Download Artificial Intelligence in Medicine: 8th Conference on by Enrico Coiera (auth.), Silvana Quaglini, Pedro Barahona, PDF

By Enrico Coiera (auth.), Silvana Quaglini, Pedro Barahona, Steen Andreassen (eds.)

This ebook constitutes the refereed lawsuits of the eighth convention on synthetic Intelligence in medication in Europe, AIME 2001, held in Cascais, Portugal in July 2001. The 31 revised complete papers awarded including 30 posters and invited papers have been conscientiously reviewed and chosen from seventy nine submissions. one of the issues addressed of their context on clinical info processing are wisdom administration, computer studying, facts mining, determination aid platforms, temporal reasoning, case-based reasoning, making plans and scheduling, usual language processing, machine imaginative and prescient, photograph and sign interpretation, clever brokers, telemedicine, careflow structures, and cognitive modeling.

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Extra resources for Artificial Intelligence in Medicine: 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001 Cascais, Portugal, July 1–4, 2001, Proceedings

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Quaglini, P. Barahona, and S. ): AIME 2001, LNAI 2101, pp. 20–29, 2001. c Springer-Verlag Berlin Heidelberg 2001 Prototype Selection and Feature Subset Selection 21 systemic circulation. This is caused by both an obstruction to outflow in the portal flow as well as an increased mesenteric flow. In the western world, cirrhosis of the liver accounts for approximately 90% of the patients. e. varices, encephalopathy, hypersplenism, ascites), bleeding from gastro-oesophageal varices is a significant cause of early mortality (approximately 30 − 50% at the first bleed) [2].

This value can then be compared against the clustered value obtained from the sliding aggregator frequency total for the organism. The user defines a threshold, and if the sliding aggregator total is greater than the threshold times the expected frequency for the given organism, then this is noted as an anomaly and presented to the user for inspection. The following is a simple example. 8 (80%) !! Possible Anomaly Detected !! Sequence Frequency - 13 System Frequency 7 Organism: KPNE R RS SSSS S SSS S 0:0:0:0:0:0:0:0:0:0:0:0:0:1:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:1:0:0:0:0:0, sum = 2 0:0:0:0:0:0:0:0:0:0:0:0:0:1:0:1:0:0:0:0:0:0:1:0:0:2:1:1:0:3:0:1:1:0:1, sum = 13 This is a fairly intuitive way of determining whether or not a specific organism has exhibited anomalous bunching.

ORG_ASP has 4 possible values. Although a reasonable amount of data is collected (bacteria from around 100 infections are identified and recorded each day), the fact that there are so many possibilities within each strain of bacterium means that the data is rather sparse. Hence, any direct attempt at clustering bacterial strains based on a symbolic comparison of their ORG_TYPE and ORG_ASP will lead to a large number of small clusters. This problem is further compounded by the fact that not all of the possible antibiotics are tested on every sample.

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