Work

Extracting semantic lexicons from discharge summaries using machine learning and the C-Value method.

Year: 2012

Type: conference-paper

Abstract: Semantic lexicons that link words and phrases to specific semantic types such as diseases are valuable assets for clinical natural language processing (NLP) systems. Although terminological terms with predefined semantic types can be generated easily from existing knowledge bases such as the Unified Medical Language Systems (UMLS), they are often limited and do not have good coverage for narrative clinical text. In this study, we developed a method for building semantic lexicons from clinical corpus. It extracts candidate semantic terms using a conditional random field (CRF) classifier and then selects terms using the C-Value algorithm. We applied the method to a corpus containing 10 years of discharge summaries from Vanderbilt University Hospital (VUH) and extracted 44,957 new terms for three semantic groups: Problem, Treatment, and Test. A manual analysis of 200 randomly selected terms not found in the UMLS demonstrated that 59% of them were meaningful new clinical concepts and 25% were lexical variants of exiting concepts in the UMLS. Furthermore, we compared the effectiveness of corpus-derived and UMLS-derived semantic lexicons in the concept extraction task of the 2010 i2b2 clinical NLP challenge. Our results showed that the classifier with corpus-derived semantic lexicons as features achieved a better performance (F-score 82.52%) than that with UMLS-derived semantic lexicons as features (F-score 82.04%). We conclude that such corpus-based methods are effective for generating semantic lexicons, which may improve named entity recognition tasks and may aid in augmenting synonymy within existing terminologies.

Source: PubMed

Authors: Min Jiang, Josh C Denny, Buzhou Tang, Hongxin Cao, Hua Xu

Institutions: Vanderbilt University, Jilin University

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Cancer Institute, U.S. National Library of Medicine, National Center for Research Resources

Awards: Interactive machine learning methods for clinical natural language processing, Beyond PheWAS: Recognition of Phenotype Patterns for Discovery and Translation, The Vanderbilt Institute for Clinical and Translational Research (VICTR) UL1, An insilico method for epidemiological studies using Electonic Medical Records