scholarly journals A study on Fuzzy Cognitive Map structures for Medical Decision Support Systems

2013 ◽  
Author(s):  
Evangelia Bourgani ◽  
Chrysostomos Stylios ◽  
Voula Georgopoulos ◽  
George Manis
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Medical Decision Support Systems

Fuzzy cognitive map architectures for medical decision support systems

2008 ◽  
Vol 8 (3) ◽  
pp. 1243-1251 ◽  
Author(s):  
Chrysostomos D. Stylios ◽  
Voula C. Georgopoulos ◽  
Georgia A. Malandraki ◽  
Spyridoula Chouliara
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Medical Decision Support Systems

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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