scholarly journals A Survey on Data Mining Algorithms and Techniques in Medicine

2017 ◽  
Vol 1 (3) ◽  
pp. 61 ◽  
Author(s):  
Kasra Madadipouya
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Performance Metrics ◽  
Support Systems ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Data Mining Algorithms ◽  
Medical Decision Support Systems ◽  
Mining Algorithms

Medical Decision Support Systems (MDSS) industry collects a huge amount of data, which is not properly mined and not put to the optimum use. This data may contain valuable information that awaits extraction. The knowledge may be encapsulated in various patterns and regularities that may be hidden in the data. Such knowledge may prove to be priceless in future medical decision making. Available medical decision support systems are based on static data, which may be out of date. Thus, a medical decision support system that can learn the relationships between patient histories, diseases in the population, symptoms, pathology of a disease, family history, and test results, would be useful to physicians and hospitals. This paper provides an in-depth review of available data mining algorithms and techniques. In addition to that, data mining applications in medicine are discussed as well as techniques for evaluating them and available applications of performance metrics.

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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