Fifth Workshop on CBR in the Health Sciences 1 Fifth Workshop on Case- Based Reasoning in the Health...

Fifth Workshop on CBR in the Health Sciences

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Fifth Workshop on Case-Based Reasoning in the

Health Sciences

Isabelle BichindaritzUniversity of Washington, Tacoma, Washington, USA

[email protected]

Stefania MontaniUniversity of Piemonte Orientale, Italy

[email protected]


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Workshop Stats

• Papers accepted: 10 papers

• Attendees: 19 participants

• Good news !!!


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Workshop Goals

• Provide a forum for identifying important contributions and opportunities for research on the application of CBR to the Health Sciences

• Promote the systematic study of how to apply CBR to the Health Sciences

• Showcase applications of CBR in the Health Sciences


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A CBR Solution for Missing Medical Data

Olga Vorobieva and Rainer Schmidt

Institute for Medical Informatics and Biometry University of Rostock, Germany

Alexander Rumiantzev

Pavlov State Medical University, St.Petersburg, Russia


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Summary• Application domain

dialysis medicineeffects of fitness on dialysis

• System contextISOR, a CBR system that explains the exceptional cases – those for which fitness does not improve renal function

• Task / problem addressedrestoration of missing data

• Research hypothesiscase-based reasoning can be applied to restore missing data in a dataset/case base

• Main contributionsynergy between CBR and statistics (statistical modeling).


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A Case-Based Reasoning Approach A Case-Based Reasoning Approach to Dose Planning in Radiotherapyto Dose Planning in Radiotherapy

Xueyan Song1, Sanja Petrovic1, and Santhanam Sundar 2

1Automated Scheduling, Optimisation and Planning GroupSchool of Computer Science

University of Nottingham, UK

2Dept. of Oncology, City Hospital Campus, Nottingham University Hospitals NHS Trust, Nottingham, UK


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dose planning in radiotherapy for prostate cancer

• System contexttrade-off between the benefit in terms of cancer control and the risk in terms of harmful side effects to neighboring tissues

• Task / problem addressedplanning problem – designing a radiotherapy dose planning

• Research hypothesiscase-based reasoning can be applied to propose dose plans

• Main contributionfuzzy representation of attribute values and similarity measurefusion of similar cases by Dempster-Shafer theory.


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On-Line Domain Knowledge Management for

Case-Based Medical Recommendation

Amélie Cordier1,Béatrice Fuchs1,Jean Lieber2, and Alain Mille1

1LIRIS CNRS, UMR 5202, Université Lyon 1, INSA Lyon, Université Lyon 2, ECL

43, bd du 11 Novembre 1918, Villeurbanne Cedex, France,{Amelie.Cordier, Beatrice.Fuchs, Alain.Mille}@liris.cnrs.fr

2LORIA (UMR 7503 CNRS–INRIA–Nancy Universities),BP 239, 54506 Vandoeuvre-lès-Nancy, France

[email protected]


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breast cancer treatment

• System contextKasimir is a knowledge management and decision-support system in oncology focusing on case-based protocol treatment recommendations

• Task / problem addressedplanning problem – recommending a treatment plan based on a protocol

• Research hypothesesconservative adaptation is recommended for adapting a protocol to a new case through case-based reasoningnew domain knowledge can be acquired by analysis of failures

• Main contributionimprovement of adaptationmethod for learning from failures of the case-based reasoning.


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Concepts for Novelty Detection and Handling

based on Case-Based Reasoning

Petra PernerPetra PernerInstitute of Computer Vision and applied Computer Sciences, IBaIInstitute of Computer Vision and applied Computer Sciences, IBaI


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Hep-2 cell image interpretation

• System contextcase-based image interpretation

• Task / problem addressedclassification problem – improve recognition of over 30 different nuclear and cytoplasmic patterns when patterns change over time or new patterns emerge

• Research hypothesiscase-based reasoning can be applied to the problem of novelty detection and also of concept drift

• Main contributionnovel application for CBR: detecting novelty, detecting concept drift.


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Similarity of Medical Cases in Health Care

Using Cosine Similarity and Ontology

Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéelevon Schéele

Mälardalen University, Department of Computer Science and Mälardalen University, Department of Computer Science and ElectronicsElectronics

PO Box 883 SE-721 23, Västerås, SwedenPO Box 883 SE-721 23, Västerås, Sweden{firstname.lastname}@mdh.se{firstname.lastname}@mdh.se


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any medical domain

• System contextelectronic medical records

• Task / problem addressedretrieval task – finding similar cases represented with structured and semi-structured data

• Research hypothesisa hybrid similarity measure based on combining the cosine similarity measure, an ontology, and the nearest neighbor method permit to successfully retrieve similar cases

• Main contributionsynergy between case-based reasoning and information retrieval.


