Download Artificial Intelligence in Medicine: Joint European by Gianpaolo Molino (auth.), Werner Horn, Yuval Shahar, Greger PDF

By Gianpaolo Molino (auth.), Werner Horn, Yuval Shahar, Greger Lindberg, Steen Andreassen, Jeremy Wyatt (eds.)

ISBN-10: 3540487204

ISBN-13: 9783540487203

ISBN-10: 354066162X

ISBN-13: 9783540661627

This publication constitutes the refereed court cases of the Joint ecu convention on man made Intelligence in drugs and scientific choice Making, AIMDM'99, held in Aalborg, Denmark, in June 1999.
The 27 complete papers and 19 brief papers awarded within the publication including 4 invited papers have been chosen from ninety submissions. The papers are equipped in topical sections on instructions and protocols; selection help platforms, knowledge-based structures, and cooperative platforms; model-based platforms; neural nets and causal probabilistic networks; wisdom illustration; temporal reasoning; computer studying; typical language processing; and snapshot processing and laptop aided layout.

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Extra resources for Artificial Intelligence in Medicine: Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, AIMDM'99 Aalborg, Denmark, June 20–24, 1999 Proceedings

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7 Workflows and Workflow Management Systems While medical processes describe the activities of a medical team in a comprehensive manner for the purpose of defining the most effective and efficient patients’ management, workflows focus on the behavioral aspects of medical work with regard to a possible support of their execution through information technology. A workflow is an activity involving the coordinated execution of multiple tasks performed by different processing entities [19]. The Workflow Management Coalition (WfMC) defined a basic set of workflows’ building blocks: activities to execute tasks, transitions between activities, participants and application performing activities, and workflow relevant data [28].

In many clinical domains, a final diagnosis is not always the main goal of data abstraction. , a major diagnosis) is known. The goal is then to abstract the clinical data, which often is acquired or recorded as time-stamped measurements, into higher-level concepts, which often hold over time periods. , planning of therapy or summarization of a patient’s record). Thus, the goal is often to create, from time-stamped input data, such as hematological measurements, interval-based temporal abstractions, such as "bone-marrow toxicity grade 2 or more for 3 weeks in the context of administration of a prednisone/azathioprine protocol for treating patients who have chronic graftversus-host disease, and complication of bone-marrow transplantation" and more complex patterns, involving several intervals (Figure 2).

Extremely high blood pressures for the past 8 months in the context of treatment of hypertension"). , only particular types of output might be required). The output abstractions should also be sensitive to the context in which they were created. 3. , a laboratory result from last Tuesday arrives today). Thus, the past can change our view of the present. This phenomenon has been called a view update [7]. Furthermore, new data should enable us to reflect on the past; thus, the present (or future) can change our interpretation of the past, a property referred to as hindsight [8].

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Artificial Intelligence in Medicine: Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, AIMDM'99 Aalborg, Denmark, June 20–24, 1999 Proceedings by Gianpaolo Molino (auth.), Werner Horn, Yuval Shahar, Greger Lindberg, Steen Andreassen, Jeremy Wyatt (eds.)


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