A computer-aided Clinical Decision Support System (CDSS) for diagnosis and treatment often plays a vital role and brings essential benefits for clinicians. Such a CDSS could function as an expert for a less experienced clinician or as a second option/opinion of an experienced clinician to their decision making task. This paper presents 3 clinical Decision Support Systems as an overview of case-based reasoning (CBR) research and development. Two medical domains are used here for the case study 1) CDSS for stress diagnosis 2) CDSS for stress treatment and 3) CDSS for post-operative pain treatment. The observation shows the current developments; future direction and pros and cons of the CBR approach. Moreover; the paper shares the experiences of developing 3CDSS in medical domain.
[1] Aamodt A. and Plaza E.; Case-based reasoning: Foundational issues; methodological variations; and system approaches; AICom - Artificial Intelligence Communications; IOS Press; Volume 7; Issue 1; pp 39-59; 1994.
[2] AAPB; The association for applied psychophysiology and biofeedback; http://www.aapb.org/; Last referred November 2011.
[3] Ahmed M. U.; Westin J.; Nyholm D.; Dougherty M. and Groth T.; A fuzzy rule-based decision support system for duodopa treatment in Parkinson; Proceedings of the 23rd annual workshop of the Swedish Artificial Intelligence Society; Umeå; May 10-12; Ed(s): Eklund P.; Minock M. and Lindgren H.; pp 45-50; 2006.
[4] Alayón S.; Robertson R.; Warfield S.K.; and Ruiz-Alzola J.; A fuzzy system for helping medical diagnosis of malformations of cortical development; Biomedical Informatics; Volume 40; Issue 3; pp 221-235; 2007.
[5] Begum S.; Ahmed M.U.; Funk P.; Xiong N. and Scheele B. V.; Using calibration and fuzzification of cases for improved diagnosis and treatment of stress; The Proceedings of the 8th European Workshop on Case-based Reasoning; pp 113-122; 2006.
[6] Begum S.; Ahmed M.U.; Funk P.; Xiong N. and Schéele B.V; A case-based decision support system for individual stress diagnosis using fuzzy similarity matching; Computational Intelligence; Blackwell Publishing; Volume 25; Nr 3; pp 180-195; 2009.
[7] Bradburn C. and Zeleznikow J.; The application of case-based reasoning to the tasks of health care planning. In proceedings of topics in case-based reasoning: 1st European workshop; EWCBR- 93; Springer; Berlin; pp 365–378; 1994.
[8] Buchanan B.G. and Shortliffe E.H.; Rule-based expert systems: The MYCIN experiments of the Stanford heuristic programming project; Reading Mass. : Addison-Wesley; 1984.
[9] Casas F.; Orozco A.; Smith W. A.; De Abreu-Garcia J. A. and Durkin J.; A fuzzy system cardio pulmonary bypass rotary blood pump controller; Expert Systems with Applications; Volume 26; issue 3; pp 357-361; 2004.
[10] Charles E. K; Linda M. R.; Katherine A. S. and Peter H.; Construction of a Bayesian network for mammographic diagnosis of breast cancer; Computers in Biology and Medicine; Volume 27; Issue 1; pp 19-29 January 1997.
[11] Charlton E.; The management of postoperative pain; update in anaesthesia; World Federation of Societies of Anaesthesiologists; Issue 7; Ar. 2; 1997; http://www.nda.ox.ac.uk/wfsa/html/u07/u07_003.htm; Last referred November 2011.
[12] Clark W.C.; Yang J.C.; Tsui S.L.; Ng K.F. and Clark S.B.; Unidimensional pain rating scales: a multidimensional affect and pain survey (MAPS) analysis of what they really measure; Pain; Volume 98; Issue 3; pp 241-247; 2002.
[13] Cleeland C.S. and Ryan K.M.; Pain assessment: Global use of the brief pain inventory; Annals of the Academy of Medicine Singapore; Volume 23; Issue 2; pp 129–138; 1994.
[14] Corchado J.M.; Bajo J. and Abraham A.; GERAmI: Improving healthcare delivery in geriatric residences; IEEE Intelligent Systems; Volume 23; Issue 2; pp 19-25. ISSN: 1541-1672; 2008.
