Titlebook: Artificial Intelligence in Medicine; 5th Conference on Ar Pedro Barahona,Mario Stefanelli,Jeremy Wyatt Conference proceedings 1995 Springer

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Dynamic propagation in causal probabilistic networks with instantiated variables,in, thereby reducing the calculation time by several orders of magnitude compared to using Hugin alone. The application of the dynamic propagation in a Diabetes Advisory System, DIAS, for giving advise on insulin dose adjustment in insulin dependent diabetes mellitus, is described as an example.

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Consequence

Conference proceedings 1995ons on medical records, temporal reasoning and simulation, probabilistic models, patient management and therapy planning, evaluation of knowledge-based systems, diagnostic support systems, models for clinical information systems, and neural networks and image interpretation.

LAIR

https://doi.org/10.1007/978-3-540-78861-4igned for medical text analysis and understanding. In particular, we discuss the principles which guide the analysis of medical jargon using semantic considerations, coupled with syntactic information when needed. As a result of the flexibility of the system, a medical application developed in the context of the AIM project HELIOS-2 is presented.

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Pillory

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Jim Pierce,Michael D. Smith,Trevor Mudgefrom 13 of 392 (3.3%) cases effected the knowledge base. Only two of the evaluator‘s comments concerned the style of the system‘s displayed output. The system was able to maintain high quality interpretive reports, appropriate to patient care, despite the expected differences in subjective morphologic interpretation between observers.

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