Healthc Inform Res.  2018 Oct;24(4):376-380. 10.4258/hir.2018.24.4.376.

Design and Construction of a NLP Based Knowledge Extraction Methodology in the Medical Domain Applied to Clinical Information

• Cedeño Moreno D
• Vargas-Lombardo M

• KMID: 2434553
• DOI: http://doi.org/10.4258/hir.2018.24.4.376

Abstract

OBJECTIVES
This research presents the design and development of a software architecture using natural language processing tools and the use of an ontology of knowledge as a knowledge base.
METHODS
The software extracts, manages and represents the knowledge of a text in natural language. A corpus of more than 200 medical domain documents from the general medicine and palliative care areas was validated, demonstrating relevant knowledge elements for physicians.
RESULTS
Indicators for precision, recall and F-measure were applied. An ontology was created called the knowledge elements of the medical domain to manipulate patient information, which can be read or accessed from any other software platform.
CONCLUSIONS
The developed software architecture extracts the medical knowledge of the clinical histories of patients from two different corpora. The architecture was validated using the metrics of information extraction systems.

Keyword

Knowledge; Knowledge Management; Natural Language Processing; Information Extraction

MeSH Terms

Humans
Information Storage and Retrieval
Knowledge Bases
Knowledge Management
Natural Language Processing
Palliative Care

Figure

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  Figure 1 Phases of the proposed architecture. NLP: natural language processing, JAPE: Java Annotation Pattern Engine.

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  Figure 2 Excerpt of a rule code written in JAPE (Java Annotation Pattern Engine) for the extraction of annotations.

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  Figure 3 Excerpt from the hierarchy of classes of the domain ontology.

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  Figure 4 Excerpt from the created ontology.

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  Figure 5 Segment of the Clinical Document Architecture file in XML format.

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