Work

Feasibility of pooling annotated corpora for clinical concept extraction.

Year: 2012

Type: article

Abstract: Availability of annotated corpora has facilitated application of machine learning algorithms to concept extraction from clinical notes. However, it is expensive to prepare annotated corpora in individual institutions, and pooling of annotated corpora from other institutions is a potential solution. In this paper we investigate whether pooling of corpora from two different sources, can improve performance and portability of resultant machine learning taggers for medical problem detection. Specifically, we pool corpora from 2010 i2b2/VA NLP challenge and Mayo Clinic Rochester, to evaluate taggers for recognition of medical problems. Contrary to our expectations, pooling of corpora is found to decrease the F1-score. We examine the annotation guidelines to identify factors for incompatibility of the corpora and suggest development of a standard annotation guideline by the clinical NLP community to allow compatibility of annotated corpora.

Source: Europe PMC (PubMed Central)

Authors: Kavishwar Wagholikar, Manabu Torii, Siddhartha Jonnalagadda, Hongfang Liu

Institution: Mayo Clinic

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institute of General Medical Sciences, U.S. National Library of Medicine

Awards: Natural language processing for clinical and translational research, Onto-BioThesaurus: ontological representation of gene/protein names for biomedica