eXiT*CBR: A framework for case-based medical diagnosis development and experimentation

Artificial Intelligence in Medicine - Tập 51 - Trang 81-91 - 2011
Beatriz López1,2, Carles Pous1,2, Pablo Gay1, Albert Pla1, Judith Sanz3, Joan Brunet2,4
1Control Engineering and Intelligent Systems Research Group, Universitat de Girona, Campus Montilivi, edifice P4, 17071 Girona, Spain
2Girona Biomedical Research Institute, Av. de França s/n, 17007 Girona, Spain
3Unitat de Consell Genètic en Càncer Hereditari, Servei d’Oncologia Mèdica, Hospital de la Santa Creu i Sant Pau, Sant Antoni Maria Claret, 167, 08025 Barcelona, Spain
4Medical Oncology Department, Catalan Institute of Oncology, Av. França s/n, 17007 Girona, Spain

Tài liệu tham khảo

Bergmann, 1997, Ingredients for developing a case-based reasoning methodology, 49 Bergmann, 1998, Methodology for building CBR applications, 299 Jaczynski, 1997, A framework for the management of past experiences with time-extended situations, 32 Aitken S. CBR shell java-v1.0. Available from: http://www.aiai.ed.ac.uk/project/cbr/cbrtools.html [accessed 08.04.10]. Bogaerts S, Leake D. IUCBRF: a framework for rapid and modular case-based reasoning system development. Technical Report TR617, Computer Science Department, Indiana University; 2005. Arcos JL. cF development framework. Available from: http://www.iiia.csic.es/∼arcos/cbr.html [accessed 29.04.10]. Diaz-Agudo, 2007, Building CBR systems with jCOLIBRI, Science of Computer Programming, 69, 68, 10.1016/j.scico.2007.02.004 Bichindaritz, 2006, Case-based reasoning in the health sciences: what's next?, Artificial Intelligence in Medicine, 127, 10.1016/j.artmed.2005.10.008 Bichindaritz, 2008, Special issue on case-based reasoning in the health sciences, Applied Intelligence, 207, 10.1007/s10489-007-0100-0 López B, Pous C. Applications in medical dababases. In: Saita L, editor. BluePrint in ubiquitous knowledge discovery KDUbic. European project IST-6FP-021321 Coordination Action document; 2007. p. 36–39. Available from: http://www.kdubiq.org/ [accessed 16.06.09]. Cios, 2002, Uniqueness of medical data mining, Artificial Intelligence in Medicine, 26, 1, 10.1016/S0933-3657(02)00049-0 Fawcett, 2006, An introduction to ROC analysis, Pattern Recognition Letters, 27, 861, 10.1016/j.patrec.2005.10.010 Swets, 2000, Better decisions through science, Scientific American Drummond, 2000, Explicitly representing expected cost: an alternative to ROC representation, 198 Dietterich, 2004, Three challenges for machine learning research Aamodt, 1994, Case-based reasoning: foundational issues, methodological variations, and system approaches, AI Communications, 7, 39, 10.3233/AIC-1994-7104 Weiss, 1999 Aggour, 2003, SOFT-CBR: a self-optimizing fuzzy tool for case-based reasoning, 5 Francisco Núñez HF. Feature weighting in plain case-based reasoning. PhD thesis, Technical University of Catalonia, Spain; 2004. Martinez T. Selecció d’atributs i manteniment de la les base de casos per a la diagnosi de falles. Master's thesis, Universitat de Girona, Spain; 2007. Wilson, 1997, Improved heterogeneous distance functions, Journal of Artificial Intelligence Research, 6, 1, 10.1613/jair.346 Torra, 2007 Zhang, 2005, “Missing is useful”: missing values in cost-sensitive decision trees, IEEE Transactions on Knowledge and Data Engineering, 17, 1689, 10.1109/TKDE.2005.188 Montani, 2008, Exploring new roles for case-based reasoning in heterogeneous ai systems for medical decision support, Applied Intelligence, 28, 275, 10.1007/s10489-007-0046-2 Bilska-Wolak, 2002, Development and evaluation of a case-based reasoning classifier for prediction of breast biopsy outcome with BI-RADS lexicon, Medical Physics, 29, 2090, 10.1118/1.1501140 Hammond, 1990, Explaining and repairing plans that fail, Artificial Intelligence, 45, 173, 10.1016/0004-3702(90)90040-7 Aha, 1991, Instance based learning algorithms, Machine Learning, 6, 37, 10.1007/BF00153759 Wilson, 2000, Reduction techniques for instance-based learning algorithms, Machine Learning, 38, 257, 10.1023/A:1007626913721 Schulz, 1999, CBR-works: a state-of-the-art shell for case-based application building, 3 Plaza, 2005, Distributed case-based reasoning, The Knowledge Engineering Review, 20, 261, 10.1017/S0269888906000683 Nin, 2008, On the disclosure risk of multivariate microaggregation, Data & Knowledge Engineering, 67, 399, 10.1016/j.datak.2008.06.014 Asuncion A, Newman DJ. UCI machine learning repository. Irvine, CA: University of California, School of Information and Computer Science; 2007. Available from: http://www.ics.uci.edu/mlearn/MLRepository.html [accessed 08.04.10]. Mangasarian, 1990, Cancer diagnosis via linear programming, SIAM News, 23, 1 Pous, 2008, Modeling reuse on case-base reasoning with application to breast cancer diagnosis, 322 Jacobi, 2009, Differences and similarities in breast cancer risk assessment models in clinical practice: which model to choose?, Breast Cancer Research and Treatment, 115, 381, 10.1007/s10549-008-0070-x Stahl, 2008, Rapid prototyping of CBR applications with the open source tool myCBR, 615 Witten, 2005 Marling, 2008, Case-based decision support for patients with type 1 diabetes on insulin pump therapy, 325 Pous C. Case-based reasoning as an extension of fault dictionary methods for linear electronic analog circuits diagnosis. PhD thesis, University of Girona, Spain; 2004. Lieber, 2008, Modeling adaptation of breast cancer treatment decision protocols in the KASIMIR project, Applied Artificial Intelligence, 261 Vorobieva O, Schmidt R. CBR investigation of therapy inefficacy. In: 2nd workshop on CBR in the health sciences, Madrid, Spain; 2004. Available from: http://www.cbr-biomed.org/jspw/workshops/ECCBR04.jsp [accessed 17.06.09]. Tax, 2002, Using two-class classifiers for multiclass classification, vol. 2, 124 López, 2009, Boosting CBR agents with genetic algorithms, 195 Armengol, 2003, Relational case-based reasoning for carcinogenic activity prediction, Artificial Intelligence Review, 20, 121, 10.1023/A:1026076312419 Aha, 1998, Supporting conversational case-based reasoning in an integrated reasoning framework, Applied Intelligence, 14, 9, 10.1023/A:1008346807097 Lenz, 1998, Textual CBR, 115