Conference Paper

A Fuzzy Approach to Temporal Model-Based Diagnosis for Intensive Care Units.

Conference: Proceedings of the 16th Eureopean Conference on Artificial Intelligence, ECAI'2004, including Prestigious Applicants of Intelligent Systems, PAIS 2004, Valencia, Spain, August 22-27, 2004
Source: DBLP
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    ABSTRACT: Time-varying information embedded in cases has often been neglected and its role oversimplified in case-based reasoning systems. In several real-world problems, and in particular in medical applications, a case should capture the evolution of the observed phenomenon over time. To this end, we propose to represent temporal information at two levels: (1) at the case level, when some features are collected in the form of time series, because they describe parameters varying within a period of time (which corresponds to the case duration), and we aim at analyzing the system behavior within the case duration interval itself; (2) at the history level, when we are interested in reconstructing the evolution of the system by retrieving temporally related cases. In this paper, we describe a framework for case representation and retrieval that is able to take into account the temporal dimension, and is meant to be used in any time dependent domain, which is particularly well suited for medical applications. To support case retrieval, we provide an analysis of similarity-based time series retrieval techniques; to support history retrieval, we introduce possible ways to summarize the case content, together with the corresponding strategies for identifying similar instances in the knowledge base. A concrete application of our framework is represented byRhene, a system for intelligent retrieval in the hemodialysis domain.
    Computational Intelligence 01/2006; 22:208-223. · 1.00 Impact Factor
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    01/2010: chapter Providing case-based retrieval as a decision support strategy in time dependent medical domains: pages 211-228; Springer.
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    ABSTRACT: Time series retrieval is a critical issue in all domains in which the observed phenomenon dynamics have to be dealt with. In this paper, we propose a novel, domain independent time series retrieval framework, based on Temporal Abstractions (TA). Our framework allows for multi-level abstractions, according to two dimensions, namely a taxonomy of (trend or state) symbols, and a variety of time granularities. Moreover, we allow for flexible querying, where queries can be expressed at any level of detail in both dimensions, also in an interactive fashion, and ground cases as well as generalized ones can be retrieved. We also take advantage of multi-dimensional orthogonal index structures, which can be refined progressively and on demand. The framework in practice is illustrated by means of a case study in hemodialysis.
    Case-Based Reasoning Research and Development, 8th International Conference on Case-Based Reasoning, ICCBR 2009, Seattle, WA, USA, July 20-23, 2009, Proceedings; 01/2009

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