Predicting Postoperative Vomiting for Orthopedic Patients Receiving Patient-Controlled Epidural Analgesia with the Application of an Artificial Neural Network

Patient-controlled epidural analgesia (PCEA) was used in many patients receiving orthopedic surgery to reduce postoperative pain but is accompanied with certain incidence of vomiting. Predictions of the vomiting event, however, were addressed by only a few authors using logistic regression (LR) models. Artificial neural networks (ANN) are pattern-recognition tools that can be used to detect complex patterns within data sets. The purpose of this study was to develop the ANN based predictive model to identify patients with high risk of vomiting during PCEA used. From January to March 2007, the PCEA records of 195 patients receiving PCEA after orthopedic surgery were used to develop the two predicting models. The ANN model had a largest area under curve (AUC) in receiver operating characteristic (ROC) curve. The areas under ROC curves of ANN and LR models were 0.900 and 0.761, respectively. The computer-based predictive model should be useful in increasing vigilance in those patients most at risk for vomiting while PCEA is used, allowing for patient-specific therapeutic intervention, or even in suggesting the use of alternative methods of analgesia.

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Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR

Scientific Reports ◽

10.1038/srep27041 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 4

Author(s):

Hsin-Yun Wu ◽

Cihun-Siyong Alex Gong ◽

Shih-Pin Lin ◽

Kuang-Yi Chang ◽

Mei-Yung Tsou ◽

...

Keyword(s):

High Risk ◽

Epidural Analgesia ◽

Predictive Model ◽

Characteristic Curve ◽

Predictive Performance ◽

Roc Curves ◽

Supervised Machine Learning ◽

Patient Specific ◽

Support Vector ◽

Orthopedic Patients

Abstract Patient-controlled epidural analgesia (PCEA) has been applied to reduce postoperative pain in orthopedic surgical patients. Unfortunately, PCEA is occasionally accompanied by nausea and vomiting. The logistic regression (LR) model is widely used to predict vomiting, and recently support vector machines (SVM), a supervised machine learning method, has been used for classification and prediction. Unlike our previous work which compared Artificial Neural Networks (ANNs) with LR, this study uses a SVM-based predictive model to identify patients with high risk of vomiting during PCEA and comparing results with those derived from the LR-based model. From January to March 2007, data from 195 patients undergoing PCEA following orthopedic surgery were applied to develop two predictive models. 75% of the data were randomly selected for training, while the remainder was used for testing to validate predictive performance. The area under curve (AUC) was measured using the Receiver Operating Characteristic curve (ROC). The area under ROC curves of LR and SVM models were 0.734 and 0.929, respectively. A computer-based predictive model can be used to identify those who are at high risk for vomiting after PCEA, allowing for patient-specific therapeutic intervention or the use of alternative analgesic methods.

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A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network

Sustainability ◽

10.3390/su12104001 ◽

2020 ◽

Vol 12 (10) ◽

pp. 4001

Author(s):

Sung-Sik Park ◽

Peter D. Ogunjinmi ◽

Seung-Wook Woo ◽

Dong-Eun Lee

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Model ◽

Model Equation ◽

Soil Layer ◽

Unit Weight ◽

Ann Model ◽

Matrix Formulation ◽

Input Parameters ◽

Artificial Neural

Conventionally, liquefaction-induced settlements have been predicted through numerical or analytical methods. In this study, a machine learning approach for predicting the liquefaction-induced settlement at Pohang was investigated. In particular, we examined the potential of an artificial neural network (ANN) algorithm to predict the earthquake-induced settlement at Pohang on the basis of standard penetration test (SPT) data. The performance of two ANN models for settlement prediction was studied and compared in terms of the R2 correlation. Model 1 (input parameters: unit weight, corrected SPT blow count, and cyclic stress ratio (CSR)) showed higher prediction accuracy than model 2 (input parameters: depth of the soil layer, corrected SPT blow count, and the CSR), and the difference in the R2 correlation between the models was about 0.12. Subsequently, an optimal ANN model was used to develop a simple predictive model equation, which was implemented using a matrix formulation. Finally, the liquefaction-induced settlement chart based on the predictive model equation was proposed, and the applicability of the chart was verified by comparing it with the interferometric synthetic aperture radar (InSAR) image.

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Assessing the Performance of Artificial Neural Networks to Predict Ionospheric TEC over Nigeria During Different Space Weather events.

Canadian Journal of Physics ◽

10.1139/cjp-2021-0001 ◽

2021 ◽

Author(s):

O.E. Abe ◽

S.S. Rukera ◽

B. Adeyemi ◽

O. Ogunmodimu ◽

I. Emmanuel ◽

...

