research-article

Automatic Extraction of Nested Entities in Clinical Referrals in Spanish

Authors:

Felipe Bravo-Marquez,

Jocelyn Dunstan,

Fabián VillenaAuthors Info & Claims

ACM Transactions on Computing for Healthcare (HEALTH), Volume 3, Issue 3

Article No.: 28, Pages 1 - 22

https://doi.org/10.1145/3498324

Published: 07 April 2022 Publication History

Abstract

Here we describe a new clinical corpus rich in nested entities and a series of neural models to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 5,000 referrals (58.6% medical and 41.4% dental) was manually annotated with 10 types of entities, six attributes, and pairs of relations with clinical relevance. In total, there are 110,771 annotated tokens. A trained medical doctor or dentist annotated these referrals, and then, together with three other researchers, consolidated each of the annotations. The annotated corpus has 48.17% of entities embedded in other entities or containing another one. We use this corpus to build models for Named Entity Recognition (NER). The best results were achieved using a Multiple Single-entity architecture with clinical word embeddings stacked with character and Flair contextual embeddings. The entity with the best performance is abbreviation, and the hardest to recognize is finding. NER models applied to this corpus can leverage statistics of diseases and pending procedures. This work constitutes the first annotated corpus using clinical narratives from Chile and one of the few in Spanish. The annotated corpus, clinical word embeddings, annotation guidelines, and neural models are freely released to the community.

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https://doi.org/10.1200/CCI.24.00124
Villena FBravo-Marquez FDunstan J(2025)NLP modeling recommendations for restricted data availability in clinical settingsBMC Medical Informatics and Decision Making10.1186/s12911-025-02948-225:1Online publication date: 7-Mar-2025
https://doi.org/10.1186/s12911-025-02948-2
Campillos-Llanos LValverde-Mateos ACapllonch-Carrión A(2025)Hybrid natural language processing tool for semantic annotation of medical texts in SpanishBMC Bioinformatics10.1186/s12859-024-05949-626:1Online publication date: 8-Jan-2025
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Index Terms

Automatic Extraction of Nested Entities in Clinical Referrals in Spanish
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
      2. Language resources

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cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 3, Issue 3

July 2022

251 pages

EISSN:2637-8051

DOI:10.1145/3514183

Editors:
Insup Lee
University of Pennsylvania, USA
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John A. Stankovic
University of Virginia, USA

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Publication History

Published: 07 April 2022

Accepted: 01 November 2021

Revised: 01 October 2021

Received: 01 November 2020

Published in HEALTH Volume 3, Issue 3

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https://doi.org/10.1200/CCI.24.00124
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Campillos-Llanos LValverde-Mateos ACapllonch-Carrión A(2025)Hybrid natural language processing tool for semantic annotation of medical texts in SpanishBMC Bioinformatics10.1186/s12859-024-05949-626:1Online publication date: 8-Jan-2025
https://doi.org/10.1186/s12859-024-05949-6
Ahumada RDunstan JRojas MPeñafiel SParedes IBáez P(2024)Automatic Detection of Distant Metastasis Mentions in Radiology Reports in SpanishJCO Clinical Cancer Informatics10.1200/CCI.23.00130Online publication date: Mar-2024
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