Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Application of Artificial Intelligence for Virtually Assisted Prognosis of Diabetes: A NODDS Project

Print
PDF
IJCA Proceedings on National Seminar on Application of Artificial Intelligence in Life Sciences 2013
© 2013 by IJCA Journal
NSAAILS - Number 1
Year of Publication: 2013
Authors:
Ankush Rai

Ankush Rai. Article: Application of Artificial Intelligence for Virtually Assisted Prognosis of Diabetes: A NODDS Project. IJCA Proceedings on National Seminar on Application of Artificial Intelligence in Life Sciences 2013 NSAAILS(1):1-5, February 2013. Full text available. BibTeX

@article{key:article,
	author = {Ankush Rai},
	title = {Article: Application of Artificial Intelligence for Virtually Assisted Prognosis of Diabetes: A NODDS Project},
	journal = {IJCA Proceedings on National Seminar on Application of Artificial Intelligence in Life Sciences 2013},
	year = {2013},
	volume = {NSAAILS},
	number = {1},
	pages = {1-5},
	month = {February},
	note = {Full text available}
}

Abstract

We present a successful application of Artificial Intelligence (AI) methodologies in the context of a network oriented virtual care service for diabetic patients management, developed within the public-funded NODDS project. Several AI methods have been exploited to implement the NODDS functionality. Temporal Abstractions and other Intelligent Data Analysis techniques are used to analyse the patient's monitoring data; the Case Based Reasoning (CBR) methodology is applied to perform the Knowledge Management task. The NODDS service is being tested through a small on field trial; the first results, though preliminary, seem to substantiate the hypothesis that the use of an AI-based risk evaluation system could present an advantage in the management of type 1 diabetic patients, leading to a more tight control of the patients' metabolic situation, in a cost-effective way.

References

  • R. D. Lasker, 1993 ,The diabetes control and complication trial. Implications for policy and practice, The New England Journal of Medicine, 329, 1035–1036.
  • R. S. H. Istepanian and A. Tseng, 2001, Diabetes Management using Advanced Mobile Technologies for Cost-Effective, Real-Time Patient Monitoring. , in Proc. of eHealth, 3rd International Conference on Advances in the Delivery of Care, pp. 139-143, City University, UK.
  • E. D. Lehmann and T. Deutsch, 1995, Application of computers in diabetes care - a review (I and II), Med. Inform. , 20, 281–329.
  • S. Andreassen et al. , 1994, A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study, Computer Methods and Programs in Biomedicine, 41, 153–165.
  • E. J. Gomez et al. , 1992, A Telemedicine Distributed Decision-Support System for Diabetes Management, 1238–1239, IEEE- 14th Ann. Int. Conf. Of the IEEE Eng. in Med. and Biol. Soc. .
  • Agnar Aamodt and Enric Plaza, 1994, Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches, Artificial Intelligence Communications, 39-52.
  • Domingos, Pedro & Michael Pazzani, 1997, On the optimality of the simple Bayesian classifier under zero-one loss, Machine Learning, 29:103–137
  • J. L. Kolodner, 1993, Case-Based Reasoning, Morgan Kaufmann.
  • I. Kononenko, Inductive and Bayesian learning in medical diagnosis, Applied Artificial Intelligence, 7, 317–337.
  • I. Zelic, I. Kononenko, N. Lavrac, V. Vuga, 1997, Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries, 61– 67, Proceedings of IDAMAP 97 workshop, IJCAI 97, Nagoya.
  • D. Spiegelhalter, A. Dawid, S. Lauritzen, R. Cowell, 1993, Bayesian Analysis in Expert Systems, Statistical Science, 8, 219–283.
  • A. Riva, R. Bellazzi, 1996, Learning temporal probabilistic causal models from longitudinal data, Artificial Intelligence in Medicine, 8, 217–234.
  • T. Mitchell, 1997, Machine Learning, Mc Graw Hill.
  • S. Montani et al. , 1998, A case-based retrieval system for diabetic patients therapy, 64–70, Proceedings of IDAMAP 98 workshop, ECAI 98, Brighton.
  • D. R. Wilson, T. R. Martinez, 1997, Improved heterogeneous distance functions, Journal of Artificial Intelligence Research, 6, 1–34.
  • L. Portinale, P. Torasso, D. Magro, 1997, Selecting most adaptable diagnostic solutions through pivoting-based retrieval, 277–288, Lecture Notes in Artificial Intelligence 1266, Springer Verlag.
  • López de Mántaras R. A, 1991, Distance-based Attribute Selection Measure for Decision Tree Induction. Machine Learning, 6: 81-92.
  • Quinlan J. R. , 1986, Induction of decision tress. Machine Learning, 1:81-106.