Concept Attribute Labeling and Context-Aware Named Entity Recognition in Electronic Health Records

Alexandra Pomares-Quimbaya, Rafael A. Gonzalez, Oscar Mauricio Muñoz Velandia, Angel Alberto Garcia Peña, Julián Camilo Daza Rodríguez, Alejandro Sierra Múnera and Cyril Labbé
Additional contact information
Alexandra Pomares-Quimbaya: Pontificia Universidad Javeriana, Bogotá, Colombia
Rafael A. Gonzalez: Pontificia Universidad Javeriana, Bogotá, Colombia
Oscar Mauricio Muñoz Velandia: Hospital Universitario San Ignacio, Pontificia Universidad Javeriana, Bogotá, Colombia
Angel Alberto Garcia Peña: Hospital Universitario San Ignacio, Pontificia Universidad Javeriana, Bogotá, Colombia
Julián Camilo Daza Rodríguez: Hospital Universitario San Ignacio, Pontificia Universidad Javeriana, Bogotá, Colombia
Alejandro Sierra Múnera: Hospital Universitario San Ignacio, Pontificia Universidad Javeriana, Bogotá, Colombia
Cyril Labbé: Laboratoire d'Informatique de Grenoble, Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France

International Journal of Reliable and Quality E-Healthcare (IJRQEH), 2018, vol. 7, issue 1, 1-15

Abstract: Extracting valuable knowledge from Electronic Health Records (EHR) represents a challenging task due to the presence of both structured and unstructured data, including codified fields, images and test results. Narrative text in particular contains a variety of notes which are diverse in language and detail, as well as being full of ad hoc terminology, including acronyms and jargon, which is especially challenging in non-English EHR, where there is a dearth of annotated corpora or trained case sets. This paper proposes an approach for NER and concept attribute labeling for EHR that takes into consideration the contextual words around the entity of interest to determine its sense. The approach proposes a composition method of three different NER methods, together with the analysis of the context (neighboring words) using an ensemble classification model. This contributes to disambiguate NER, as well as labeling the concept as confirmed, negated, speculative, pending or antecedent. Results show an improvement of the recall and a limited impact on precision for the NER process.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJRQEH.2018010101 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jrqeh0:v:7:y:2018:i:1:p:1-15

Access Statistics for this article

International Journal of Reliable and Quality E-Healthcare (IJRQEH) is currently edited by Anastasius Moumtzoglou

More articles in International Journal of Reliable and Quality E-Healthcare (IJRQEH) from IGI Global
Bibliographic data for series maintained by Journal Editor ().