scholarly journals Empirical Exploration of the Challenges in Temporal Relation Extraction from Clinical Text

2020 ◽  
Vol 27 (2) ◽  
pp. 383-409
Author(s):  
Diana Galvan-Sosa ◽  
Koji Matsuda ◽  
Naoaki Okazaki ◽  
Kentaro Inui
Keyword(s):  
Relation Extraction ◽  
Temporal Relation ◽  
Clinical Text ◽  
Temporal Relation Extraction

scholarly journals Investigating the Challenges of Temporal Relation Extraction from Clinical Text

2018 ◽  
Author(s):  
Diana Galvan ◽  
Naoaki Okazaki ◽  
Koji Matsuda ◽  
Kentaro Inui
Keyword(s):  
Relation Extraction ◽  
Temporal Relation ◽  
Clinical Text ◽  
Temporal Relation Extraction

scholarly journals Temporal Relation Extraction in Clinical Texts

2022 ◽  
Vol 54 (7) ◽  
pp. 1-36
Author(s):  
Yohan Bonescki Gumiel ◽  
Lucas Emanuel Silva e Oliveira ◽  
Vincent Claveau ◽  
Natalia Grabar ◽  
Emerson Cabrera Paraiso ◽  
...  
Keyword(s):  
Task Complexity ◽  
State Of The Art ◽  
Relation Extraction ◽  
Clinical Findings ◽  
The State ◽  
Temporal Relation ◽  
Medical Specialties ◽  
Clinical Text ◽  
Temporal Expressions ◽  
Temporal Relation Extraction

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.

Attention Neural Model for Temporal Relation Extraction

2019 ◽  
Author(s):  
Sijia Liu ◽  
Liwei Wang ◽  
Vipin Chaudhary ◽  
Hongfang Liu
Keyword(s):  
Relation Extraction ◽  
Neural Model ◽  
Temporal Relation ◽  
Temporal Relation Extraction

scholarly journals TEMPTING system: A hybrid method of rule and machine learning for temporal relation extraction in patient discharge summaries

2013 ◽  
Vol 46 ◽  
pp. S54-S62 ◽  
Author(s):  
Yung-Chun Chang ◽  
Hong-Jie Dai ◽  
Johnny Chi-Yang Wu ◽  
Jian-Ming Chen ◽  
Richard Tzong-Han Tsai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Relation Extraction ◽  
Patient Discharge ◽  
Temporal Relation ◽  
System A ◽  
Temporal Relation Extraction ◽  
Discharge Summaries

scholarly journals A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction

2020 ◽  
Author(s):  
Chen Lin ◽  
Timothy Miller ◽  
Dmitriy Dligach ◽  
Farig Sadeque ◽  
Steven Bethard ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Temporal Relation ◽  
Task Model ◽  
Temporal Relation Extraction

scholarly journals Fine-Grained Temporal Relation Extraction

2019 ◽  
Author(s):  
Siddharth Vashishtha ◽  
Benjamin Van Durme ◽  
Aaron Steven White
Keyword(s):  
Relation Extraction ◽  
Temporal Relation ◽  
Fine Grained ◽  
Temporal Relation Extraction

scholarly journals Event temporal relation extraction with attention mechanism and graph neural network

2022 ◽  
Vol 27 (1) ◽  
pp. 79-90
Author(s):  
Xiaoliang Xu ◽  
Tong Gao ◽  
Yuxiang Wang ◽  
Xinle Xuan
Keyword(s):  
Neural Network ◽  
Relation Extraction ◽  
Attention Mechanism ◽  
Temporal Relation ◽  
Temporal Relation Extraction
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