Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations

By Yuan Luo, Ozlem Uzuner, Peter Szolovits. 2017

75 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Information Extraction, Computational Biology & Bioinformatics

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Citations in the corpus

Graph Convolutional Networks for Text Classification (2018, neutral): “There are also existing studies on converting texts to graphs and perform feature engineering on graphs and sub-graphs (Luo, Uzuner, and Szolovits 2016; Rousseau, Kia-gias, and Vazirgiannis 2015; Skianis, Rousseau, and Vazir-giannis 2016; Luo et al. 2014; Luo et al. 2015).”
Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “Understanding these relations is important for knowledge representation and inference to augment structured knowledge bases [182, 183].”
Recurrent Neural Networks for Classifying Relations in Clinical Notes (2017, neutral): “In the biomedical and clinical domain, extracting relations from scientific publications and clinical narratives has also been an important focus over the past decade with numerous challenges due to the complexity of language and domain specific knowledge involved [2].”
Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, methodology): “Both biomedical named entity recognition (NER) and relation extraction have been active research areas for decades [13, 14].”
Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction (2017, unknown): “Intuitively, the temporal trends, and in general relations as features, are more expressive and informative, but their extraction is often difficult and often involves manual work such as prespecifying rules or patterns [18] and matching against clinical time series [19].”
Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes (2018, background): “Unfortunately, systems that use human-engineered features often do not generalize well to new datasets.(3,28) Recent studies on applying convolutional neural networks (CNNs) to clinical datasets aimed to automatically learn feature representations to reduce the need for engineered features and have ”