[15] D’Aquin M.; Lieber J. and Napoli A.; Adaptation knowledge acquisition: A case study for case-based decision support in oncology; Computational Intelligence; Volume 22; Issue 3-4; pp 161 – 176; 2006.
[16] Demirali J.; Reason: An introduction to critical thinking; 1st edition. ISSBN 978-1-4583-7749-4; Analogical Reasoning Institute; 2011.
[17] De Paz J.F.; Rodriguez S.; Bajo J. and Corchao J.M.; Case-based reasoning as a decision support system for cancer diagnosis: A case study; International Journal of Hybrid Intelligent Systems (IJHIS); IOS press; Volume 6; Issue 2; pp 97-110; 2009.
[18] Díaz F.; Fdez-Riverola F. and Corchado J.M.; GENE-CBR: A casebased reasoning tool for cancer diagnosis using microarray data sets; Computational Intelligence; Volume 22; Issue 3-4; pp 254- 268; 2006.
[19] Gardner R.M.; Pryor T.A. and Warner H.R.; The HELP hospital information system: update 1998; International Journal of Medical Informatics; Volume 54; Issue 3; pp 169-182; 1999.
[20] Glez-Peña D.; Díaz F.; Hernández J.M.; Corchado J.M. and Fdez- Riverola F.; geneCBR:A translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research; BMC Bioinformatics; Volume 10; Issue 1; Publisher: BioMed Central; 2009.
[21] Hartrick C.T.; Kovan J.P. and Shapiro S.; The numeric rating scale for clinical pain measurement: a ratio measure?; Pain Pract; Volume 3; Issue 4; pp 310–316; 2003.
[22] Hawthorn J. and Redmond K.; Pain: Causes and management; Journal of Psychiatric and Mental Health Nursing; Volume 6; Issue 5; pp 409–410; 1999.
[23] Huskisson E.C.; Measurement of pain; The Lancet; Volume 304; Issue 7889; pp 1127-1131; 1974.
[24] Jose D.A.; Fischer H.B.J.; Ivani G.; Mogensen T.; Narchi P.; Singelyn F.J.; Stienstr R. and Wulf H.; Postoperative pain management – good clinical practice; European Society of Regional Anaesthesia and Pain therapy; http://www.esraeurope.org/PostoperativePainManagement.pdf; Last referred September; 2011.
[25] Kappes B.; Biofeedback therapy: Training or treatment; Applied Psychophysiology and Biofeedback; Volume 33; pp 173-179; 2008.
[26] Kolodner J.L. and Leake D.; A tutorial introduction to case-basedreasoning; In Case-Based Reasoning: Experiences; Lessons andFuture Directions; Leale D. (Ed.); Cambridge; MA: AAAI/MIT Press; pp 31-65; 1996.
[27] Koton P.; Using experience in learning and problem solving;Massachusetts Institute of Technology; Laboratory of Computer Science; PhD Thesis MIT/LCS/TR-441; 1989.
[28] Kumar S.; Tandon O.P. and Mathur R.; Pain measurement: a formidable task; Indian Journal of Physiol Pharmacol; Volume 46; Issue 4; pp 396–406; 2002.
[29] Lehrer P.; Smetankin A. and Potapova T.; Respiratory Sinus Arrhythmia Biofeedback Therapy for Asthma: A Report of 20 Unmedicated Pediatric Cases Using the Smetankin Method; Applied Psychophysiology and Biofeedback; Volume 25; Issue 3; pp 193-200; 2000.
[30] Lindgaard G.; Egan P.; Jones C.; Pyper C.; Frize M.; Walker R.; Boutilier C.; Hui B.; Narasimhan S.; Folkens J. and Winogron B.; Intelligent decision support in medicine: back to Bayes?; Journal of Universal Computer Science; Volume. 14; Issue 16; pp 2720- 2736; 2008.
[31] Marling C. and Whitehouse P.; Case-based reasoning in the care of Alzheimer’s disease patients; 4th International Conference on Case-Based Reasoning; ICCBR 01; pp 702-715; 2001.
[32] Montani S.; Portinale L.; Leonardi G. and Bellazzi R.; Case-based retrieval to support the treatment of end stage renal failure patients; Artificial Intelligence in Medicine; Volume 37; Issue 1; pp 31-42; 2006.
