A Model-based Temporal Abductive Diagnosis Model for an Intensive Coronary Care Unit

  • J. T. Palma
  • R. Marín
  • J. L. Sánchez
  • F. Palacios
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 83)


In current high-dependency clinical environments such as Intensive Coronary Care Unit (ICCU hereinafter), operating rooms and so on, the clinical staff is presented with a large mass of data about the patient’s state. These data can be obtained from the advanced biomedical equipment (especially from electrical and hemodynamical monitors), patient’s history, physical examination findings and test results. This massive flow of information can lead to some well-known problems such as data overload and missing data and misinterpretation [1,13]. In order to avoid these kinds of problems, Intelligent Patient Supervision Systems (IPSS hereinafter) have been developed. IPSSs must be developed to support the interpretation of these data and they should provide information in higher abstraction levels in order to improve the decision making process.


Temporal Pattern Temporal Constraint Causal Network Temporal Reasoning Diagnosis Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • J. T. Palma
    • 1
  • R. Marín
    • 1
  • J. L. Sánchez
    • 1
  • F. Palacios
    • 2
  1. 1.Artificial Intelligence and Knowledge Engineering Group, Computer Science SchoolUniversity of MurciaMurciaSpain
  2. 2.Hospital General de ElcheElcheSpain

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