Clinical Decision Support System Using Fuzzy Logic Programming and Data Analysis

2021 ◽  
pp. 175-183
Author(s):  
Sandip Paul ◽  
Kumar Sankar Ray ◽  
Diganta Saha
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Data Analysis ◽  
Decision Support System ◽  
Logic Programming ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision

Design and Implementation of TCM Constitution Clinical Decision Support System

2014 ◽  
Vol 519-520 ◽  
pp. 1442-1446
Author(s):  
Ming Feng Zhu ◽  
Jian Qiang Du ◽  
Cheng Hua Ding
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Design And Implementation ◽  
Clinical Diagnoses ◽  
Feature Information

In this paper, a TCM constitution clinical decision support system is introduced. The features, functions, structure and working flow of this system are discussed and illustrated in detail. This system is composed of 5 modules. They are query module, investigation construction module, investigation modification module, investigation deletion module and data analysis module respectively. The property information, constitution information and tongue feature information are collected through investigation construction module. The constitution types of the testers can be automatically recognized and the prescriptions related to specific constitutions are automatically produced. Through data analysis module, specificities and sensitivities between the specific constitutions and the tongue features can be automatically obtained. The implementation of this system is of important value and guiding function during the process of TCM clinical diagnoses and treatments.

1309-P: Utilizing a Clinical Decision Support System for Hypoglycemia Management

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1309-P
Author(s):  
JACQUELYN R. GIBBS ◽  
KIMBERLY BERGER ◽  
MERCEDES FALCIGLIA
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
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.

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