- Author(s): Robert Greens (Editor)
- Publication: Academic Press, Inc.
- Year: 2007
- Presenter: Yavar
Chapter 10 of the book "Clinical Decision Support - The Road Ahead"
The chapter introduces a number of prediction methods that are currently in practical use for Clinical Decision Support. The authors explain what models are used (or not used) in the community, and how models are evaluated. In particular, "simple, understandble models" (such as linear and logidtic regression) are preffered to "Sophisticated models" such as SVMs and neural networks.
Here is a short summary of the provided case studies:
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APACHE (Acute Physiology and Chronic Health Evaluation) series of models
Predict the individual patient's risk of hospital death, based on a variety of physiological variables (using linear regression)
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Cardiovascular Disease Risk
Estimates the risk of developing future heart disease, based on most recent 10-year heart disease data from Framingham cohort (using linear regression)
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Pneumonia Severity of Illness Index
Predicts the risk of death within 30 days for adult patients with pneumonia. The authors claim that using this model, 26 to 31 percent of patients can be treated safely as outpatients, which would result in a savings of more than 1.2 Billion dollars per year in US.