Extended Clinical Discourse Representation Structure for Controlled Natural Language Clinical Decision Support Systems

Extended Clinical Discourse Representation Structure for Controlled Natural Language Clinical Decision Support Systems

David José Murteira Mendes (Universidade de Évora, Évora, Portugal), Irene Pimenta Rodrigues (Universidade de Évora, Évora, Portugal), Carlos F. Baeta (ULSNA, Évora, Portugal) and Carlos Solano-Rodriguez (Universidad de Alcalá de Henares, Alcalá de Henares, Spain)
Copyright: © 2015 |Pages: 11
DOI: 10.4018/IJRQEH.2015040101
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Abstract

To support an end to end Question and Answering system to help the clinical practitioners in a cardiovascular healthcare environment, an extended discourse representation structure CIDERS is introduced. This extension of the well-known DRT (Discourse Representation Theory) structures, go beyond single text representation extending them to embrace the general clinical history of a given patient. Introduced is a proposed and developed ontology framework, Ontology for General Clinical Practice, enhancing the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty, It's shown the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox).
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Introduction

This paper describes an aspect extracted from the work undertaken by the authors when developing a Clinical Decision Support System for the Cardiovascular Healthcare environment based in state-of-the-art Artificial Intelligence techniques.

It was originated by the cooperation among the different authors in the accomplishment of the first one PhD degree in Computer Science and namely in Clinical Knowledge representation for reasoning and acquisition based in NLP (Natural Language Processing).

We found that the problem known as “Knowledge Acquisition Bottleneck” (Wong et al., 2012) is currently the major obstacle for development of adequate representations of medical knowledge computable representations, namely ontologies in the specific domain and in particular in the healthcare sub domain. Trying to devise a valid solution to that problem in order to enable clinical automatic reasoning either in a local, distributed or Semantic Web fashion, different subproblems had to be addressed and solutions found are proposed and summarized in “Our Solution” section.

The particular solution illustrated here is the extension of the usual DRS (Discourse Representation Structure) that usually handles single texts and our proposal that we named CIDERS (Clinical Integrated Discourse Enhanced Representation Structure) has the extended capability of representing the whole discourse of a patient's clinical history.

First of all we introduce the scientific question of overcoming the KAB problem in the next section and explain why it is so hard to overcome.

Next the solution proposed is detailed in its various problems, and pragmatic approaches taken for the problem’s different facets.

The fourth section briefly explains the rationale behind CIDERS and why it may be as a natural extension of the application of ACE tools in our work.

In the fifth section, we present the results obtained so far and explain the promising applicability to different clinical realities with expectable similar results.

Finally in the last section some conclusions are drawn and summarized from the various sections of the present paper.

Nomenclature

  • ACE: Attempto Controlled English

  • AI: Artificial Intelligence

  • CAT: Computer Aided Translation

  • CIDERS: Clinical Integrated Discourse Extended Representation Structure

  • CNL: Controlled Natural Language

  • CORE: Clinical Observations Recording and Encoding

  • CPR: Computer Based Patient Record Ontology

  • CQA: Clinical Question Answering

  • DO: Disease Ontology

  • DR: Discourse Reasoning

  • DRS: Discourse Representation Structure

  • EHR: Electronic Health Record

  • FOL: First Order Logic

  • GS: Gold Standard

  • IE: Information Extraction

  • KAB: Knowledge Acquisition Bottleneck

  • KR: Knowledge Representation

  • NLP: Natural Language Processing

  • OGCP: Ontology for General Clinical Practice

  • OGMS: Ontology for General Medical Science

  • OWL2: Web Ontology Language Version 2

  • SNOMED-CT: Systematized Nomenclature of Medicine - Clinical Terms

  • SOAP: Subjective, Objective, Assessment, Plan

  • TM: Translation Memories

  • WS: Web Services

  • WSD: Word Sense Disambiguation

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