Keyword(s):

High Solar Activity ◽

Electron Content ◽

Data Sets ◽

Ann Model ◽

Total Electron ◽

Ionosphere Model ◽

Weather Events ◽

The Mean ◽

Artificial Neural ◽

Artificial Neural Network Ann

Ionosphere model is much essential to satellite-based system in order to accurately correct the ionospheric error encountered by the satellite signals’ en-route. Levenberg-Marquardt backpropagation (LMBP) algorithm in the Artificial Neural Network (ANN) was used in this work to predict the Total Electron Content (TEC) within the trough of Equatorial Ionization Anomaly (EIA) over Nigeria. Two sets of data were used over the period of three consecutive years (2011-2013) of high solar activity. The first set was used as an input to the ANN model and the second set of data was used as a target. 70% of the data sets were used to train the network, 15% of the data were used for validation and 15% used for testing. The performance of the model was assessed during specific quiet and disturbed geomagnetic conditions. The regression analysis of the model output was optimized by minimizing a cost function of the Mean Square Error (MSE). The results of the errors, regression and comparative analyses have revealed that ANN model is able to predict accurate and reliable TEC that compares well with the actual experimental at any geophysical conditions. Hence, this model would be useful to forecast TEC over Nigeria to a reliable threshold.

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An Artificial Neural Network Approach for Prediction of Bearing Capacity of Spread Foundations in Sand

Jurnal Teknologi ◽

10.11113/jt.v72.4004 ◽

2015 ◽

Vol 72 (3) ◽

Cited By ~ 1

Author(s):

Ramli Nazir ◽

Ehsan Momeni ◽

Kadir Marsono ◽

Harnedi Maizir

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Bearing Capacity ◽

Predictive Model ◽

Linear Regression Analysis ◽

Load Test ◽

Coefficient Of Determination ◽

Ann Model ◽

Cohesionless Soils ◽

Artificial Neural

This study highlights the application of Back-Propagation (BP) feed forward Artificial Neural Network (ANN) as a tool for predicting bearing capacity of spread foundations in cohesionless soils. For network construction, a database of 75 recorded cases of full-scale axial compression load test on spread foundations in cohesionless soils was compiled from literatures. The database presents information about footing length (L), footing width (B), embedded depth of the footing (Df), average vertical effective stress of the soil at B/2 below footing (s΄), friction angle of the soil (f) and the ultimate axial bearing capacity (Qu). The last parameter was set as the desired output in the ANN model, while the rest were used as input of the ANN predictive model of bearing capacity. The prediction performance of ANN model was compared to that of Multi-Linear Regression analysis. Findings show that the proposed ANN model is a suitable tool for predicting bearing capacity of spread foundations. Coefficient of determination R2 equals to 0.98, strongly indicates that the ANN model exhibits a high degree of accuracy in predicting the axial bearing capacity of spread foundation. Using sensitivity analysis, it is concluded that the geometrical properties of the spread foundations (B and L) are the most influential parameters in the proposed predictive model of Qu.

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Artificial Neural Network Model for Rainfall–Runoff Relationship

Jurnal Teknologi ◽

10.11113/jt.v37.524 ◽

2012 ◽

Cited By ~ 2

Author(s):

Sobri Harun ◽

Nor Irwan Ahmat Nor ◽

Amir Hashim Mohd. Kassim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Flood Control ◽

Mathematical Structure ◽

Data Sets ◽

Rainfall Runoff ◽

Ann Model ◽

Linear Relationships ◽

Artificial Neural ◽

Artificial Neural Network Ann

Permodelan bagi proses hidraulik dan hidrologi adalah penting apabila dilihat dari sudut kepelbagaian penggunaan sumber air seperti janakuasa hidroeletrik, pengairan, pengagihan bekalan air, dan kawalan banjir. Terdapat banyak kajian sebelum ini yang telah menggunakan kaedah rangkaian neural tiruan atau artificial neural network (ANN) untuk permodelan pelbagai perhubungan tak linear dan kompleks dalam proses hidrologi. Kaedah rangkaian neural tiruan ini telah diketahui bahawa ia merupakan suatu struktur matematik yang mudah ubah (flexible) dan berpotensi untuk menjana dan merumus set-set data masukan dan keluaran yang kurang tepat atau kabur dan tidak dihalusi dengan sempurna. Kawasan kajian adalah kawasan tadahan Sungai Lui (Selangor, Malaysia). Kertas Kerja ini mengutarakan cadangan menggunakan kaedah rangkaian neural tiruan ini bagi mendapatkan jumlah air larian permukaan harian dengan menggunakan hujan sebagai nod masukan kepada model berkenaan. Terdapat dua kaedah telah digunakan dalam pemilihan bilangan nod masukan iaitu seperti yang telah dicadangkan oleh [10] dan [5]. Seterusnya, hasil keputusan yang diperolehi daripada permodelan rangkaian neural tiruan ini dibandingkan dengan hasil keputusan yang diperolehi daripada model HEC-HMS. Didapati bahawa model rangkaian neural tiruan dapat menjana dan merumus perhubungan antara air larian permukaan dan curahan hujan lebih baik berbanding dengan model HEC-HMS. Kata kunci: hidrologi, rangkaian neural tiruan, hubungan air larian permukaan-curahan hujan The modelling of hydraulic and hydrological processes is important in view of the many uses of water resources such as hydropower generation, irrigation, water supply, and flood control. There are many previous works using the artificial neural network (ANN) method for modelling various complex non-linear relationships of hydrologic processes. The ANN is well known as a flexible mathematical structure and has the ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. The study area is Sungai Lui catchment (Selangor, Malaysia). This paper presents the proposed ANN model for prediction of daily runoff using the rainfall as input nodes. The method for selection of input nodes by [10] and [5] is applied. Further, the results are compared between ANN and HEC-HMS model. It has been found that the ANN models show a good generalization of rainfall-runoff relationship and is better than HEC-HMS model. Key words: hydrologic, artificial neural network, rainfall-runoff relationship

