Mining biomedical time series by combining structural analysis and temporal abstractions

This paper describes the combination of Structural Time Series analysis and Temporal Abstractions for the interpretation of data coming from home monitoring of diabetic patients. Blood Glucose data are analyzed by a novel Bayesian technique for time series analysis. The results obtained are post-processed using Temporal Abstractions in order to extract knowledge that can be exploited "at the point of use" from physicians. The proposed data analysis procedure can be viewed as a Knowledge Discovery in Data Base process that is applied to time-varying data. The work here described is part of a Web-based telemedicine system for the management of Insulin Dependent Diabetes Mellitus patients, called T-IDDM.

[1]  Giuseppe De Nicolao,et al.  Dynamic Probabilistic Networks for Modelling and Identifying Dynamic Systems: a MCMC Approach , 1997, Intell. Data Anal..

[2]  E. Carson,et al.  Time series analysis and control of blood glucose levels in diabetic patients. , 1994, Computer methods and programs in biomedicine.

[3]  E. Carson,et al.  A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study. , 1994, Computer Methods and Programs in Biomedicine.

[4]  E. Carson,et al.  UTOPIA: a consultation system for visit-by-visit diabetes management. , 1996, Medical informatics = Medecine et informatique.

[5]  Paola Sebastiani,et al.  The Use of Exogenous Knowledge to Learn Bayesian Networks from Incomplete Databases , 1997, IDA.

[6]  Yuval Shahar,et al.  Knowledge-based temporal abstraction in clinical domains , 1996, Artif. Intell. Medicine.

[7]  A Riva,et al.  A Web-based system for the intelligent management of diabetic patients. , 1997, M.D. computing : computers in medical practice.

[8]  Yuval Shahar,et al.  A Framework for Knowledge-Based Temporal Abstraction , 1997, Artif. Intell..

[9]  G F Cooper,et al.  Representing and developing temporally abstracted knowledge as a means towards facilitating time modeling in medical decision-support systems , 1997, Comput. Biol. Medicine.

[10]  E. Lehmann.  Application of computers in clinical diabetes care , 1997 .

[11]  Paul Dagum,et al.  Time series prediction using belief network models , 1995, Int. J. Hum. Comput. Stud..

[12]  Masahito Yamada,et al.  Structural Time Series Models and the Kalman Filter , 1989 .

[13]  Ramasamy Uthurusamy,et al.  Data mining and knowledge discovery in databases , 1996, CACM.