Clinical Decision Support Systems Question and Answering

Clinical Decision Support Systems Question and Answering

David José Murteira Mendes, Irene Pimenta Rodrigues, César Fonseca
Copyright: © 2021 |Pages: 12
DOI: 10.4018/978-1-7998-9023-2.ch026
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Abstract

A question answering system to help clinical practitioners in a cardiovascular healthcare environment to interface clinical decision support systems can be built by using an extended discourse representation structure, CIDERS, and an ontology framework, Ontology for General Clinical Practice. CIDERS is an extension of the well-known DRT (discourse representation theory) structures, intending to go beyond single text representation to embrace the general clinical history of a given patient represented in an ontology. The Ontology for General Clinical Practice improves the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty. The chapter shows 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). To be able to use the current reasoning techniques and methodologies, the authors made a thorough inventory of biomedical ontologies currently available in OWL2 format.
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Este estudo é integrante do Programa de Cooperação INTERREG V - A Espanha - Portugal (POCTEP 0445_4IE_4_P), no âmbito da criação do Instituto Internacional de Investigação e Inovação do Envelhecimento (com financiamento de 1 346 288,05 €).

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1. Work In Progress And Motivation

We will present in this paper two sections that illustrate our present work status:

  • 1.

    The steps that we consider are involved in the complex acquisition procedure of clinical concepts expressed in English from text in Portuguese.

  • 2.

    A proposal of a Software architecture needed for an automated acquisition to articulate the steps presented before.

Our work final objective is to enrich/populate an ontology that shall allow us to devise AI (Artificial Intelligence) tools that reason about Clinical Practice. Given the reasons explained in (D. Mendes & Rodrigues, 2011b) we chose CPR (Computer-based Patient Record) Ontology as target for population. CPR is a W3C (World Wide Web Consortium) standard for representing clinical practice knowledge. In the mentioned paper, we itemize the different issues faced and explore some of the possible solutions in population/enrichment of the ontology. The major problem of personal jargon creation was not properly addressed, however, and so is fully discussed in the present article. We are in the process of demonstrating the possibility of information extraction from free-text clinical episode reports in an automated manner.

When developing a methodology for automatic population of a CPR Ontology we faced the particular problem of clinical concept recognition when dealing with Portuguese natural language text. After consulting with several MD (Medical Doctors) whose activity is the main subject of representing knowledge that way, we found that we can take into our advantage the fact that each one usually develops his/her own way of writing down their daily chores. What we have to develop is a way of maintaining an acquired controlled vocabulary. Assuming that this is a task involving NLP (Natural Language Processing) specifically for Portuguese, we presented in (D. Mendes, Rodrigues, Rodriguez-Solano, & Baeta, 2012) some techniques that can be easily applicable to different languages with the same set of constraints presented ahead.

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2. Experiencies In The Field

2.1 The ULSNA Experiment

The ULSNA, E.P.E. (http://www.ulsna.min-saude.pt/) has as its principal object the provision of primary and secondary health care, rehabilitation, palliative and integrated continued care to the population and the means necessary to exercise the powers of the health authority in the geographic area affected by it. ULSNA is a healthcare providing regional system that includes two hospitals (José Maria Grande in Portalegre and Santa Luzia in Elvas) and the primary care centers in all the district counties. Universidade de Évora signed an agreement with ULSNA that enabled the usage of de-identified (according to safe-harbor principles as reviewed in (Meystre, Friedlin, South, Shen, & Samore, 2010) clinical data from the SAM system in use both in the Primary Healthcare units and in the Hospitals. Using the clinical data that is available for us we intend to take advantage of the tooling available to reach the objectives mentioned in section 1.

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