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Artificial Neural Network Prediction Model of Dust Effect on Photovoltaic Performance for Residential applications: Malaysia Case Study

International Journal of Renewable Energy Development ◽

10.14710/ijred.2022.42195 ◽

2021 ◽

Vol 11 (2) ◽

pp. 365-373

Author(s):

Emy Zairah Ahmad ◽

Hasila Jarimi ◽

Tajul Rosli Razak

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Model ◽

Predictive Model ◽

Operating Conditions ◽

Ann Model ◽

Pv System ◽

Pv Module ◽

Artificial Neural ◽

Dust Accumulation

Dust accumulation on the photovoltaic system adversely degrades its power conversion efficiency (PCE). Focusing on residential installations, dust accumulation on PV modules installed in tropical regions may be vulnerable due to lower inclination angles and rainfall that encourage dust settlement on PV surfaces. However, most related studies in the tropics are concerned with studies in the laboratory, where dust collection is not from the actual field, and an accurate performance prediction model is impossible to obtain. This paper investigates the dust-related degradation in the PV output performance based on the developed Artificial Neural Network (ANN) predictive model. For this purpose, two identical monocrystalline modules of 120 Wp were tested and assessed under real operating conditions in Melaka, Malaysia (2.1896° N, 102.2501° E), of which one module was dust-free (clean). At the same time, the other was left uncleaned (dusty) for one month. The experimental datasets were divided into three sets: the first set was used for training and testing purposes, while the second and third, namely Data 2 and Data 3, were used for validating the proposed ANN model. The accuracy study shows that the predicted data using the ANN model and the experimentally acquired data are in good agreement, with MAE and RMSE for the cleaned PV module are as low as 1.28 °C, and 1.96 °C respectively for Data 2 and 3.93 °C and 4.92 °C respectively for Data 3. Meanwhile, the RMSE and MAE for the dusty PV module are 1.53°C and 2.82 °C respectively for Data 2 and 4.13 °C and 5.26 °C for Data 3. The ANN predictive model was then used for yield forecasting in a residential installation and found that the clean PV system provides a 7.29 % higher yield than a dusty system. The proposed ANN model is beneficial for PV system installers to assess and anticipate the impacts of dust on the PV installation in cities with similar climatic conditions.

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Estimating Human Body Dimensions Using RBF Artificial Neural Networks Technology and Its Application in Activewear Pattern Making

Applied Sciences ◽

10.3390/app9061140 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1140

Author(s):

Zhujun Wang ◽

Jianping Wang ◽

Yingmei Xing ◽

Yalan Yang ◽

Kaixuan Liu

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Predictive Model ◽

Human Body ◽

Mean Squared Error ◽

Back Propagation ◽

Adult Males ◽

Ann Model ◽

Body Dimensions ◽

Artificial Neural

Nowadays, the popularity of the internet has continuously increased. Predicting human body dimensions intelligently would be beneficial to improve the precision and efficiency of pattern making for enterprises in the apparel industry. In this study, a new predictive model for estimating body dimensions related to garment pattern making is put forward based on radial basis function (RBF) artificial neural networks (ANNs). The model presented in this study was trained and tested using the anthropometric data of 200 adult males between the ages 20 and 48. The detailed body dimensions related to pattern making could be obtained by inputting four easy-to-measure key dimensions into the RBF ANN model. From the simulation results, when spreading parameter σ and momentum factor α were set to 0.012 and 1, the three-layer model with 4, 72, and 8 neurons in the input, hidden, and output layers, respectively, showed maximum accuracy, after being trained by a dataset with 180 samples. Moreover, compared with a classic linear regression model and the back propagation (BP) ANN model according to mean squared error, the predictive performance of the RBF ANN model put forward in this study was better than the other two models. Therefore, it is feasible for the presented predictive model to design garment patterns, especially for tight-fitting garment patterns like activewear. The estimating accuracy of the proposed model would be further improved if trained by more appropriate datasets in the future.

