Therapy management needs sophisticated patient monitoring and therapy planning, especially in high-frequency domains, like Neonatal Intensive Care Units (NICUs), where complex data sets are collected every second. An elegant method to tackle this problem is the use of time-oriented, skeletal plans. Asgaard is a framework for the representation, visualization, and execution of such plans. These plans work on qualitative abstracted time-oriented data which closely resemble the concepts used by experienced clinicians. This papers presents the data abstraction unit of the Asgaard system. It provides a range of connectable data abstraction methods bridging the gap between the raw data collected by monitoring devices and the abstract concepts used in therapeutic plans. The usability of this data abstraction unit is demonstrated by the implementation of a controller for the automated optimization of the fraction of inspired oxygen (FiO2 ). The use of the time-oriented data abstraction methods results in safe and smooth adjustment actions of our controller in a neonatal care setting. 1

ions C. Larizza, R. Bellazzi, G. Lanzola Dipartimento di Informatica e Sistemistica, Universita' di Pavia, via Ferrata 1, 27100 Pavia, Italy e-mail: cri, ric, giordano@aim.unipv.it Abstract This paper describes an HTTP based server able to extract from multi-dimensional time series different types of patterns. It exploits a method coming from the AI field, called Temporal Abstractions, which is used to obtain a high level description of temporal data. The server has been successfully integrated within a telemedicine system for the home monitoring management of diabetic patients. 1. Introduction Many monitoring problems and, in particular long-term monitoring of chronic patients, involve a great effort of the physicians to carefully analyze and interpret the large amount of clinical data collected over time. In fact, patients follow-up data are heterogeneous and present some common features that prevent from adopting classical time series analysis techniques. The most import...

In this paper we present a method for the analysis and subsequent clustering of data coming from home monitoring of diabetic patients. Our method aims at characterising the patient behaviour over time in order to be able to cluster periods with similar dynamics, and to provide a mean to physicians for better learning the action/reaction pattern that a certain patient shows in response to Insulin therapy. The method is described and a case study is reported. 1. Introduction The analysis of multi-variate time series is an ubiquitous problem in science, and represents a crucial challenge in biomedicine applications, such as clinical monitoring, where several parameters must be contemporaneously examined to understand the patient's overall situation. This rather complex task has been traditionally faced with descriptive and inferential statistical techniques[Deutsch,1994]. More recently, an AI-based methodology, known as Temporal Abstractions (TAs) has been proposed and successfully exp...

Abstract not found

Abstract not found