Using Connectionist Modules for Decision Support

1990 ◽  
Vol 29 (03) ◽  
pp. 167-181 ◽  
Author(s):  
G. Hripcsak
Keyword(s):  
Decision Support ◽  
Standard Deviation ◽  
Confidence Interval ◽  
Posterior Probability ◽  
Back Propagation ◽  
Connectionist Model ◽  
Test Set ◽  
The Third ◽  
Independent Test ◽  
Better Than

AbstractA connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.

The use of a rule-based module as a decision support system for dystocia detection in dairy cows

2018 ◽  
Author(s):  
D. Zaborski ◽  
W. Grzesiak ◽  
J. Wojcik
Keyword(s):  
Decision Support ◽  
Dairy Cows ◽  
Predictive Performance ◽  
Test Set ◽  
Rule Based ◽  
Black And White ◽  
Holstein Friesian ◽  
Independent Test ◽  
Tree Models ◽  
User Friendly

The aim of the present study was to construct a rule-based module (RBM) for dystocia detection in dairy cattle and to verify its predictive performance. A total of 3041 calving records of Polish Holstein-Friesian Black-and-White heifers and cows were used. Three continuous and seven categorical predictors of dystocia were included in the three decision tree models, from which the rules for RBM were extracted. The system was equipped with a user-friendly text interface. The percentage of correctly detected easy, moderate and difficult calvings in heifers on the independent test set was 26.13%, 76.52% and 77.27%, respectively. The overall accuracy was 60.12%. The respective values for cows were: 59.18%, 69.01%, 0% and 62.03%. The predictive performance of the constructed system was satisfactory, except for the difficult category in cows.

scholarly journals Automated Recognition of Ultrasound Cardiac Views Based on Deep Learning with Graph Constraint

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1177
Author(s):  
Yanhua Gao ◽  
Yuan Zhu ◽  
Bo Liu ◽  
Yue Hu ◽  
Gang Yu ◽  
...  
Keyword(s):  
Cardiac Cycle ◽  
Test Set ◽  
Automated Recognition ◽  
Cardiac Image ◽  
Independent Test ◽  
Comparable Accuracy ◽  
The Mean ◽  
Shape Changes ◽  
Generalization Accuracy ◽  
Better Than

In transthoracic echocardiographic (TTE) examination, it is essential to identify the cardiac views accurately. Computer-aided recognition is expected to improve the accuracy of cardiac views of the TTE examination, particularly when obtained by non-trained providers. A new method for automatic recognition of cardiac views is proposed consisting of three processes. First, a spatial transform network is performed to learn cardiac shape changes during a cardiac cycle, which reduces intra-class variability. Second, a channel attention mechanism is introduced to adaptively recalibrate channel-wise feature responses. Finally, the structured signals by the similarities among cardiac views are transformed into the graph-based image embedding, which acts as unsupervised regularization constraints to improve the generalization accuracy. The proposed method is trained and tested in 171792 cardiac images from 584 subjects. The overall accuracy of the proposed method on cardiac image classification is 99.10%, and the mean AUC is 99.36%, better than known methods. Moreover, the overall accuracy is 97.73%, and the mean AUC is 98.59% on an independent test set with 37,883 images from 100 subjects. The proposed automated recognition model achieved comparable accuracy with true cardiac views, and thus can be applied clinically to help find standard cardiac views.

A Developmental Study of Intonation Recognition

1968 ◽  
Vol 11 (4) ◽  
pp. 825-832 ◽  
Author(s):  
Marilyn M. Corlew
Keyword(s):  
Significant Interaction ◽  
Multiple Choice ◽  
Semantic Content ◽  
First Grade ◽  
Choice Test ◽  
Developmental Study ◽  
Multiple Choice Test ◽  
The Third ◽  
Matching Test ◽  
Better Than

Two experiments investigated the information conveyed by intonation from speaker to listener. A multiple-choice test was devised to test the ability of 48 adults to recognize and label intonation when it was separated from all other meaning. Nine intonation contours whose labels were most agreed upon by adults were each matched with two English sentences (one with appropriate and one with inappropriate intonation and semantic content) to make a matching-test for children. The matching-test was tape-recorded and given to children in the first, third, and fifth grades (32 subjects in each grade). The first-grade children matched the intonations with significantly greater agreement than chance; but they agreed upon significantly fewer sentences than either the third or fifth graders. Some intonation contours were matched with significantly greater frequency than others. The performance of the girls was better than that of the boys on an impatient question and a simple command which indicates that there was a significant interaction between sex and intonation.

SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN DAGING KAMBING BERKUALITAS BAIK DENGAN METODE FUZZY SUGENO BERBASIS ANDROID

2018 ◽  
Vol 10 (1) ◽  
pp. 1005
Author(s):  
Achmad Dika Asrori ◽  
Retno Wardhani ◽  
Masruroh Masruroh
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Design Stage ◽  
The Third

The decision support system used to determine good quality meat using Fuzzy Sugeno Method is made using Eclipse Application with some stages, those are: making the project then create the layout file and Java, after that design stage and coding are done once the project is ready to run. The results of the calculation on the Fuzzy Sugeno Method test show that there are five variables. First, the color of meat has three sets. It is categorized as bad 1 if it is in the range of 1-3,  good in the range of 4-6, and bad 2 in the range of 7-8. The second variable is related to the fat color. It has two sets, those are bad if it is in the range of 4-9 and good in the range 1-3. The third variable is marbling which has two sets, those are bad if it is in the range of 1-9 and good in the range of 10-12. The fourth variable has three sets, those are bad 1 if it is in the range of 0-4, good in the range of 5,3-5,8, and bad 2 in the range of 5,8-8 . The third category of marbling has two sets, those are bad if it is in the range of 8 and good in the range of 8.

scholarly journals Deep learning for intelligent diagnosis in thyroid scintigraphy

2021 ◽  
Vol 49 (1) ◽  
pp. 030006052098284
Author(s):  
Tingting Qiao ◽  
Simin Liu ◽  
Zhijun Cui ◽  
Xiaqing Yu ◽  
Haidong Cai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Kappa Coefficient ◽  
Diagnostic Assessment ◽  
First Year ◽  
Thyroid Scintigraphy ◽  
Diagnostic Ability ◽  
The Third ◽  
Classification Time ◽  
Better Than

Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

scholarly journals Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jong Ho Kim ◽  
Haewon Kim ◽  
Ji Su Jang ◽  
Sung Mi Hwang ◽  
So Young Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Confidence Interval ◽  
Neck Circumference ◽  
Difficult Laryngoscopy ◽  
Gradient Boosting ◽  
Test Set ◽  
Equal Distribution ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Abstract Background Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation. Methods Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set. Results The model’s performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72–0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27–0.37]). Conclusions Machine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.

A Comparison of Two Aerial Photo Volume Tables for Pine Stands in Central Mississippi

1981 ◽  
Vol 5 (2) ◽  
pp. 92-96 ◽  
Author(s):  
James L. Smith ◽  
Roy A. Mead
Keyword(s):  
Prediction Models ◽  
Quadratic Model ◽  
Aerial Photo ◽  
Test Set ◽  
Volume Table ◽  
Independent Test ◽  
Pine Stands ◽  
Negative Volume

Abstract Two aerial photo volume prediction models, Avery's Composite Aerial Volume Table, and Mead's Quadratic Model, were compared using graphs and a small independent test set. The graphs indicated that Mead's model predicted higher merchantable volumes for pine stands in central Mississippi than did Avery's model. Both models tended to underpredict ground merchantable volume. However, only Avery's model underpredicted in a statistically significant manner. Even though the possibility of negative volume predictions exists when using Mead's Quadratic Model, it was deemed the superior model of the two investigated.

