Automatic extraction of relations between medical concepts in clinical texts

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Recurrent Neural Networks for Classifying Relations in Clinical Notes (2017, neutral): “[53] specifically used any word used to describe the first (second) concept as features, which mitigated the effect of the inconsistent annotation of concept boundaries.”
Enhancing Clinical Concept Extraction with Contextual Embedding (2019, background): “, 2018b), and a precursor to downstream tasks such as relations (Rink et al., 2011), frames (Gupta et al.”
Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes (2018, methodology): “The top systems from i2b2/VA challenge participants still represent the state of the art for this dataset.(17,19) In order to fairly compare Seg-CNN with those systems, we used the same training and test datasets.”
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets (2019, unknown): “Chem-Prot (Peng et al., 2018); i2b2 (Rink et al., 2011); HoC (Du et al., 2019); MedNLI (Romanov and Shivade, 2018).”