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An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III

BioMed Research International ◽

10.1155/2021/6638919 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Ning Ding ◽

Cuirong Guo ◽

Changluo Li ◽

Yang Zhou ◽

Xiangping Chai

Keyword(s):

Neural Networks ◽

Acute Pancreatitis ◽

Logistic Regression ◽

Artificial Neural Networks ◽

Hospital Mortality ◽

Predictive Model ◽

Fatal Outcome ◽

Ann Model ◽

Artificial Neural ◽

Mimic Iii

Background. Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of in-hospital mortality in AP. Methods. Patients with AP were identified from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Clinical and laboratory data were utilized to perform a predictive model by back propagation ANN approach. Results. A total of 337 patients with AP were analyzed in the study, and the in-hospital mortality rate was 11.2%. A total of 12 variables that differed between patients in survivor group and nonsurvivor group were applied to construct ANN model. Three independent variables were identified as risk factors associated with in-hospital mortality by multivariate logistic regression analysis. The predictive performance based on the area under the receiver operating characteristic curve (AUC) was 0.769 for ANN model, 0.607 for logistic regression, 0.652 for Ranson score, and 0.401 for SOFA score. Conclusion. An ANN predictive model for in-hospital mortality in patients with AP in MIMIC-III database was first performed. The patients with high risk of fatal outcome can be screened out easily in the early stage of AP by our model.

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Hysteresis modeling of impact dynamics using artificial neural network

Journal of Mechanics ◽

10.1093/jom/ufab007 ◽

2021 ◽

Vol 37 ◽

pp. 333-338

Author(s):

T A Tabaza ◽

O Tabaza ◽

J Barrett ◽

A Alsakarneh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Impact Dynamics ◽

Hysteresis Phenomenon ◽

Data Sets ◽

Ann Model ◽

Hysteresis Modeling ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The Impact

Abstract In this paper, the process of training an artificial neural network (ANN) on predicting the hysteresis of a viscoelastic ball and ash wood bat colliding system is discussed. To study how the material properties and the impact speed affect the hysteresis phenomenon, many experiments were conducted for colliding three types of viscoelastic balls known as sliotars at two different speeds. The aim of the study is to innovate a neural network model to predict the hysteresis phenomenon of the collision of viscoelastic materials. The model accurately captured the input data and was able to produce data sets out of the input ranges. The results show that the ANN model predicted the impact hysteresis accurately with <1% error.

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Effect of CT-based attenuation correction on uptake ratios in skeletal SPECT

Nuklearmedizin ◽

10.1055/s-0037-1616624 ◽

2007 ◽

Vol 46 (01) ◽

pp. 38-42 ◽

Cited By ~ 12

Author(s):

V. Schulz ◽

I. Nickel ◽

A. Nömayr ◽

A. H. Vija ◽

C. Hocke ◽

...

Keyword(s):

Vertebral Body ◽

Facet Joint ◽

One Dimension ◽

Patient Specific ◽

Data Sets ◽

Facet Joints ◽

Lumbar Vertebral Body ◽

Ct System ◽

Ct Data ◽

Attenuation Corrected

SummaryThe aim of this study was to determine the clinical relevance of compensating SPECT data for patient specific attenuation by the use of CT data simultaneously acquired with SPECT/CT when analyzing the skeletal uptake of polyphosphonates (DPD). Furthermore, the influence of misregistration between SPECT and CT data on uptake ratios was investigated. Methods: Thirty-six data sets from bone SPECTs performed on a hybrid SPECT/CT system were retrospectively analyzed. Using regions of interest (ROIs), raw counts were determined in the fifth lumbar vertebral body, its facet joints, both anterior iliacal spinae, and of the whole transversal slice. ROI measurements were performed in uncorrected (NAC) and attenuation-corrected (AC) images. Furthermore, the ROI measurements were also performed in AC scans in which SPECT and CT images had been misaligned by 1 cm in one dimension beforehand (ACX, ACY, ACZ). Results: After AC, DPD uptake ratios differed significantly from the NAC values in all regions studied ranging from 32% for the left facet joint to 39% for the vertebral body. AC using misaligned pairs of patient data sets led to a significant change of whole-slice uptake ratios whose differences ranged from 3,5 to 25%. For ACX, the average left-to-right ratio of the facet joints was by 8% and for the superior iliacal spines by 31% lower than the values determined for the matched images (p <0.05). Conclusions: AC significantly affects DPD uptake ratios. Furthermore, misalignment between SPECT and CT may introduce significant errors in quantification, potentially also affecting leftto- right ratios. Therefore, at clinical evaluation of attenuation- corrected scans special attention should be given to possible misalignments between SPECT and CT.

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