By Eta S. Berner

Construction at the good fortune of the former versions, this absolutely up-to-date booklet once more brings jointly all over the world specialists to demonstrate the underlying technology and daily use of choice aid structures in medical and academic settings.

Topics mentioned include:

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

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Extra resources for Clinical decision support systems : theory and practice

Example text

Measuring the impact of diagnostic decision support on the quality of decision-making: development of a reliable and valid composite score. J Am Med Inform Assoc 2003;10:563–572. 34. Berner ES, Maisiak RS, Cobbs CG, Taunton OD. Effects of a decision support system on physicians’ diagnostic performance. J Am Med Inform Assoc 1999;6: 420–427. 35. Berner ES, Maisiak RS. Influence of case and physician characteristics on perceptions of decision support systems. J Am Med Inform Assoc 1999;6:428–434. 36.

Input is often constrained by controlled vocabularies or limitations in temporal expression of clinical features. Reasoning engines take on different designs, but their operation is usually transparent to the user of a CDSS. Knowledge bases contain data from which the reasoning engine takes rules, probabilities, and other constructs required to convert the input into output. Output can take many forms, including a differential diagnosis list or simply a probability of a particular diagnosis. Nonknowledge-based systems use techniques of machine learning to generate methods of turning input into meaningful output, regardless of an explicit representation of expert knowledge.

In this population, one’s chance of having one disease is unaffected by the presence of the other disease. In medicine, we are often faced with the question of the likelihood of two interrelated events occurring simultaneously in a patient. The case of a diagnostic test and the disease it is supposed to test for is a good example: what is the probability of an abnormal chest radiograph and pneumonia occurring in the same patient simultaneously? The question asks for this probability: Pr(pneumonia AND abnormal CXR).

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