This paper describes the application of a method for the Intelligent Analysis of Clin- ical Time Series in the Diabetes Mellitus domain. Such method is based on Temporal Abstractions and relies on the following steps: (i) pre-processing of raw data through the application of suitable filtering techniques; (ii) extraction from the pre-processed data of a set of abstract episodes (Temporal Abstractions); (iii) post-processing of Temporal Ab- stractions; the post-processing phase results in a new set of features, that embeds high level information on the patient dynamics. The derived features set is used to obtain new knowledge through the application of machine learning algorithms. The paper de- scribes in detail the application of this methodology and presents some results obtained on simulated data and on a data-set of four Diabetic patients monitored for more than one year.

This paper describes the application oflntelligent 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.

Data mining is often used to develop predictive models from data, but rarely addresses how these models are to be employed. To use the constructed model, the user is usually required to run an often complex data mining suite in which the model has been constructed. A better mechanism for the communication of resulting models and less complex, easy to use tools for their employment are needed. We propose a technological solution to the problem, where a predictive model is encoded in XML and then used through a Web- or Palm handheld-based decision support shell. This schema supports developer-to-user and user-to-user communication. To facilitate the communication between the developers we advocate the use of data mining scripts. 1