Helping physicians to organize guidelines within conceptual hierarchies

Diego Sona, Paolo Avesani, Robert Moskovitch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Clinical Practice Guidelines (CPGs) are increasingly common in clinical medicine for prescribing a set, of rules that a physician should follow. Recent interest, is in accurate retrieval of CPGs at the point of care. Examples are the CPGs digital libraries National Guideline Clearinghouse (NGC) or Vaiclurya, which are organized along predefined concept hierarchies. In this case, both browsing and concept-based search can be applied. However, mandatory sl.ep in enabling both ways to CPGs retrieval is manual classification of CPGs along the concepts hierarchy, which is extremely time consuming. Supervised learning approaches are usually not satisfying, since commonly too few or no CPGs are provided as training set for each class. In this paper we apply TaxSOM for multiple classification. TaxSOM is an unsupervised model that supports the physician in the classification of CPGs along the concepts hierarchy, even when no labeled examples are available. This model exploits lexical and topological information on the hierarchy to elaborate a classification hypothesis for any given CPG. We argue that such a kind of urisupervised classification can support, a physician to classify CPGs by recommending the most probable classes. An experimental evaluation on various concept, hierarchies with hundreds of CPGs and categories provides the empirical evidence of the proposed technique.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Proceedings
PublisherSpringer Verlag
Pages141-145
Number of pages5
ISBN (Print)3540278311, 9783540278313
DOIs
StatePublished - 1 Jan 2005
Event10th Conference on Artificial Intelligence in Medicine, AIME 2005 - Aberdeen, United Kingdom
Duration: 23 Jul 200527 Jul 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3581 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th Conference on Artificial Intelligence in Medicine, AIME 2005
Country/TerritoryUnited Kingdom
CityAberdeen
Period23/07/0527/07/05

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