Abstract
Collaborative Filtering is a well-approved method for prediction of consumer behaviour in marketing strategies. We adapt and evaluate this method for prognosis of the longterm metabolic control level in diabetic patients. The underlying data for the prediction were extracted from a central diabetic data pool (DPVSCIENT) [1],[2], containing longtime documentation of about 60% of all young patients with type-1 diabetes in Germany. Prediction results were successfully checked against random values and evaluated calculating sensitivity, specifity and total performance of a prognosis test. Best results were: sensitivity = 76%, specifity = 92% and total performance = 84%. This novel approach in diabetology demonstrates tracking for metabolic control and allows to predict favorable or unfavorable results, providing an objective basis to target intervention in individual patients.
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References
Grabert M., Schweiggert F., Holl R. W. and the German Pediatric Quality Control Group on Diabetes, 10 Years Successful Implementation Of a Diabetes Documentation System, Supporting External Benchmarking and Nation-wide Scientific Investigations. Diabetes, Nutrition & Metabolism Vol 13. N.4 (2000):pp.225
Hecker W, Grabert M, Holl RW, Quality of paediatric IDDM care in Germany: A multicentre analysis. German Paediatric Diabetology Group. J Pediatr Endocrinol Metab. 1999 Jan–Feb;12 (1):31–8
EURODIAB ACE study group, Variation and trends in incidence of childhood diabetes in Europe. Lancet 2000 11. März (9207), pp. 873–876
Diabetes Control and Complications Trial Research Group, The effect of intensive treatment of diabetes on the development and progression of long-term complications of insulin-dependet diabetes mellitus. N. Engl. J. Med. 329: 977–986, 1993
Resnick P., Neophytos I., Mitesh S., Bergstrom P., Riedl J., GroupLens, An Open Architecture for Collaborative Filtering of Netnews. Proc. of CSCW ’94: Conference on Computer Supported Cooperative Work (Chapel Hill, 1994), pp175–186, Addison-Wesley
Runte M., Personalisierung im Internet — Individualisierte Angebote mit Collaborative Filtering, Ph.D. thesis 2000, Christian-Albrechts-University of Kiel, Faculty of Economics and Social Sciences
Schafer, J.B., Konstan, J.A., and Riedl, J. (1999), Recommender Systems in E-Commerce. In ACM Conference on Electronic Commerce (EC-99), pages 158–166
Breese, J., Heckerman, D. and Kadie, C. (1998), Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-98). San Francisco, CA: Morgan Kaufmann, 43–52.
Herlocker J.L., Konstan J.A., Borchers A., Riedl J. (1999), An Algorithmic Framework for Performing Collaborative Filtering, Proceedings of the 1999 Conference on Research and Development in Information Retrieval, August 1999
Balabanovic M., Shoham Y., Fab: Content-Based, Collaborative Recommendation, Communication of the ACM Vol.40 No. 3.(1997): pp. 66–72
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Grabert, M., Holl, R.W., Krause, U., Melzer, I., Schweiggert, F. (2001). Predicting the Level of Metabolic Control Using Collaborative Filtering. In: Crespo, J., Maojo, V., Martin, F. (eds) Medical Data Analysis. ISMDA 2001. Lecture Notes in Computer Science, vol 2199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45497-7_16
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DOI: https://doi.org/10.1007/3-540-45497-7_16
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