temporal relation extraction
Recently Published Documents





2022 ◽  
Vol 54 (7) ◽  
pp. 1-36
Yohan Bonescki Gumiel ◽  
Lucas Emanuel Silva e Oliveira ◽  
Vincent Claveau ◽  
Natalia Grabar ◽  
Emerson Cabrera Paraiso ◽  

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.

2021 ◽  
Pengfei Cao ◽  
Xinyu Zuo ◽  
Yubo Chen ◽  
Kang Liu ◽  
Jun Zhao ◽  

2021 ◽  
Puneet Mathur ◽  
Rajiv Jain ◽  
Franck Dernoncourt ◽  
Vlad Morariu ◽  
Quan Hung Tran ◽  

2020 ◽  
Vol 27 (2) ◽  
pp. 383-409
Diana Galvan-Sosa ◽  
Koji Matsuda ◽  
Naoaki Okazaki ◽  
Kentaro Inui

2020 ◽  
Chen Lin ◽  
Timothy Miller ◽  
Dmitriy Dligach ◽  
Farig Sadeque ◽  
Steven Bethard ◽  

Sign in / Sign up

Export Citation Format

Share Document