Application of Bayesian Network in Safety Evaluation of Metro Construction

2013 ◽  
Vol 838-841 ◽  
pp. 1463-1468
Author(s):  
Xiang Ke Liu ◽  
Zhi Shen Wang ◽  
Hai Liang Wang ◽  
Jun Tao Wang
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Posterior Probability ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Safety Evaluation ◽  
Tree Analysis ◽  
Tunnel Blasting ◽  
Better Than

The paper introduced the Bayesian networks briefly and discussed the algorithm of transforming fault tree into Bayesian networks at first, then regarded the structures impaired caused by tunnel blasting construction as a example, introduced the built and calculated method of the Bayesian networks by matlab. Then assumed the probabilities of essential events, calculated the probability of top event and the posterior probability of each essential events by the Bayesian networks. After that the paper contrast the characteristics of fault tree analysis and the Bayesian networks, Identified that the Bayesian networks is better than fault tree analysis in safety evaluation in some case, and provided a valid way to assess risk in metro construction.

Applying Artificial Neural Networks. I. Estimating Nicotine in Tobacco from near Infrared Data

1995 ◽  
Vol 3 (3) ◽  
pp. 133-142 ◽  
Author(s):  
M. Hana ◽  
W.F. McClure ◽  
T.B. Whitaker ◽  
M. White ◽  
D.R. Bahler
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Near Infrared ◽  
Back Propagation ◽  
Linear Network ◽  
Data Set ◽  
Input Layer ◽  
Propagation Network ◽  
Better Than

Two artificial neural network models were used to estimate the nicotine in tobacco: (i) a back-propagation network and (ii) a linear network. The back-propagation network consisted of an input layer, an output layer and one hidden layer. The linear network consisted of an input layer and an output layer. Both networks used the generalised delta rule for learning. Performances of both networks were compared to the multiple linear regression method MLR of calibration. The nicotine content in tobacco samples was estimated for two different data sets. Data set A contained 110 near infrared (NIR) spectra each consisting of reflected energy at eight wavelengths. Data set B consisted of 200 NIR spectra with each spectrum having 840 spectral data points. The Fast Fourier transformation was applied to data set B in order to compress each spectrum into 13 Fourier coefficients. For data set A, the linear regression model gave better results followed by the back-propagation network which was followed by the linear network. The true performance of the linear regression model was better than the back-propagation and the linear networks by 14.0% and 18.1%, respectively. For data set B, the back-propagation network gave the best result followed by MLR and the linear network. Both the linear network and MLR models gave almost the same results. The true performance of the back-propagation network model was better than the MLR and linear network by 35.14%.

scholarly journals Predicting the discharge coefficient of oblique cylindrical weir using neural network techniques

2021 ◽  
Vol 14 (16) ◽  
Author(s):  
Adnan A. Ismael ◽  
Saleh J. Suleiman ◽  
Raid Rafi Omar Al-Nima ◽  
Nadhir Al-Ansari
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Mean Square ◽  
The Third ◽  
Testing Stage ◽  
Inclination Angles

AbstractCylindrical weir shapes offer a steady-state overflow pattern, where the type of weirs can offer a simple design and provide the ease-to-pass floating debris. This study considers a coefficient of discharge (Cd) prediction for oblique cylindrical weir using three diameters, the first is of D1 = 0.11 m, the second is of D2 = 0.09 m, and the third is of D3 = 0.06.5 m, and three inclination angles with respect to channel axis, the first is of θ1 = 90 ͦ, the second is of θ2 = 45 ͦ, and the third is of θ3 = 30 ͦ. The Cd values for total of 56 experiments are estimated by using the radial basis function network (RBFN), in addition of comparing that with the back-propagation neural network (BPNN) and cascade-forward neural network (CFNN). Root mean square error (RMSE), mean square error (MSE), and correlation coefficient (CC) statics are used as metrics measurements. The RBFN attained superior performance comparing to the other neural networks of BPNN and CFNN. It is found that, for the training stage, the RBFN network benchmarked very small RMSE and MSE values of 1.35E-12 and 1.83E-24, respectively and for the testing stage, it also could benchmark very small RMSE and MSE values of 0.0082 and 6.80E-05, respectively.

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