Conference Paper

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

In proceeding of: 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: Supporting decision making in domains in which the observed phenomenon dynamics have to be dealt with, can greatly benefit of retrieval of past cases, provided that proper representation and retrieval techniques are implemented. In particular, when the parameters of interest take the form of time series, dimensionality reduction and flexible retrieval have to be addresses to this end. Classical methodological solutions proposed to cope with these issues, typically based on mathematical transforms, are characterized by strong limitations, such as a difficult interpretation of retrieval results for end users, reduced flexibility and interactivity, or inefficiency. In this paper, we describe a novel framework, in which time-series features are summarized by means of Temporal Abstractions, and then retrieved resorting to abstraction similarity. Our approach grants for interpretability of the output results, and understandability of the (user-guided) retrieval process. In particular, multilevel abstraction mechanisms and proper indexing techniques are provided, for flexible query issuing, and efficient and interactive query answering. Experimental results have shown the efficiency of our approach in a scalability test, and its superiority with respect to the use of a classical mathematical technique in flexibility, user friendliness, and also quality of results.
    IEEE Transactions on Knowledge and Data Engineering 01/2011; · 1.89 Impact Factor
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    ABSTRACT: Interpreting time series of measurements and exploring a repository of cases with time series data looking for similarities, are non-trivial, but very important tasks. Classical methodological solutions proposed to deal with (some of) these goals, typically based on mathematical techniques, are characterized by strong limitations, such as unclear or incorrect retrieval results and reduced interactivity and flexibility. In this paper, we describe a novel case base exploration and retrieval architecture, which supports time series summarization and interpretation by means of Temporal Abstractions, and in which multi-level abstraction mechanisms and proper indexing techniques are provided, in order to grant expressiveness in issuing queries, as well as efficiency and flexibility in answering queries themselves. Relying on a set of concrete examples, taken from the haemodialysis domain, we illustrate the system facilities, and we demonstrate the advantages of relying on this methodology, with respect to more classical mathematical ones.
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    ABSTRACT: Case-based Reasoning (CBR), and more specifically case-based retrieval, is recently being recognized as a valuable decision support methodology in “time dependent” medical domains, i.e. in all domains in which the observed phenomenon dynamics have to be dealt with. However, adopting CBR in these applications is non trivial, since the need for describing the process dynamics impacts both on case representation and on the retrieval activity itself. The aim of this chapter is the one of analysing the different methodologies introduced in the literature in order to implement time dependent medical CBR applications, with a particular emphasis on time series representation and retrieval. Among the others, a novel approach, which relies on Temporal Abstractions for time series dimensionality reduction, is analysed in depth, and illustrated by means of a case study in haemodialysis.
    10/2010: pages 211-228;

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