Clinical Decision Support Systems: Theory and Practice by Eta S. Berner

By Eta S. Berner

Development at the good fortune of the former variations, this totally up to date ebook once more brings jointly all over the world specialists to demonstrate the underlying technology and daily use of determination help structures in scientific and academic settings.

Topics mentioned include:

-Mathematical Foundations of determination help Systems
-Design and Implementation Issues
-Ethical and felony matters in choice Support
-Clinical Trials of knowledge Interventions
-Hospital-Based selection Support
-Real international Case Studies 

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Extra resources for Clinical Decision Support Systems: Theory and Practice

Sample text

A. 4 Bayes’ Rule If we have a patient with jaundice, how likely is it that he has hepatitis? ” We may not have this probability at our fingertips, but we might be able to find a slightly different probability more easily: Pr( jaundice | hepatitis), which is, simply, the probability of jaundice given the presence of hepatitis. The latter probability could be found by studying a series of patients with proven hepatitis (it would be easy to get this data by looking up diagnosis codes in the medical records department) and computing the percentage of these patients who present with jaundice.

This is much like the AND function in PubMed or the interaction function as applied to sets. The familiar “OR” function from our PubMed program also has a mathematical meaning in combining probabilities. 72. Conditional probability is another type of probability often used in medicine. A conditional probability is the probability of an event (or the probability of the truth of a statement) given the occurrence of another event (or the truth of another statement). The most familiar case of conditional probability in medicine arises in the interpretation of diagnostic tests.

Pr(B) was high), then our estimation of the probability of hepatitis as a specific diagnosis would be lowered. , Pr(A | B) was 1 or near to it), then our hepatitis diagnosis would be greatly increased. By using numerical estimates of the probability of diseases, findings, and conditional probabilities, Bayes’ rule can help make medical decisions. One might imagine a simple CDSS in which one enters a single symptom and receives the probability of the presence of a disease given that symptom. A problem arises when one wishes to get disease probabilities given multiple symptoms.

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