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Towards Case-Based Reasoning for DiabetesManagement

Cindy Marling1, Jay Shubrook2 and Frank Schwartz2

1 School of Electrical Engineering and Computer ScienceRuss College of Engineering and TechnologyOhio University, Athens, Ohio 45701, USA

[email protected] Appalachian Rural Health Institute, Diabetes and Endocrine Center

College of Osteopathic MedicineOhio University, Athens, Ohio 45701, USA

[email protected], [email protected]


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type I diabetes management

• System contextreal-time monitoring of glucose level through insulin pump

• Task / problem addressedtreatment planning – adjusting insulin dosage

• Research hypothesiscase-based reasoning can adjust insulin dosage in real timecases required for the future CBR system can be acquired through an online Web-based interface

• Main contributionplanning the development of a case-based reasoning system for automatic type I diabetes monitoring.


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Hypothetico-Deductive Case-Based Reasoning

David McSherry

School of Computing and Information Engineering,University of Ulster, Northern Ireland


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contact lenses classification

• System contextconversational CBR

• Task / problem addressedclassification problem – recommending type of contact lenses

• Research hypothesisa hypothetico-deductive CBR approach to test selection can minimize the number of tests required to confirm a hypothesis proposed by the system or user

• Main contributionsynergy between case-based reasoning and hypothetico-deductive reasoningexplanations in CBR.


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Other Papers Summaries• Case-based Reasoning for managing non-

compliance with clinical guidelines, Stefania Montani, University of Piemonte Orientale, Alessandria, Italy A CBR system able to

– Retrieve similar past episodes (cases) of non-compliance to guidelines, to be suggested to the physician

– Learn more general indications from ground non-compliance cases, adoptable for a formal GL revision by an experts committee

• CBR for Temporal Abstractions Configuration in Haemodyalisis, Leonardi Giorgio, Bottrighi Alessio, Portinale Luigi, Montani Stefania, University of Piemonte Orientale, Alessandria, ItalyA CBR system able to choose the appropriate parameters for the configuration of temporal abstractions in medical domain of haemodyalisis


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Other Papers Summaries

• Prototypical Cases for Knowledge Prototypical Cases for Knowledge Maintenance in Biomedical CBR, Maintenance in Biomedical CBR, Isabelle Bichindaritz, University of Washington, Tacoma, WA, USAPrototypical cases have served various purposes in biomedical CBR systems, among which to organize and structure the memory, to guide the retrieval as well as the reuse of cases, and to serve as bootstrapping a CBR system memory when real cases are not available in sufficient quantity and/or quality. Knowledge maintenance is yet another role that these prototypical cases can play in biomedical CBR systems


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Discussion • Trends and issues

– Integration of CBR with electronic patient records and/or in clinical practice (Begum et al., Marling et al.)

– Importance of prototypical cases (Bichindaritz)– Incompleteness / non-reliability of cases or CBR

system knowledge (Vorobieva et al., Cordier et al., Bichindaritz)

– Novel domains of applications for CBR (Perner, Leonardi et al., Montani)

– Need for synergy with other AI methods (Song et al., McSherry)


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Discussion • Pearls of wisdom

– Remember Occam’s razor – introducing complexity in CBR should be carefully justified

– Knowledge in medical cases / domain knowledge is often questionable – finding methods for dealing with this reality is essential for the development of CBR in biomedical domains

– CBR can be promoted as the methodology of choice for evidence gathering in evidence-based medicine


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Future Plans

• A second special issue on CBR in the Health Sciences, based on papers from this Fifth Workshop on CBR in the Health Sciences is going to be published in Computational Intelligence.

• The Web-site (version 1.beta) and mailing list for our research group are now live:http://www.cbr-health.orghttp://www.cbr-biomed.org

http://www.cbr-health.org/

http://www.cbr-biomed.org/


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Future Plans

• A book on CBR in the Health Sciences is in preparation. Please contact us should you want to contribute – we may also contact you !

Fifth Workshop on CBR in the Health Sciences 1 Fifth Workshop on Case- Based Reasoning in the Health...

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