Evidence map›Paper›PMID 35527259›Full record

ArticleJournal of biomedical semantics2022

SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks.

Lucas Emanuel Silva E Oliveira, Ana Carolina Peters, Adalniza Moura Pucca da Silva, Caroline Pilatti Gebeluca, Yohan Bonescki Gumiel, Lilian Mie Mukai Cintho, Deborah Ribeiro Carvalho, Sadid Al Hasan, Claudia Maria Cabral Moro

Abstract readMulticenter Study
In one paragraph

Article in Journal of biomedical semantics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
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  4. Disambiguation of acronyms in clinical narratives with large language models.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Lucas Emanuel Silva E OliveiraHealth Technology Program, Pontifical Catholic University of Paraná, Rua Imaculada Conceição, 1155 - Curitiba, Paraná, 80215-901, Brazil. kunkaweb@gmail.com.
Ana Carolina PetersHealth Technology Program, Pontifical Catholic University of Paraná, Rua Imaculada Conceição, 1155 - Curitiba, Paraná, 80215-901, Brazil.
Adalniza Moura Pucca da SilvaHealth Technology Program, Pontifical Catholic University of Paraná, Rua Imaculada Conceição, 1155 - Curitiba, Paraná, 80215-901, Brazil.
Caroline Pilatti GebelucaHealth Technology Program, Pontifical Catholic University of Paraná, Rua Imaculada Conceição, 1155 - Curitiba, Paraná, 80215-901, Brazil.
Yohan Bonescki GumielHealth Technology Program, Pontifical Catholic University of Paraná, Rua Imaculada Conceição, 1155 - Curitiba, Paraná, 80215-901, Brazil.
Lilian Mie Mukai CinthoHealth Technology Program, Pontifical Catholic University of Paraná, Rua Imaculada Conceição, 1155 - Curitiba, Paraná, 80215-901, Brazil.
Deborah Ribeiro CarvalhoHealth Technology Program, Pontifical Catholic University of Paraná, Rua Imaculada Conceição, 1155 - Curitiba, Paraná, 80215-901, Brazil.
Sadid Al HasanAI Lab, Philips Research North America, Cambridge, MA, USA.
Claudia Maria Cabral MoroHealth Technology Program, Pontifical Catholic University of Paraná, Rua Imaculada Conceição, 1155 - Curitiba, Paraná, 80215-901, Brazil.

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Finance Code 001
6 · The paper itself

Abstract

backgroundThe high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field.

methodsIn this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations.

resultsThis study resulted in SemClinBr, a corpus that has 1000 clinical notes, labeled with 65,117 entities and 11,263 relations. In addition, both negation cues and medical abbreviation dictionaries were generated from the annotations. The average annotator agreement score varied from 0.71 (applying strict match) to 0.92 (considering a relaxed match) while accepting partial overlaps and hierarchically related semantic types. The extrinsic evaluation, when applying the corpus to two downstream NLP tasks, demonstrated the reliability and usefulness of annotations, with the systems achieving results that were consistent with the agreement scores.

conclusionThe SemClinBr corpus and other resources produced in this work can support clinical NLP studies, providing a common development and evaluation resource for the research community, boosting the utilization of EHRs in both clinical practice and biomedical research. To the best of our knowledge, SemClinBr is the first available Portuguese clinical corpus.

Indexed as

MedicineNatural Language ProcessingElectronic Health RecordsHumansPortugalReproducibility of ResultsClinical narrativesCorporaGold standardNatural language processingSemantic annotation

Identifiers

PMID35527259
PMCPMC9080187

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.