[33] Nilsson M.; Funk P.; Olsson E.; Von Schéele B. H. C.; and Xiong N.; Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system; Artificial Intelligence in Medicine; Volume 36; Issue 2; pp 159-176; 2006.
[34] Noble R.E; Diagnosis of stress; Metabolism - Clinical and Experimental; Elsevier Science (USA);Volume 51; Issue 6; Part B; pp 37-39; 2002.
[35] Podgorelec V.; Kokol P. and Zavrsnik J.; Medical diagnosis prediction using genetic programming; 12th IEEE Symposium on Computer-Based Medical Systems; pp 202 - 207; 1999.
[36] Salton G.; Wong A. and Yang C.S.; A vector space model for automatic indexing; Communications of the ACM; Volume18; Issue11; pp 613–620; 1975.
[37] Salton G. and Buckley C.; Term weighting approaches in automatic text retrieval; Information Processing and Management; Volume 24; Issue 5; p513-23; 1988.
[38] Schmidt R. and Vorobieva O.; Case-based reasoning investigation of therapy inefficacy; Knowledge-Based Systems; Volume 19; Issue 5; pp 333–340; 2006.
[39] Selye H.; The Stress of Life. New York: McGrawHill; 1956; Revised edition 1976.
[40] Watson I.; Applying Case-Based Reasoning; Techniques for Enterprise Systems; Morgan Kaufmann Publishers Inc; 250 pages; ISBN 1-55860-462-6; 1997.
[41] Yang J.B.; Xu D.L. and Kong G.; Clinical decision support systems: a review on knowledge representation and inference under uncertainties; International Journal of Computational Intelligence Systems; Volume 1; Nr. 2; pp 159-167; 2008.
[42] Begum S.; A Case-Based Driver’s State Monitoring System UsingPhysiological Sensor Signals; 11th Industrial Conference; ICDM 2011; Advances in Data Mining; Workshop Proceedings on Case- Based Reasoning ; p 42-54; Ibai Publishing; New York; USA; Editor(s):Petra Perner and Georg Rub; September; 2011.
[43] Begum S.; Ahmed M.U.; Funk P.; Xiong N.; Folke M.; Case-Based Reasoning Systems in the Health Sciences: A Survey on Recent Trends and Developments; International journal of IEEE Transactions on Systems; Man; and Cybernetics-Part C: Applications and Reviews; vol 41; Issue 4; 2011; pp 421 - 434.
[44] Ahmed M.U.; Begum S.; Funk P.; A Hybrid Case-Based System in Stress Diagnosis and Treatment; Accepted in the IEEEEMBS International Conference on Biomedical and Health Informatics (BHI2012); 2012.
[45] Ahmed M.U.; Begum S.; Funk P.; Xiong N.; von Schéele B; Casebased Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity. International journal of Transactions on Case-Based Reasoning on Multimedia Data; vol 1; Number 1; IBaI Publishing; ISSN: 1864-9734; 2008; pp 3-19.
[46] Ahmed M.U.; Begum S.; Funk P.; Xiong N.; von Schéele B; A Multi-Module Case Based Biofeedback System for Stress Treatment. International journal of Artificial Intelligence in Medicine; vol.51; Issue 2; Publisher: Elsevier B.V.; 2010; pp 107- 115.
[47] Ahmed M.U.; Begum S.; Funk P.; Xiong N.; Fuzzy Rule-Based Classification to Build Initial Case Library for Case-Based Stress Diagnosis. In the proceedings of 9th International Conference on Artificial Intelligence and Applications (AIA); 2009; pp 225-230.
[48] Ahmed M.U.; Funk P.; A Case-Based Retrieval System for Postoperative Pain Treatment; In the proceeding of International Workshop on Case-Based Reasoning CBR 2011; IBaI; Germany; New York/USA; Ed(s):Petra Perner and Georg Rub; September; 2011; pp 30-41.
[49] Ahmed M.U.; Funk P.; Mining Rare Cases in Post-Operative Pain by Means of Outlier Detection; Accepted in the IEEE International Symposium on Signal Processing and Information Technology; 2011.
