Linked Life Data

9194856

Source:http://linkedlifedata.com/resource/pubmed/id/9194856

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
1997-7-15
pubmed:abstractText
The aim of the present work is to show that decision tree induction algorithms are a useful tool for extracting reliable information from data series, with the objective of assisting pathologists in identifying specific diagnostic and prognostic markers in various types of tumor pathologies. In terms of accuracy, we show that the decision tree technique exceeds other more sophisticated techniques, such as multilayer neural networks. Furthermore, because of the case with which decision tree results can be interpreted (logical classification rules), new methodologies can be readily developed to further assist in analyzing complex data that mix heterogeneous features. In this paper, we illustrate such capabilities in the context of different complex diagnostic and/or prognostic problems in tumor pathology relating to bladder, astrocytomas, and adipose tissues.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0023-6837
pubmed:author
pubmed:issnType
Print
pubmed:volume
76
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
799-808
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1997
pubmed:articleTitle
Decision tree induction: a useful tool for assisted diagnosis and prognosis in tumor pathology.
pubmed:affiliation
Laboratory of Histology, Faculty of Medicine, Erasme Hospital, Brussels, Belgium.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't