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Document-Level Biomedical Relation Extraction Leveraging Pretrained Self-Attention Structure and Entity Replacement: Algorithm and Pretreatment Method Validation Study

Nevertheless, because of long-distance dependencies and co-references, their methods cannot be adapted to cross-sentence relation extraction.

Xiaofeng Liu, Jianye Fan, Shoubin Dong

JMIR Med Inform 2020;8(5):e17644

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Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation

IntroductionValuable biomedical information and knowledge are still hidden in the exponentially increasing biomedical literature, such as the chemical-disease relation (CDR).

Jian Wang, Xiaoyu Chen, Yu Zhang, Yijia Zhang, Jiabin Wen, Hongfei Lin, Zhihao Yang, Xin Wang

JMIR Med Inform 2020;8(7):e17638

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Mining of Textual Health Information from Reddit: Analysis of Chronic Diseases With Extracted Entities and Their Relations

information extraction, previous studies relied on formal research and individual case studies to identify biomedical information.

Vasiliki Foufi, Tatsawan Timakum, Christophe Gaudet-Blavignac, Christian Lovis, Min Song

J Med Internet Res 2019;21(6):e12876

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Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study

Then, we briefly review different methods of relation extraction in biomedical domains.Disease Annotation DatasetsBefore identifying DDAs, we have to identify diseases in the text first.

Po-Ting Lai, Wei-Liang Lu, Ting-Rung Kuo, Chia-Ru Chung, Jen-Chieh Han, Richard Tzong-Han Tsai, Jorng-Tzong Horng

JMIR Med Inform 2019;7(4):e14502

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Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning

The second step is relation extraction (RE) [15], which is a model that determines whether two entities have a specific relation (eg, medication and ADE).Previous studies employed traditional machine-learning techniques [15,16,23,24] such as condition random

Fei Li, Weisong Liu, Hong Yu

JMIR Med Inform 2018;6(4):e12159

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A Graph Convolutional Network–Based Method for Chemical-Protein Interaction Extraction: Algorithm Development

CPI extraction plays an important role in various biomedical tasks such as drug discovery, medicine precision, and knowledge graph construction [1].

Erniu Wang, Fan Wang, Zhihao Yang, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang

JMIR Med Inform 2020;8(5):e17643

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Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models

The postprocessing module aggregated the entity-level results to the document level for both concept extraction and relation identification subtasks.Figure 1Overview of our family history extraction system.Extracting Family History ConceptsThe concept extraction

Xi Yang, Hansi Zhang, Xing He, Jiang Bian, Yonghui Wu

JMIR Med Inform 2020;8(12):e22982

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Extraction of Family History Information From Clinical Notes: Deep Learning and Heuristics Approach

This process of extracting information is usually split in named entity recognition (NER), named entity normalization (NEN), and relation extraction (RE).

João Figueira Silva, João Rafael Almeida, Sérgio Matos

JMIR Med Inform 2020;8(12):e22898

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Family History Information Extraction With Neural Attention and an Enhanced Relation-Side Scheme: Algorithm Development and Validation

Researchers currently apply a variety of approaches to tackle the task of FHI extraction.

Hong-Jie Dai, You-Qian Lee, Chandini Nekkantti, Jitendra Jonnagaddala

JMIR Med Inform 2020;8(12):e21750

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Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning

Other resources have been shown to be useful for identifying ADEs, including biomedical literature [14] and social media [15-18]. However, biomedical literature has been shown to identify mostly a limited set of rare ADEs [19].

Tsendsuren Munkhdalai, Feifan Liu, Hong Yu

JMIR Public Health Surveill 2018;4(2):e29

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