@article{f840085742354505ba8fc8744b2f1dcd,
title = "Intelligent analysis of clinical time series: an application in the diabetes mellitus domain",
abstract = "This paper describes the application of a method for the intelligent analysis of clinical time series in the diabetes mellitus domain. Such a 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); and (iii) 'post-processing' of temporal abstractions; 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 describes 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 > 1 year. (C) 2000 Elsevier Science B.V.",
author = "R. BELLAZZI and C. LARIZZA and P. MAGNI and Stefania MONTANI",
note = "Funding Information: The aim of this paper is to present the application of the above presented scheme to the problem of analyzing data coming from home monitoring of type I diabetic patients. In this paper, we will describe the methods used in each step and we will present some results. In particular, we will report an assessment study done on a simulated patient and we will show the results obtained on four real patients, who have been monitored for >1 year at the Policlinico S. Matteo Hospital of Pavia. Such patients have been enrolled within the telemedicine project — Telematic Management of Insulin Dependent Diabetes Mellitus{\textquoteright} (T-IDDM ), funded by the European Commission. T-IDDM has been devoted to providing patients and physicians with an information technology infrastructure for better diabetes management. In this project, the physician relies on a set of distributed web services, provided by a medical workstation. The approach described in this paper is part of the data analysis and visualization tools that are linked with the data-management and decision support modules of the whole system. For further details see Ref. [5] . Funding Information: We gratefully acknowledge Dr Giuseppe d{\textquoteright}Annunzio and Dr Stefano Fiocchi for providing their support and medical knowledge. We sincerely thank Alberto Riva, without whom this work could not have been done. Finally, we thank the anonymous reviewers for their help in improving the paper. This work is part of the project T-IDDM (HC 1047), funded by the European Commission.",
year = "2000",
language = "English",
volume = "20",
pages = "37--57",
journal = "Artificial Intelligence in Medicine",
issn = "0933-3657",
publisher = "Elsevier B.V.",
}
