Mining Hospital Data to Learn SDA* Clinical Algorithms

  • David Riaño
  • Joan Albert López-Vallverdú
  • Samson Tu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4924)

Abstract

The practice of evidence-based medicine requires the integration of individual clinical expertise with the best available external clinical evidence from systematic research and the patient’s unique values and circumstances. This paper addresses the problem of making explicit the knowledge on individual clinical expertise which is implicit in the hospital databases as data about the daily treatment of patients. The EHRcom data model is used to represent the procedural data of the hospital to which a machine learning process is applied in order to obtain a SDA* clinical algorithm that represents the course of actions followed by the clinical treatments in that hospital. The methodology is tested with data on COPD patients in a Spanish hospital.

Keywords

Health-care procedural knowledge machine learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    C.P.-E: 13606-1: Health Informatics - Electronic Health Record Communication - Part 1: Reference Model (2004)Google Scholar
  2. 2.
    Eichelberg, M., Aden, T., Riesmeier, J., Dogac, A., Laleci, G.B.: A Survey and Analysis of Electronic Health care Record Standards. ACM Computing Surveys (to appear)Google Scholar
  3. 3.
    GOLD-Global Initiative for Chronic Obstructive Lung Disease. Executive Summary (2006)Google Scholar
  4. 4.
    Iakovidis, I.: Towards Personal Health Records: Current Situation, Obstacles and Trends in Implementation of Electronic Health care Records in Europe. Int. J. of Medical Informatics 52(128), 105–117 (1998)CrossRefGoogle Scholar
  5. 5.
    López-Vallverdú, J.A., Riaño, D., Collado, A.: Increasing acceptability of decision trees with domain attributes partial orders. In: Proc. of the 20th IEEE International Symposium on Computer-Based Medical Systems, Maribor, Slovenia (2007)Google Scholar
  6. 6.
    Quinlan, J.R.: C4.5: Programs for ML, San Mateo, CA, USA. Morgan Kaufmann, San Francisco (1993)Google Scholar
  7. 7.
    Riaño, D.: The SDA* Model: A Set Theory Approach. In: Proc.of the 20th IEEE International Symposium on Computer-Based Medical Systems, Maribor, Slovenia (2007)Google Scholar
  8. 8.
    Sackett, D., Straus, S., Rosenberg, V., Haynes, B.: Evidence-Based Medicine: How to practice and teach EBM, 2nd edn. Chirchill Livingstone (2000)Google Scholar
  9. 9.
    Shahar, Y., Miksch, S., Johnson, P.: The Asgaard project: A task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artificial Intelligence in Medicine 14, 29–51 (1998)CrossRefGoogle Scholar
  10. 10.
    Sutton, D.R., Fox, J.: The Syntax and Semantics of the PROforma guideline modelling language. J. Am. Med. Inform. Assoc. 10(5), 433–443 (2003)CrossRefGoogle Scholar
  11. 11.
    Tu, S.W., Musen, M.A.: Modeling Data and Knowledge in the EON Guideline Architecture. In: Proc. MedInfo 2001, London, UK, pp. 280–284 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • David Riaño
    • 1
  • Joan Albert López-Vallverdú
    • 1
  • Samson Tu
    • 2
  1. 1.Rovira i Virgili UniversityTarragonaSpain
  2. 2.Stanford Medical InformaticsStanford UniversityPalo AltoUS

Personalised recommendations