Titre :

Temporal Relation Extraction in Clinical Texts

Auteur(s) :

Gumiel, Yohan Bonescki [Auteur]
Pontifícia Universidade Católica do Paraná [Curitiba, Brasil] = Pontifical Catholic University of Paraná [Curitiba, Brazil] = Université catholique pontificale du Paraná [Curitiba, Brésil] [PUCPR]
Silva E Oliveira, Lucas Emanuel [Auteur]
Pontifícia Universidade Católica do Paraná [Curitiba, Brasil] = Pontifical Catholic University of Paraná [Curitiba, Brazil] = Université catholique pontificale du Paraná [Curitiba, Brésil] [PUCPR]
Claveau, Vincent [Auteur]
Creating and exploiting explicit links between multimedia fragments [LinkMedia]
Grabar, Natalia [Auteur]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Paraiso, Emerson Cabrera [Auteur]
Pontifícia Universidade Católica do Paraná [Curitiba, Brasil] = Pontifical Catholic University of Paraná [Curitiba, Brazil] = Université catholique pontificale du Paraná [Curitiba, Brésil] [PUCPR]
Moro, Claudia [Auteur]
Pontifícia Universidade Católica do Paraná [Curitiba, Brasil] = Pontifical Catholic University of Paraná [Curitiba, Brazil] = Université catholique pontificale du Paraná [Curitiba, Brésil] [PUCPR]
Carvalho, Deborah Ribeiro [Auteur]
Pontifícia Universidade Católica do Paraná [Curitiba, Brasil] = Pontifical Catholic University of Paraná [Curitiba, Brazil] = Université catholique pontificale du Paraná [Curitiba, Brésil] [PUCPR]

Titre de la revue :

ACM Computing Surveys

Pagination :

1-36

Éditeur :

Association for Computing Machinery

Date de publication :

2022-09-30

ISSN :

0360-0300

Discipline(s) HAL :

Informatique [cs]/Recherche d'information [cs.IR]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Informatique et langage [cs.CL]

Résumé en anglais : [en]

Unstructured data in electronic health records, represented by clinical texts, are a vast source of healthcare information because they describe a patient's journey, including clinical findings, procedures, and information ...
Lire la suite >Unstructured data in electronic health records, represented by clinical texts, are a vast source of healthcare information because they describe a patient's journey, including clinical findings, procedures, and information about the continuity of care. The publication of several studies on temporal relation extraction from clinical texts during the last decade and the realization of multiple shared tasks highlight the importance of this research theme. Therefore, we propose a review of temporal relation extraction in clinical texts. We analyzed 105 articles and verified that relations between events and document creation time, a coarse temporality type, were addressed with traditional machine learning–based models with few recent initiatives to push the state-of-the-art with deep learning–based models. For temporal relations between entities (event and temporal expressions) in the document, factors such as dataset imbalance because of candidate pair generation and task complexity directly affect the system's performance. The state-of-the-art resides on attention-based models, with contextualized word representations being fine-tuned for temporal relation extraction. However, further experiments and advances in the research topic are required until real-time clinical domain applications are released. Furthermore, most of the publications mainly reside on the same dataset, hindering the need for new annotation projects that provide datasets for different medical specialties, clinical text types, and even languages.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Savoirs, Textes, Langage (STL) - UMR 8163

Source :

Harvested from HAL