Clinical Concept Extraction: a Methodology Review
arXiv:1910.11377 · doi:10.1016/j.jbi.2020.103526
Abstract
Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement. Objectives In this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications. Methods Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted for retrieving EHR-based information extraction articles written in English and published from January 2009 through June 2019 from Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and the ACM Digital Library. Results A total of 6,686 publications were retrieved. After title and abstract screening, 228 publications were selected. The methods used for developing clinical concept extraction applications were discussed in this review.
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Cited by in corpus (7)
- Applications of artificial intelligence in drug development using real-world data
- Ontology-driven weak supervision for clinical entity classification in electronic health records
- Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension
- Clinical Relation Extraction Using Transformer-based Models
- Optimal strategies to perform multilingual analysis of social content for a novel dataset in the tourism domain
- High-Throughput Phenotyping of Clinical Text Using Large Language Models
- An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)