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Formal verification of CNL health recommendations

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Author(s)

Fahrurrozi Rahman, Juliana Kuster Filipe Bowles

School/Research organisations

Abstract

Clinical texts, such as therapy algorithms, are often described in natural language and may include hidden inconsistencies, gaps and potential deadlocks. In this paper, we propose an approach to identify such problems with formal verification. From each sentence in the therapy algorithm we automatically generate a parse tree and derive case frames. From the case frames we construct a state-based representation (in our case a timed automaton) and use a model checker (here UPPAAL) to verify the model. Throughout the paper we use an example of the algorithm for blood glucose lowering therapy in adults with type 2 diabetes to illustrate our approach.
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Details

Original languageEnglish
Title of host publicationIntegrated Formal Methods
Subtitle of host publication13th International Conference, IFM 2017, Turin, Italy, September 20-22, 2017, Proceedings
EditorsNadia Polikarpova, Steve Schneider
Place of PublicationCham
PublisherSpringer
Pages357-371
Number of pages15
ISBN (Electronic)9783319668451
ISBN (Print)9783319668444
DOIs
StatePublished - 2017
Event13th International Conference on integrated Formal Methods (iFM 2017) - Torino, Italy

Publication series

NameLecture Notes in Computer Science (Programming and Software Engineering)
PublisherSpringer International Publishing
Volume10510
ISSN (Electronic)0302-9743

Conference

Conference13th International Conference on integrated Formal Methods (iFM 2017)
Abbreviated titleiFM
CountryItaly
CityTorino
Period18/09/1722/09/17
Internet address

    Research areas

  • Formal verification, Controlled natural language, Timed automata, Health recommendations

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