Intelligent analysis of clinical time series: an application in the diabetes mellitus domain

• Published in: Artificial intelligence in medicine Vol. 20; no. 1; pp. 37 - 57
• Main Authors: Bellazzi, R, Larizza, C, Magni, P, Montani, S, Stefanelli, M
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.08.2000
• Subjects: Artificial Intelligence; Computer applications; Computer Simulation; Decision Support Techniques; Diabetes; Diabetes mellitus; Diabetes Mellitus - therapy; Diabetes Mellitus, Type 1 - therapy; Humans; Intelligent data analysis; Medicine; Monitoring, Physiologic; Software; Temporal abstractions; Time series analysis; Time series analysis; Diabetes mellitus; Intelligent data analysis; Temporal abstractions
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/S0933-3657(00)00052-X

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.