Enrichment/Population of Customized CPR (Computer-Based Patient Record) Ontology from Free-Text Reports for CSI (Computer Semantic Interoperability)

Author:

Mendes David1,Rodrigues Irene Pimenta1,Rodriguez-Solano Carlos2,Baeta Carlos Fernandes3

Affiliation:

1. Universidade de Évora, Évora, Portugal

2. Universidad de Alcalá, Alcalá de Henares, Spain

3. Unidade Local de Saúde do Norte Alentejano, Portalegre, Portugal

Abstract

CSI (Computer Semantic Interoperability) is a very important issue in healthcare. Ways for heterogeneous computer systems to “understand” important facts from the clinical process for clinical decision support are now beginning to be addressed. The authors present here comprehensive contributions to achieve CSI. EHR (Electronic Health Record) systems provide a way to extract reports of the clinicians activity. In order to formalize an automated acquisition from semi-structured, free-form, natural language texts in Portuguese into a Clinical Practice Ontology an important step is to develop the ability of decoding all the nicknames, acronyms and short-hand forms that each clinician tend to write down in their reports. The authors present the steps to develop clinical vocabularies extracting directly from clinical reports in Portuguese available in the SAM (Sistema de Apoio ao Médico) system. The presented techniques are easily further developed for any other natural language or knowledge representation framework with due adaptations.

Publisher

IGI Global

Subject

General Computer Science

Reference9 articles.

1. Mistakes in medical ontologies: Where do they come from and how can they be detected?;W.Ceusters;Studies in Health Technology and Informatics,2004

2. Mendes, D., & Rodrigues, I. P. (2011). Well formed clinical practice ontology selection. In Jornadas de Informática da Universidade de Évora.

3. Mendes, D., & Rodrigues, I. P. (2011b). A semantic web pragmatic approach to develop clinical ontologies, and thus semantic interoperability, based in HL7 v2.xml messaging. In Proceedings of the International Workshop on Health and Social Care Information Systems and Technologies (HCist 2011). Springer-Verlag.

4. Mendes, D., & Rodrigues, I. P. (2011c). Automatic ontology population extracted from SAM healthcare texts in Portuguese. In Jornadas de Informática da Universidade de Évora.

5. Automatic de-identification of textual documents in the electronic health record: a review of recent research

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