A computer system for interpreting blood glucose data

Computer Methods and Programs in Biomedicine

Volumn 76, Issue 1, 2004, Pages 41-51

  Deutsch, T ^a,b   Gergely, T ^a   Trunov, V ^c  

^a Applied Logic Laboratory (ALL)   (Hungary)

^b SEMMELWEIS UNIVERSITY   (Hungary)

^c INSTITUTE FOR INFORMATION TRANSMISSION PROBLEMS   (Russian Federation)

Author keywords

Blood glucose data; Diabetes mellitus; Intelligent data analysis; Patient specific model

Indexed keywords

DATA REDUCTION; GLUCOSE; METABOLISM;

BLOOD GLUCOSE (BG) PROFILES; DIABETIC PATIENTS; EXTRACTING TREND PATTERNS; METABOLIC PROBLEMS;

COMPUTER SYSTEMS;

CLASSIFICATION; COMPUTER ANALYSIS; COMPUTER PROGRAM; COMPUTER SYSTEM; CONTROLLED STUDY; DATA ANALYSIS; DIABETES MELLITUS; DIAGNOSTIC PROCEDURE; GLUCOSE BLOOD LEVEL; HOME MONITORING; HUMAN; INFORMATION PROCESSING; MEAL; METABOLIC DISORDER; METABOLIC REGULATION; METHODOLOGY; PATIENT; PATIENT INFORMATION; RECORDING; REVIEW; SYSTEM ANALYSIS; TECHNIQUE; TIME;

EID: 4143050228     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2004.02.008     Document Type: Article

References (18)

1. htp://www.mendosa.com.
2. Shahar Y., Musen M.A. Knowledge based temporal abstractions in clinical domain. Artif. Intell. Med. 8:1996;267-298
3. Deutsch T., Lehmann E.D., Carson E.R., Roudsari A., Hopkins K.D., Sonksen P.H. Time series analysis and control of blood glucose levels in diabetic patients. Comput. Methods Prog. Biomed. 41:1994;167-182
4. Bellazzi R., Larizza C., Magni P., Montani S., Stefanelli M. Intelligent Analysis of Clinical Time Series: an application in the Diabetes Mellitus Domain. Artif. Intell. Med. 20:2000;37-57
5. Kahn M.G., Abrams C.B., Orland M.J., Beard J.C., Cousins S.B., Miller J.P., Santiago J.V. Intelligent computer-based interpretation and graphical presentation of self-monitored blood glucose and insulin data. Diabetes Nutr. Metab. 4(Suppl. 1):1991;99-107
6. N. Lavrac, E. Keravnou, B. Zupan (Eds.). Intelligent Data Analysis in Medicine and Pharmacology, Kluwer Academic Publisher, Boston, 1997.
7. Deutsch T., Gergely T. An intelligent partner system for chronic illness care. Inform. Primary Care. 11:2003;11-15
8. Shahar Y., Cheng Y. Intelligent visualisation and exploration of time oriented clinical data. Comput. Health Inform. Manage. 20(2):1999;15-31
9. Kosara R., Miksch S. Visualization methods for data analysis and planning in medical applications. Int J. Med. Inform. 68:2002;141-153
10. Lehmann E.D., Deutsch T. AIDA: a mark II automated insulin dosage advisor. J. Biomed. Eng. 15:1993;201-211
11. Lehmann E.D., Deutsch T. Application of computers in diabetes care - a review I. Computers for data collection and interpretation. Med. Inform. 20:1995;281-302
12. L. Kaufman, P.J. Rousseuw, Finding groups in data, Introduction to Cluster Analysis, Wiley, New York, 1990.
13. R. Bellazzi, C. Larizza, S. Montani, M. Stefanelli, Intra-patients learning by combining clustering and temporal abstractions, in: S.Miksch, Y. Shahar (Eds.), Proceedings of the 4th Workshop on Intelligent Data Analysis in Medicine and Pharmacology (AMIA, Washington), 1999, pp. 23-34.
14. Lane D.A. The Bayesian approach to causality assessment: an introduction. Drug Inform. J. 20:1986;455-461
15. T. Deutsch, D.G. Cramp, E.R. Carson, Decisions, Computers and Medicines: The Informatics of Pharmacotherapy, Elsevier, Amsterdam, 2001.
16. Riva A., Bellazzi R. Learning temporal probabilistic causal models from longitudinal data. Artif Intell. Med. 8:1996;217-234
17. T. Gergely, G.A. Ivakhnenko, Twice-multilayered neural networks self-organisation for noise filtration and unknown factors discovery, in: E.J.H. Kerckhorffs, M. Snorek (Eds.), Proceedings of the 15th European Simulation Multiconference (SCS, Prague, 2001), pp. 680-684.
18. General Principles of Software Validation Final Guidance for Industry and FDA Staff, U.S. Food and Drug Administration, Rockville, 2002.