The development of a decision support system for the pathological diagnosis of human cerebral tumours based on a neural network classifier

1994 ◽  
Vol 129 (3-4) ◽  
pp. 193-197 ◽  
Author(s):  
G. Sieben ◽  
M. Praet ◽  
H. Roels ◽  
G. Otte ◽  
L. Boullart ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Pathological Diagnosis ◽  
Neural Network Classifier ◽  
Cerebral Tumours

scholarly journals Weighted Deep Neural Network Based Clinical Decision Support System for the Determination of Fetal Health

2019 ◽  
Vol 8 (4) ◽  
pp. 8564-8569
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Deep Neural Network ◽  
Predictive Analytics ◽  
Clinical Decision ◽  
Svm Classifier ◽  
Healthcare Data ◽  
The Individual

Healthcare industry is undergoing changes at a tremendous rate due to healthcare innovations. Predictive analytics is increasingly being used to diagnose the patient’s ailments and provide actionable insights into already existing healthcare data. The paper looks at a decision support system for determining the health status of the foetus from cardiotographic data using deep learning neural networks. The foetal health records are classified as normal, suspect and pathological. As the multiclass cardiotographic datset of the foetus shows a high degree of imbalance a weighted deep neural network is applied. To overcome the accuracy paradox due to the multiclass imbalance, relevant metrics such as the sensitivity, specificity, F1 Score and Gmean are used to measure the performance of the classifier rather than accuracy. The metrics are applied to the individual classes to ensure that the positive cases are identified correctly. The weighted DNN based classifier is able to classify the positive instances with Gmean score of 91% which is better than than the SVM classifier.

Clinical Decision Support System for Early Disease Detection and Management

2019 ◽  
pp. 108-155
Author(s):  
Likewin Thomas ◽  
Manoj Kumar M. V. ◽  
Annappa B.
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Medical Error ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Healthcare Management ◽  
Statistical Techniques

Medical error is an adverse event of a failure in healthcare management, causing unintended injuries. Proper clinical care can be provided by employing a suitable clinical decision support system (CDSS) for healthcare management. CDSS assists the clinicians in identifying the severity of disease at the time of admission and predicting its progression. In this chapter, CDSS was developed with the help of statistical techniques. Modified cascade neural network (ModCNN) was built upon the architecture of cascade-correlation neural network (CCNN). ModCNN first identifies the independent factors associated with disease and using that factor; it predicts its progression. A case progressing towards severity can be given better care, avoiding later stage complications. Performance of ModCNN was evaluated and compared with artificial neural network (ANN) and CCNN. ModCNN showed better accuracy than other statistical techniques. Thus, CDSS developed in this chapter is aimed at providing better treatment planning by reducing medical error.

Applying Fuzzy Logic and Neural Network to Rheumatism Treatment in Oriental Medicine

2007 ◽  
Vol 11 (1) ◽  
pp. 4-10 ◽  
Author(s):  
Cao Thang ◽  
◽  
Eric W. Cooper ◽  
Yukinobu Hoshino ◽  
Katsuari Kamei ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference ◽  
Training Data ◽  
Oriental Medicine ◽  
The Neural Networks

In this paper, we present an application of soft computing into a decision support system RETS: Rheumatic Evaluation and Treatment System in Oriental Medicine (OM). Inputs of the system are severities of observed symptoms on patients and outputs are a diagnosis of rheumatic states, its explanations and herbal prescriptions. First, an outline of the proposed decision support system is described after considering rheumatic diagnoses and prescriptions by OM doctors. Next, diagnosis by fuzzy inference and prescription by neural networks are described. By fuzzy inference, RETS diagnoses the most appropriate rheumatic state in which the patient appears to be infected, then it gives a prescription written in suitable herbs with reasonable amounts based on neural networks. Training data for the neural networks is collected from experienced OM physicians and OM text books. Finally, we describe evaluations and restrictions of RETS.

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