior. To account for the impacts of uncertainties, the state-of-the-art kriging nonlinear autoregressive with exogenous (NARX) model is explored in this study to replicate the response of nonlinear single-degree-of-freedom systems. The generalized hysteretic Bouc-Wen model with internal uncertainties is selected to emulate the stiffness and strength degradation. A probabilistic stochastic ground motion model is introduced to represent the external uncertainties. The global terms of NARX model are selected by least-angle regression algorithm and the kriging model is utilized to surrogate uncertain parameters into corresponding NARX model coefficients. The predictions of kriging NARX models are further compared with that of the polynomial chaos nonlinear autoregressive with exogenous input form model as well as Monte Carlo simulation. The comparisons show that kriging NARX model presents an effective and efficient meta-model technique for uncertainty quantification of systems with degradation.

Application of nonlinear autoregressive neural network to estimation of global solar radiation over Nigeria

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Olusola Samuel Ojo ◽  
Babatunde Adeyemi
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Surface Data ◽  
Narx Models ◽  
Sahel Region ◽  
Nonlinear Autoregressive ◽  
Narx Model ◽  
Mlr Model ◽  
Autoregressive Neural Network

In this paper, surface data meteorological were used as input variables to create, train and validate the network in which global solar radiation serves as a target. These surface data were obtained from the archives of the European centre for Medium-Range weather forecast for a span of 36 years (1980-2015) over Nigeria. The research aims to evaluate the predictive ability of the nonlinear autoregressive neural network with exogenous input (NARX) model compared with the multivariate linear regression (MLR) model using the statistical metrics. Model selection analysis using the index of agreement (dr) metric showed that the MLR and NARX models have values of 0.710 and 0.853 in the Sahel, 0.748 and 0.849 in the Guinea Savannah, 0.664 and 0.791 in the Derived Savannah, 0.634 and 0.824 in the Coastal regions, and 0.771 and 0.806 in entire Nigeria respectively. Meanwhile, error analyses of the models using root mean square errors (RMSE) showed the values of 1.720 W/m2 and 1.417 in the Sahel region, 2.329 W/m2 and 1.985 W/m2 in the Guinea Savannah region, 2.459 W/m2 and 2.272 W/m2 in the Derived Savannah region, 2.397 W/m2 and 2.261 W/m2 in the Coastal region and 1.691 W/m2 and 1.600 W/m2 in entire Nigeria for MLR and NARX models respectively. These showed that the NARX model has higher dr values and lower RMSE values over all the climatic regions and entire Nigeria than the MLR model. Finally, it can be inferred from these metrics that the NARX model gives a better prediction of global solar radiation than the traditional common MLR models in all the zones in Nigeria.

scholarly journals A Time Series Prediction Model of Foundation Pit Deformation Based on Empirical Wavelet Transform and NARX Network

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1535
Author(s):  
Qingwen Ma ◽  
Sihan Liu ◽  
Xinyu Fan ◽  
Chen Chai ◽  
Yangyang Wang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Prediction Model ◽  
Mean Square Error ◽  
External Input ◽  
Mean Square ◽  
Foundation Pit ◽  
Empirical Wavelet Transform ◽  
The Mean ◽  
Nonlinear Autoregressive ◽  
Narx Model

Large deep foundation pits are usually in a complex environment, so their surface deformation tends to show a stable rising trend with a small range of fluctuation, which brings certain difficulty to the prediction work. Therefore, in this study we proposed a nonlinear autoregressive exogenous (NARX) prediction method based on empirical wavelet transform (EWT) pretreatment is proposed for this feature. Firstly, EWT is used to conduct adaptive decomposition of the measured deformation data and extract the modal signal components with characteristic differences. Secondly, the main components affecting the deformation of the foundation pit are analyzed as a part of the external input. Then, we established a NARX prediction model for different components. Finally, all predicted values are superpositioned to obtain a total value, and the result is compared with the predicted results of the nonlinear autoregressive (NAR) model, empirical mode decomposition-nonlinear autoregressive (EMD-NAR) model, EWT-NAR model, NARX model, EMD-NARX model and EWT-NARX model. The results showed that, compared with the EWT-NAR and EWT-NARX models, the EWT-NARX model reduced the mean square error of KD25 by 91.35%, indicating that the feature of introducing external input makes NARX more suitable for combining with the EWT method. Meanwhile, compared with the EMD-NAR and EWT-NAR models, the introduction of the NARX model reduced the mean square error of KD25 by 78.58% and 95.71%, indicating that EWT had better modal decomposition capability than EMD.

scholarly journals Performance Analysis of various Neural Network functions for Parkinson’s disease Classification using EEG and EMG

2019 ◽  
Vol 9 (1) ◽  
pp. 3402-3406 ◽  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Mean Square Error ◽  
Mean Square ◽  
Data Set ◽  
Elapsed Time ◽  
Levenberg Marquardt ◽  
R Value

Artificial neural network (ANN) is a significant tool for classification of various types of disease using either Biosignals/images or may be any kind of physical parameters. Establishment of appropriate combination of learning, transfer function and training function is a very tedious task. Here, we compared the performance of different training parameters in feed forward neural network for differentiating of Parkinson’s disease using human brain (Electroencephalogram) and muscle signals (Electromyogram) features as the input vector. 3 different types of training algorithm with six training functions is used. They are Gradient Descent algorithms (traingd, traingdm), Conjugate Gradient algorithms (trainscg, traincgp) and Quasi-Newton algorithms (trainbfg, trainlm). Proposed work compared the mentioned algorithm in terms of mean square error, classification rate (%),R-value and the elapsed time. Study showed that trainlm (Levenberg-Marquardt) best fits for larger data set. It showed the highest accuracy rate of 100% with 0 mismatch classification with a best validation mean square error of 0.0040254 in 3 epochs with a elapsed time of 1.12 seconds. The R-value found was 0.9998 which is in nearly equals to 1. Hence, Levenberg-Marquardt can be used as a training function for the classification of any brain disorder

