An Intelligent Clinical Decision Support System Based on Artificial Neural Network for Early Diagnosis of Cardiovascular Diseases in Rural Areas

2019 ◽  
Author(s):  
Sonakshi Harjai ◽  
Sunil Kumar Khatri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Decision Support ◽  
Cardiovascular Diseases ◽  
Early Diagnosis ◽  
Clinical Decision Support ◽  
Support System ◽  
Rural Areas ◽  
Clinical Decision Support System ◽  
Clinical Decision

A Clinical Decision Support System for Assessing the Risk of Cardiovascular Diseases in Diabetic Hemodialysis Patients

2020 ◽  
Vol 16 (3) ◽  
pp. 262-269
Author(s):  
Tahere Talebi Azad Boni ◽  
Haleh Ayatollahi ◽  
Mostafa Langarizadeh
Keyword(s):  
Decision Support ◽  
Cardiovascular Diseases ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Diabetic Patients ◽  
K Value ◽  
Care Plans

Background: One of the greatest challenges in the field of medicine is the increasing burden of chronic diseases, such as diabetes. Diabetes may cause several complications, such as kidney failure which is followed by hemodialysis and an increasing risk of cardiovascular diseases. Objective: The purpose of this research was to develop a clinical decision support system for assessing the risk of cardiovascular diseases in diabetic patients undergoing hemodialysis by using a fuzzy logic approach. Methods: This study was conducted in 2018. Initially, the views of physicians on the importance of assessment parameters were determined by using a questionnaire. The face and content validity of the questionnaire was approved by the experts in the field of medicine. The reliability of the questionnaire was calculated by using the test-retest method (r = 0.89). This system was designed and implemented by using MATLAB software. Then, it was evaluated by using the medical records of diabetic patients undergoing hemodialysis (n=208). Results: According to the physicians' point of view, the most important parameters for assessing the risk of cardiovascular diseases were glomerular filtration, duration of diabetes, age, blood pressure, type of diabetes, body mass index, smoking, and C reactive protein. The system was designed and the evaluation results showed that the values of sensitivity, accuracy, and validity were 85%, 92% and 90%, respectively. The K-value was 0.62. Conclusion: The results of the system were largely similar to the patients’ records and showed that the designed system can be used to help physicians to assess the risk of cardiovascular diseases and to improve the quality of care services for diabetic patients undergoing hemodialysis. By predicting the risk of the disease and classifying patients in different risk groups, it is possible to provide them with better care plans.

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.

scholarly journals Sistem Pendukung Keputusan Klinis dengan Memanfaatkan Jaringan Syaraf Tiruan Untuk Mendeteksi Stadium Penderita Kanker Paru-Paru Jenis Karsinoma Bukan Sel Kecil

2011 ◽  
Vol 5 (2) ◽  
Author(s):  
Rio Kurniawan ◽  
Sri Hartati
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Learning Process ◽  
Clinical Decision Support System ◽  
Early Stage ◽  
Clinical Decision

Abstract-- Lung cancer is leading cause of death in the cancer group. In general, lung cancer has some symptoms, but at an early stage, symptoms are not perceived by the patient. As a result, when patients go to hospital, lung cancer has been diagnosed in middle or high stage. For early detection of lung cancer, necessary a decision support system based on computerized technology that can be utilized by doctor needed to detection lung cancer. The clinical decision support system will help to determine specific medical treatment. The clinical decision support system capable to know data input and produce output result by learning process. The learning process is  part of process in artificial neural network (ANN). Many methods used in ANN as Backpropagation (BP)learning algorithm. BP used to produce output result in decision support system. Keywords-- lung cancer, stage, clinical decision support systems, neural network, multilayer perceptron, backpropagation algorithm

Clinical Decision Support System for Early Disease Detection and Management

2021 ◽  
pp. 1035-1075
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.

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