Time series prediction using belief network models
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Data mining and knowledge discovery in databases
Communications of the ACM
A framework for knowledge-based temporal abstraction
Artificial Intelligence
The Use of Exogenous Knowledge to Learn Bayesian Networks from Incomplete Databases
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Using Time-Oriented Data Abstraction Methods to Optimize Oxygen Supply for Neonates
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Intelligent analysis of clinical time series: an application in the diabetes mellitus domain
Artificial Intelligence in Medicine
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This paper describes the application of Intelligent Data Analysis techniques for extracting information on trends and cycles of time series coming from home monitoring of diabetic patients. In particular, we propose the combination of structural Time Series analysis and Temporal Abstractions for the interpretation of longitudinal Blood Glucose measurements. First, the measured time series is analyzed by using a novel Bayesian technique for structural filtering; second, 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 typical Intelligent Data Analysis process applied to time-varying data: Background Knowledge is exploited in each step of the analysis, and the final result is a meaningful, abstract description of the complex process at hand. The work here described is part of a web-based telemedicine system for the management of Insulin Dependent Diabetes Mellitus patients, developed within the EU-funded project called T-IDDM.