Download Clinical Decision Support Systems: Theory and Practice by Eta S. Berner EdD, FCMI, FHIMSS, Tonya J. La Lande (auth.), PDF

By Eta S. Berner EdD, FCMI, FHIMSS, Tonya J. La Lande (auth.), Eta S. Berner EdD, FACMI, FHIMSS (eds.)

Building at the good fortune of the 1998 variation, Clinical selection help structures: concept and Practice, moment variation, once more brings jointly all over the world specialists to demonstrate the underlying technology and day by day use of choice help systmes in medical and academic settings.

Writes the editor, "If used correctly, CDSS have the aptitude to alter the best way drugs has been taught and practiced." As medical determination aid structures (CDSS) achieve an more and more vital function within the supply of top of the range future health care, it turns into extra vital for the wellbeing and fitness care neighborhood to appreciate their use. this article is designed as a source for working towards clinicians, informaticians, academics and scholars alike, and offers the most up-tp-date, entire glance a the improvement and overview of medical selection help systems.

subject matters mentioned include:

-Mathematical Foundations of selection help Systems

-Design and Implementation Issues

-Ethical and criminal concerns in choice Support

-Clinical Trials of data Interventians

-Hospital-Based determination Support

-Real global Case reports

Eta S. Berner, EdD, is a Professor of future health Informatics on the collage of Alabama at Birmingham.

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Additional resources for Clinical Decision Support Systems: Theory and Practice

Example text

This is much like the AND function in Medline or the intersection function as applied to sets. The familiar “OR” function from our Medline program also has a mathematical meaning in combining probabilities. If we wanted to know how many patients in the above example had diabetes or hypertension (remember: this would also include those with both diseases in the usual mathematical sense of or), we would compute: Pr(diabetes OR hypertension) = Pr(diabetes) + Pr(hypertension) − Pr(diabetes AND hypertension).

By joining the concept “history of” with the concept of a particular bacterial pathogen, one successfully abstracts the temporal nature of this finding, which would be pertinent in the diagnosis of rheumatic fever or post-streptococcal glomerulonephritis. Note that no explicit definition of “recent” is part of this representation; if for some reason one needed to distinguish infection 2 weeks ago from infection 3 months ago, this abstraction would not suffice. Thus, there is a disadvantage to this simplification.

18. A. Spooner We were free to multiply these together, because in our hypothetical population, the likelihood of one disease occurring in an individual was independent of the other. In other words: Pr(hypertension) = Pr(hypertension | diabetes) and Pr(diabetes) = Pr(diabetes | hypertension). 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.

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