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals Association of Emergency Department Waiting Times With Patient Experience in Admitted and Discharged Patients

2021 ◽  
Vol 8 ◽  
pp. 237437352110114
Author(s):  
Andrew Nyce ◽  
Snehal Gandhi ◽  
Brian Freeze ◽  
Joshua Bosire ◽  
Terry Ricca ◽  
...  
Keyword(s):  
Emergency Department ◽  
Length Of Stay ◽  
Patient Experience ◽  
Waiting Times ◽  
Hospital Length ◽  
Short Length ◽  
Hospital Length Of Stay ◽  
Patient’S Experience ◽  
Discharged Patients ◽  
The Impact

Prolonged waiting times are associated with worse patient experience in patients discharged from the emergency department (ED). However, it is unclear which component of the waiting times is most impactful to the patient experience and the impact on hospitalized patients. We performed a retrospective analysis of ED patients between July 2018 and March 30, 2020. In all, 3278 patients were included: 1477 patients were discharged from the ED, and 1680 were admitted. Discharged patients had a longer door-to-first provider and door-to-doctor time, but a shorter doctor-to-disposition, disposition-to-departure, and total ED time when compared to admitted patients. Some, but not all, components of waiting times were significantly higher in patients with suboptimal experience (<100th percentile). Prolonged door-to-doctor time was significantly associated with worse patient experience in discharged patients and in patients with hospital length of stay ≤4 days. Prolonged ED waiting times were significantly associated with worse patient experience in patients who were discharged from the ED and in inpatients with short length of stay. Door-to-doctor time seems to have the highest impact on the patient’s experience of these 2 groups.

Does a Dedicated Pediatric Team Within a Busy Mixed Emergency Department Make a Difference in Waiting Times, Satisfaction, and Care Transition?

2010 ◽  
Vol 26 (4) ◽  
pp. 274-280 ◽  
Author(s):  
Gerben Keijzers ◽  
Julia Crilly ◽  
Benjamin Walters ◽  
Rosalind Crawford ◽  
Christa Bell
Keyword(s):  
Emergency Department ◽  
Waiting Times ◽  
Care Transition

A MODIFIED RATIO TYPE ESTIMATOR OF FINITE POPULATION MEAN UNDER STRATIFIED RANDOM SAMPLING SCHEME

2021 ◽  
pp. 58-60
Author(s):  
Naziru Fadisanku Haruna ◽  
Ran Vijay Kumar Singh ◽  
Samsudeen Dahiru
Keyword(s):  
Mean Square Error ◽  
Random Sampling ◽  
Minimum Mean Square Error ◽  
Real Life ◽  
Population Data ◽  
Auxiliary Variable ◽  
Mean Square ◽  
Data Set ◽  
Population Mean ◽  
The Mean

In This paper a modied ratio-type estimator for nite population mean under stratied random sampling using single auxiliary variable has been proposed. The expression for mean square error and bias of the proposed estimator are derived up to the rst order of approximation. The expression for minimum mean square error of proposed estimator is also obtained. The mean square error the proposed estimator is compared with other existing estimators theoretically and condition are obtained under which proposed estimator performed better. A real life population data set has been considered to compare the efciency of the proposed estimator numerically.

Heat Waves and Emergency Department Visits Among the Homeless, San Diego, 2012–2019

2022 ◽  
Vol 112 (1) ◽  
pp. 98-106
Author(s):  
Lara Schwarz ◽  
Edward M. Castillo ◽  
Theodore C. Chan ◽  
Jesse J. Brennan ◽  
Emily S. Sbiroli ◽  
...  
Keyword(s):  
Emergency Department ◽  
San Diego ◽  
Heat Waves ◽  
Positive Association ◽  
Emergency Department Visits ◽  
Psychiatric Consultation ◽  
Vulnerability Factors ◽  
Exact Matching ◽  
Data Set ◽  
Ed Visits

Objectives. To determine the effect of heat waves on emergency department (ED) visits for individuals experiencing homelessness and explore vulnerability factors. Methods. We used a unique highly detailed data set on sociodemographics of ED visits in San Diego, California, 2012 to 2019. We applied a time-stratified case–crossover design to study the association between various heat wave definitions and ED visits. We compared associations with a similar population not experiencing homelessness using coarsened exact matching. Results. Of the 24 688 individuals identified as experiencing homelessness who visited an ED, most were younger than 65 years (94%) and of non-Hispanic ethnicity (84%), and 14% indicated the need for a psychiatric consultation. Results indicated a positive association, with the strongest risk of ED visits during daytime (e.g., 99th percentile, 2 days) heat waves (odds ratio = 1.29; 95% confidence interval = 1.02, 1.64). Patients experiencing homelessness who were younger or elderly and who required a psychiatric consultation were particularly vulnerable to heat waves. Odds of ED visits were higher for individuals experiencing homelessness after matching to nonhomeless individuals based on age, gender, and race/ethnicity. Conclusions. It is important to prioritize individuals experiencing homelessness in heat action plans and consider vulnerability factors to reduce their burden. (Am J Public Health. 2022;112(1):98–106. https://doi.org/10.2105/AJPH.2021.306557 )

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