Medical Relation Extraction
9 papers with code • 2 benchmarks • 5 datasets
Biomedical relation extraction is the task of detecting and classifying semantic relationships from biomedical text.
Benchmarks
These leaderboards are used to track progress in Medical Relation Extraction
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DDI
RadGraph
Latest papers
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest
However, the quality of the 1-best dependency tree for medical texts produced by an out-of-domain parser is relatively limited so that the performance of medical relation extraction method may degenerate.
LinkBERT: Pretraining Language Models with Document Links
michiyasunaga/LinkBERT
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ACL 2022
Language model (LM) pretraining can learn various knowledge from text corpora, helping downstream tasks.
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
cbluebenchmark/cblue
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ACL 2022
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Leveraging Dependency Forest for Neural Medical Relation Extraction
freesunshine0316/dep-forest-re
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IJCNLP 2019
Medical relation extraction discovers relations between entity mentions in text, such as research articles.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
dmis-lab/biobert
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Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
A hybrid deep learning approach for medical relation extraction
Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain.
Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network
sunilitggu/DDI-extraction-through-LSTM
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The two models, {\it AB-LSTM} and {\it Joint AB-LSTM} also use attentive pooling in the output of Bi-LSTM layer to assign weights to features.
Crowdsourcing Ground Truth for Medical Relation Extraction
Cognitive computing systems require human labeled data for evaluation, and often for training.
