Download Artificial Intelligence in Medicine: 12th Conference on by Carol Friedman (auth.), Carlo Combi, Yuval Shahar, Ameen PDF

By Carol Friedman (auth.), Carlo Combi, Yuval Shahar, Ameen Abu-Hanna (eds.)

This publication constitutes the refereed complaints of the twelfth convention on man made Intelligence in drugs in Europe, AIME 2009, held in Verona, Italy in July 2009.

The 24 revised lengthy papers and 36 revised brief papers offered including 2 invited talks have been conscientiously reviewed and chosen from one hundred forty submissions. The papers are prepared in topical sections on agent-based structures, temporal facts mining, computer studying and information discovery, textual content mining, average language processing and iteration, ontologies, determination aid structures, purposes of AI-based picture processing innovations, protocols and instructions, in addition to workflow systems.

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Extra resources for Artificial Intelligence in Medicine: 12th Conference on Artificial Intelligence in Medicine, AIME 2009, Verona, Italy, July 18-22, 2009. Proceedings

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I−1 ai−1 + βi+1 ai+1 + . . + βm am , by considering the distinct data samples in w and w respectively. The couple of regression models for which information loss in predicting a parameter ai is the lowest are selected. The subset Ea = {aj ∈ P − {ai }} such that the difference between βj and βj is greater than an automatically determined threshold σj is selected and associated with the time window w : [tF , tL ] of the detected event. The set Ea is filtered in order to remove those parameters for which no interval of values can be generated which discriminates samples in w from samples in w .

G. the time series of systolic pressure values). In order to get a uniform representation of these data as temporal sequences of events, the clinical data needed first to undergo to a pre-processing procedure. In particular, we exploited knowledge-based Temporal Abstractions (TAs) to shift from a time point quantitative representation of the time series to a qualitative interval-based description of the available data [4]. To this end both state and trend TAs were exploited in the procedure. State detection was applied to discretize clinical continuous values in state intervals.

Table 3 reports also the support of the discovered events: for instance, e1 occurs in a set of three events out of a total set of three candidates (3/3) detected in [t3998 ,t4018 ]. Discussion. In this paragraph, we try to evaluate the results through a qualitative and quantitative analysis, although we are aware that a deeper discussion can be performed in conjunction with clinicians. 34 C. Loglisci and D. Malerba Table 3. Events detected for the transitions S22 → S23 . The columns report the specification of the events identified in the column # Event.

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