Clinical Practice Ontology Automatic Learning from SOAP Reports

Clinical Practice Ontology Automatic Learning from SOAP Reports

David Mendes (Universidade de Évora, Portugal), Irene Pimenta Rodrigues (Universidade de Évora, Portugal) and Carlos Fernandes Baeta (Unidade Local de Saúde do Norte Alentejano, Portugal)
Copyright: © 2016 |Pages: 15
ISBN13: 9781466688285|ISBN10: 1466688289|EISBN13: 9781466688292|ISSN: 2475-6628|EISSN: 2475-6636
DOI: 10.4018/978-1-4666-8828-5.ch016
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MLA

Mendes, David, Irene Pimenta Rodrigues and Carlos Fernandes Baeta. "Clinical Practice Ontology Automatic Learning from SOAP Reports." Handbook of Research on Trends in the Diagnosis and Treatment of Chronic Conditions. IGI Global, 2016. 349-363. Web. 27 Mar. 2020. doi:10.4018/978-1-4666-8828-5.ch016

APA

Mendes, D., Rodrigues, I. P., & Baeta, C. F. (2016). Clinical Practice Ontology Automatic Learning from SOAP Reports. In D. Fotiadis (Ed.), Handbook of Research on Trends in the Diagnosis and Treatment of Chronic Conditions (pp. 349-363). Hershey, PA: IGI Global. doi:10.4018/978-1-4666-8828-5.ch016

Chicago

Mendes, David, Irene Pimenta Rodrigues and Carlos Fernandes Baeta. "Clinical Practice Ontology Automatic Learning from SOAP Reports." In Handbook of Research on Trends in the Diagnosis and Treatment of Chronic Conditions, ed. Dimitrios I. Fotiadis, 349-363 (2016), accessed March 27, 2020. doi:10.4018/978-1-4666-8828-5.ch016

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Abstract

We show how we implemented an end-to-end process to automatically develop a clinical practice knowledge base acquiring from SOAP notes. With our contribution we intend to overcome the “Knowledge Acquisition Bottleneck” problem by jump-starting the knowledge gathering from the most widely available source of clinical information that are natural language reports. We present the different phases of our process to populate automatically a proposed ontology with clinical assertions extracted from daily routine SOAP notes. The enriched ontology becomes a reasoning able knowledge base that depicts accurately and realistically the clinical practice represented by the source reports. With this knowledge structure in place and novel state-of-the-art reasoning capabilities, based in consequence driven reasoners, a clinical QA system based in controlled natural language is introduced that reveals breakthrough possibilities regarding the applicability of Artificial Intelligence techniques to the medical field.

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