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Towards a Possibility-Theoretic Approach to Uncertainty in Medical Data Interpretation for Text Generation

  • François Portet
  • Albert Gatt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5943)

Abstract

Many real-world applications that reason about events obtained from raw data must deal with the problem of temporal uncertainty, which arises due to error or inaccuracy in data. Uncertainty also compromises reasoning where relationships between events need to be inferred. This paper discusses an approach to dealing with uncertainty in temporal and causal relations using Possibility Theory, focusing on a family of medical decision support systems that aim to generate textual summaries from raw patient data in a Neonatal Intensive Care Unit. We describe a framework to capture temporal uncertainty and to express it in generated texts by mean of linguistic modifiers. These modifiers have been chosen based on a human experiment testing the association between subjective certainty about a proposition and the participants’ way of verbalising it.

Keywords

Neonatal Intensive Care Unit Temporal Relation Clinical Decision Support System Oral Suction Linguistic Expression 
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 2010

Authors and Affiliations

  • François Portet
    • 1
  • Albert Gatt
    • 2
  1. 1.Laboratoire d’Informatique de GrenobleGrenoble Institute of TechnologyFrance
  2. 2.Institute of LinguisticsUniversity of MaltaMalta

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