Mining data from a knowledge management perspective: An application to outcome prediction in patients with resectable hepatocellular carcinoma

Riccardo Bellazzi, Ivano Azzini, Gianna Toffolo, Stefano Bacchetti, Mario Lise

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents the use of data mining tools to derive a prognostic model of the outcome of resectable hepatocellular carcinoma. The main goal of the study was to summarize the experience gained over more than 20 years by a surgical team. To this end, two decision trees have been induced from data: a model M1 that contains a full set of prognostic rules derived from the data on the basis of the 20 available factors, and a model M2 that considers only the two most relevant factors. M1 will be used to explicit the knowledge embedded in the data (externalization), while the model M2 will be used to extract operational rules (socialization). The models performance has been compared with the one of a Naive Bayes classifier and have been validated by the expert physicians. The paper concludes that a knowledge management perspective improves the validity of data mining techniques in presence of small data sets, coming from severe pathologies with relative low incidence. In these cases, it is more crucial the quality of the extracted knowledge than the predictive accuracy gained.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages40-49
Number of pages10
Volume2101
ISBN (Print)3540422943, 9783540422945
Publication statusPublished - 2001
Event8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001 - Cascais, Portugal
Duration: Jul 1 2001Jul 4 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2101
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001
CountryPortugal
CityCascais
Period7/1/017/4/01

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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