scholarly journals HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V

Database ◽  
2016 ◽  
Vol 2016 ◽  
pp. baw077 ◽  
Author(s):  
Haodi Li ◽  
Buzhou Tang ◽  
Qingcai Chen ◽  
Kai Chen ◽  
Xiaolong Wang ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Extraction System ◽  
End To End

scholarly journals EliIE: An open-source information extraction system for clinical trial eligibility criteria

2017 ◽  
Vol 24 (6) ◽  
pp. 1062-1071 ◽  
Author(s):  
Tian Kang ◽  
Shaodian Zhang ◽  
Youlan Tang ◽  
Gregory W Hruby ◽  
Alexander Rusanov ◽  
...  
Keyword(s):  
Information Extraction ◽  
Open Source ◽  
Relation Extraction ◽  
Extraction System ◽  
Free Text ◽  
Source Information ◽  
Eligibility Criteria ◽  
Negation Detection ◽  
Attribute Recognition ◽  
Information Extraction System

Abstract Objective To develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5.0. Materials and Methods EliIE parses EC in 4 steps: (1) clinical entity and attribute recognition, (2) negation detection, (3) relation extraction, and (4) concept normalization and output structuring. Informaticians and domain experts were recruited to design an annotation guideline and generate a training corpus of annotated EC for 230 Alzheimer’s clinical trials, which were represented as queries against the OMOP CDM and included 8008 entities, 3550 attributes, and 3529 relations. A sequence labeling–based method was developed for automatic entity and attribute recognition. Negation detection was supported by NegEx and a set of predefined rules. Relation extraction was achieved by a support vector machine classifier. We further performed terminology-based concept normalization and output structuring. Results In task-specific evaluations, the best F1 score for entity recognition was 0.79, and for relation extraction was 0.89. The accuracy of negation detection was 0.94. The overall accuracy for query formalization was 0.71 in an end-to-end evaluation. Conclusions This study presents EliIE, an OMOP CDM–based information extraction system for automatic structuring and formalization of free-text EC. According to our evaluation, machine learning-based EliIE outperforms existing systems and shows promise to improve.

scholarly journals Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction

2021 ◽  
Author(s):  
Bruno Taillé ◽  
Vincent Guigue ◽  
Geoffrey Scoutheeten ◽  
Patrick Gallinari
Keyword(s):  
Relation Extraction ◽  
End To End

scholarly journals Improving Graph Convolutional Networks Based on Relation-Aware Attention for End-to-End Relation Extraction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 51315-51323 ◽  
Author(s):  
Yin Hong ◽  
Yanxia Liu ◽  
Suizhu Yang ◽  
Kaiwen Zhang ◽  
Aiqing Wen ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Convolutional Networks ◽  
End To End

scholarly journals Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods

2019 ◽  
Vol 27 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Fenia Christopoulou ◽  
Thy Thy Tran ◽  
Sunil Kumar Sahu ◽  
Makoto Miwa ◽  
Sophia Ananiadou
Keyword(s):  
Electronic Health Records ◽  
Adverse Drug Events ◽  
Short Term Memory ◽  
Relation Extraction ◽  
Short Term ◽  
Health Records ◽  
Term Memory ◽  
Long Short Term Memory ◽  
End To End ◽  
Electronic Health

AbstractObjectiveIdentification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared-task in extracting relations between medication-related entities in electronic health records.Materials and MethodsWe proposed an ensemble approach for relation extraction and classification between drugs and medication-related entities. We incorporated state-of-the-art named-entity recognition (NER) models based on bidirectional long short-term memory (BiLSTM) networks and conditional random fields (CRF) for end-to-end extraction. We additionally developed separate models for intra- and inter-sentence relation extraction and combined them using an ensemble method. The intra-sentence models rely on bidirectional long short-term memory networks and attention mechanisms and are able to capture dependencies between multiple related pairs in the same sentence. For the inter-sentence relations, we adopted a neural architecture that utilizes the Transformer network to improve performance in longer sequences.ResultsOur team ranked third with a micro-averaged F1 score of 94.72% and 87.65% for relation and end-to-end relation extraction, respectively (Tracks 2 and 3). Our ensemble effectively takes advantages from our proposed models. Analysis of the reported results indicated that our proposed approach is more generalizable than the top-performing system, which employs additional training data- and corpus-driven processing techniques.ConclusionsWe proposed a relation extraction system to identify relations between drugs and medication-related entities. The proposed approach is independent of external syntactic tools. Analysis showed that by using latent Drug-Drug interactions we were able to significantly improve the performance of non–Drug-Drug pairs in EHRs.

Sign in / Sign up

Export Citation Format

Share Document