INTELLIGENT CLINICAL DECISION SUPPORT SYSTEMS

Alexandru G. Floares

2010

Abstract

Clinical Decision Support Systems (CDSS) have the potential to replace painful, invasive, and costly procedures, to optimize medical decisions, improve medical care, and reduce costs. An even better strategy is to make use of a knowledge discovery in data approach, with the aid of artificial intelligence tools. This results in transforming conventional CDSS in Intelligent Clinical Decision Support (i-CDSS). Evolving i-CDSS give to the conventional CDSS the capability of self-modifying their rules set, through supervised learning from patients data. Intelligent and evolving CDSS represent a strong foundation for evidence-based medicine. We proposed a methodology of building i-CDSS and related concepts. These are illustrated with some of our results in liver diseases and prostate cancer, some of them showing the best published performance.

References

  1. Balacescu, O., Neagoe, I., Balacescu, L., Crisan, N., Feciche, B., Tudoran, O., Coman, I., and Irimie, A. (2008). Angiogenesis serum protein quantifcation for prostate pathology. Curr Urol, (2):181-187.
  2. Berner, E., editor (2007). Clinical decision support systems. Springer: New York, NY.
  3. Fawcett, T. (2004). Roc graphs: Notes and practical considerations for researchers. technical report. Technical report, Palo Alto, USA: HP Laboratories.
  4. Floares, A. G. (2008). Intelligent systems for interferon treatment decision support in chronic hepatitis c based on i-biopsy. In IDAMAP - Intelligent Data Analysis in Biomedicine and Pharmacology, Washington DC.
  5. Floares, A. G. (2009a). Intelligent clinical decision supports for interferon treatment in chronic hepatitis c and b based on i-biopsy. In International Joint Conference on Neural Networks, 2009, Atlanta, Georgia, USA.
  6. Floares, A. G. (2009b). Liver i-Biopsy and the Corresponding Intelligent Fibrosis Scoring Systems: i-Metavir F and i-Ishak F, pages 253-264. Lecture Notes in Computer Science. Springer Berlin / Heidelberg.
  7. Floares, A. G., Lupsor, M., Stefanescu, H., Sparchez, Z., Serban, A., Suteu, T., and Badea, R. (2008). Toward intelligent virtual biopsy: Using artificial intelligence to predict fibrosis stage in chronic hepatitis c patients without biopsy. Journal of Hepatology, 48(2).
  8. Freund, Y. and Schapire, R. E. (1997). A decision theoretic generalization of on line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139.
  9. Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L. (2006). Feature Extraction: Foundations and Applications. Studies in Fuzziness and Soft Computing. Springer.
  10. Lindor, A. (1996). The role of ultrasonography and automatic-needle biopsy in outpatient percutaneous liver biopsy. Hepatology, 23:1079-1083.
  11. Poynard, T., Morra, R., Halfon, P., Castera, L., Ratziu, V., Imbert-Bismut, F., Naveau, S., Thabut, D., Lebrec, D., Zoulim, F., Bourliere, M., Cacoub, P., Messous, D., Muntenau, M., and de Ledinghen, V. (2007). Metaanalyses of fibrotest diagnostic value in chronic liver disease. BMC Gastroenterology, 7(40).
  12. Quinlan, J. (1993). C4.5 : Programs for Machine Learning. Morgan Kaufmann.
  13. Shaheen, A., Wan, A., and Myers, R. (2007). Fibrotest and fibroscan for the prediction of hepatitis c-related fibrosis: a systematic review of diagnostic test accuracy. Am J Gastroenterol, 102(11):2589-2600.
  14. Tobkes, A. and Nord, H. J. (1995). Liver biopsy: Review of methodology and complications. Digestive Disorders, 13:267-274.
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Paper Citation


in Harvard Style

G. Floares A. (2010). INTELLIGENT CLINICAL DECISION SUPPORT SYSTEMS . In Proceedings of the Third International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2010) ISBN 978-989-674-016-0, pages 282-287. DOI: 10.5220/0002740802820287


in Bibtex Style

@conference{healthinf10,
author={Alexandru G. Floares},
title={INTELLIGENT CLINICAL DECISION SUPPORT SYSTEMS},
booktitle={Proceedings of the Third International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2010)},
year={2010},
pages={282-287},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002740802820287},
isbn={978-989-674-016-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2010)
TI - INTELLIGENT CLINICAL DECISION SUPPORT SYSTEMS
SN - 978-989-674-016-0
AU - G. Floares A.
PY - 2010
SP - 282
EP - 287
DO - 10.5220/0002740802820287