Fuzzy Cognitive Maps Structure for Medical Decision Support Systems

2007 ◽  
pp. 151-174 ◽  
Author(s):  
Chrysostomos D. Stylios ◽  
Voula C. Georgopoulos
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Medical Decision Support Systems

Medical decision support systems based on Fuzzy Cognitive Maps

2019 ◽  
Vol 12 (06) ◽  
pp. 1950069 ◽  
Author(s):  
Shaista Habib ◽  
Muhammad Akram
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Maternal Education ◽  
Support Systems ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Assessment System ◽  
Medical Decision ◽  
Skin Fold Thickness ◽  
Medical Decision Support Systems

This paper determines the risk for cardiovascular diseases (CVDs), and nutrition level in infants aged 0–6 months using Fuzzy Cognitive Maps (FCMs). The aim of this study is to facilitates the medical experts to early detects these diseases with accuracy, so that overall death ratio can be reduced. Firstly, we have introduced the concepts of FCMs and briefly refer to the applications of these methods in medical. After that, two intelligent decision support systems for cardiovascular and malnutrition are developed using FCMs. The proposed cardiovascular risk assessment system takes six inputs: chest pain, cholesterol, heart rate, blood pressure, blood sugar, and old peak and determines CVDs risk. The second decision support system of malnutrition diagnosis takes twelve inputs: breastfeeding, daily income, maternal education, colostrum intake, energy intake, protein intake, vitamin A intake, iron intake, family size, height, weight, head circumference, and skin fold thickness and diagnoses the nutrition level in infants. We have explained the working of both decision support systems using case studies.

Medical Outcome Prediction for Intensive Care Unit Patients

2012 ◽  
pp. 251-262
Author(s):  
Simone A. Ludwig ◽  
Stefanie Roos ◽  
Monique Frize ◽  
Nicole Yu
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Medical Decision ◽  
Adult Intensive Care Unit ◽  
Medical Decision Support ◽  
Elimination Algorithm ◽  
Medical Decision Support Systems

The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: “hours of ventilation” and the “mortality rate” in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected.

Medical Outcome Prediction for Intensive Care Unit Patients

2012 ◽  
pp. 1068-1079
Author(s):  
Simone A. Ludwig ◽  
Stefanie Roos ◽  
Monique Frize ◽  
Nicole Yu
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Medical Decision ◽  
Adult Intensive Care Unit ◽  
Medical Decision Support ◽  
Elimination Algorithm ◽  
Medical Decision Support Systems

The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: “hours of ventilation” and the “mortality rate” in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected.

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