Study on Self-Adaptive Clinical Pathway Decision Support System Based on Case-Based Reasoning

  • Gang Qu
  • Zhe Liu
  • Shengnan Cui
  • Jiafu Tang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)


To regulate medical behavior, improve medical quality and reduce health-care costs, clinical pathway is served as a standard treating model for both reducing the sources and sticking to the principles of managing the quality, which has been adopted worldwide. Thus we introduce an artificial intelligence method to meet this need which complete origin system of self-adaptive clinical pathway based on some cases inducing, give the whole framework and procedures of working, realize several key managements. Take the classified files of clinical path and electrical medical records as the origin database. Meanwhile, regard the unimplemented clinical pathways and as new cases. Then the clinical pathway can be calculated by using the above self-adaptive system based on CBR. The results are of great importance when to examining the feasibility and effectiveness of the system, determine a flexible, self-adaptive clinical pathway in the treatment process.


Case-based reasoning (CBR) Clinical pathway (CP) Self-adaption Decision 


  1. 1.
    Ardissono L, Di Leva A, Petrone G et al (2006) Adaptive medical workflow management for a context-dependent home healthcare assistance service. Electron Notes Theor Comput Sci 146:59–68CrossRefGoogle Scholar
  2. 2.
    Barra V, Laoffon T et al (1997) Clinical pathways in the peri-operative setting. Nursing Case Manage 2(3):97–106Google Scholar
  3. 3.
    Coeffy RJ et al (1992) An introduction to critical paths. Qual Manage Health Care 1(1):45–54Google Scholar
  4. 4.
    Di Leva A, Reyneri C (2004) The PARADIGMA project: an ontology based approach for cooperative work in the medical domain. Proc EMOI-INTEROP conferenceGoogle Scholar
  5. 5.
    Hurley KF, Abidi SSR (2007) Ontology engineering to model clinical pathways: towards the computerization and execution of clinical pathways. In: Proceedings of the twentieth IEEE international symposium computer-based medical systems, pp 536–541Google Scholar
  6. 6.
    Li CD (2004). Study of knowledge reconfiguration for clinical pathway implementation. Ph.D. thesis, Tianjin University, Management Science and EngineeringGoogle Scholar
  7. 7.
    Li H, Liu ZX, Men F (2010) Study on method of single disease cost prediction based on clinical pathway and CBR. Ind Eng Manage 15(2):105–110MATHGoogle Scholar
  8. 8.
    Abidi SSR, Chen H (2006) Adaptable personalized care planning via a semantic web framework. In Proceedings of the 20th international congress of the European federation for medical informatics. IOP Press, MaastrichtGoogle Scholar
  9. 9.
    Yang HQ, Li SZ, Zhao JP, Li WZ (2009) Study of clinical pathway based on organizational semiotics methods. Comput Eng Des 30(13):3189–3192Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Business AdministrationNortheastern UniversityShenyangChina
  2. 2.Xinhua Hospital Affiliated to Dalian UniversityDalianChina
  3. 3.School of Medical DevicesShenyang Pharmaceutical UniversityShenyangChina
  4. 4.School of Information Science and EngineeringNortheastern UniversityShenyangChina

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