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Medical Outcome Prediction for Intensive Care Unit Patients

Medical Outcome Prediction for Intensive Care Unit Patients
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Author(s): Simone A. Ludwig (North Dakota State University, USA), Stefanie Roos (Darmstadt University, Germany), Monique Frize (Carleton University, Canada) and Nicole Yu (Carleton University, Canada)
Copyright: 2010
Volume: 1
Issue: 4
Pages: 12
Source title: International Journal of Computational Models and Algorithms in Medicine (IJCMAM)
DOI: 10.4018/jcmam.2010100102

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Abstract

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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