Document Detail


eXiT*CBR: A framework for case-based medical diagnosis development and experimentation.
MedLine Citation:
PMID:  20971621     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
OBJECTIVE: Medical applications have special features (interpretation of results in medical metrics, experiment reproducibility and dealing with complex data) that require the development of particular tools. The eXiT*CBR framework is proposed to support the development of and experimentation with new case-based reasoning (CBR) systems for medical diagnosis.
METHOD: Our framework offers a modular, heterogeneous environment that combines different CBR techniques for different application requirements. The graphical user interface allows easy navigation through a set of experiments that are pre-visualized as plots (receiver operator characteristics (ROC) and accuracy curves). This user-friendly navigation allows easy analysis and replication of experiments. Used as a plug-in on the same interface, eXiT*CBR can work with any data mining technique such as determining feature relevance.
RESULTS: The results show that eXiT*CBR is a user-friendly tool that facilitates medical users to utilize CBR methods to determine diagnoses in the field of breast cancer, dealing with different patterns implicit in the data.
CONCLUSIONS: Although several tools have been developed to facilitate the rapid construction of prototypes, none of them has taken into account the particularities of medical applications as an appropriate interface to medical users. eXiT*CBR aims to fill this gap. It uses CBR methods and common medical visualization tools, such as ROC plots, that facilitate the interpretation of the results. The navigation capabilities of this tool allow the tuning of different CBR parameters using experimental results. In addition, the tool allows experiment reproducibility.
Authors:
Beatriz López; Carles Pous; Pablo Gay; Albert Pla; Judith Sanz; Joan Brunet
Publication Detail:
Type:  Journal Article     Date:  2010-10-25
Journal Detail:
Title:  Artificial intelligence in medicine     Volume:  51     ISSN:  1873-2860     ISO Abbreviation:  Artif Intell Med     Publication Date:  2011 Feb 
Date Detail:
Created Date:  2011-03-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8915031     Medline TA:  Artif Intell Med     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  81-91     Citation Subset:  IM    
Copyright Information:
Copyright © 2010 Elsevier B.V. All rights reserved.
Affiliation:
Control Engineering and Intelligent Systems Research Group, Universitat de Girona, Campus Montilivi, edifice P4, 17071 Girona, Spain; Girona Biomedical Research Institute, Av. de França s/n, 17007 Girona, Spain.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Cotterillia bromelicola nov. gen., nov. spec., a gonostomatid ciliate (Ciliophora, Hypotricha) from ...
Next Document:  Phytic acid protects porcine intestinal epithelial cells from deoxynivalenol (DON) cytotoxicity.