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 with no code
Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distribution
Federated medical relation extraction enables multiple clients to train a deep network collaboratively without sharing their raw medical data.
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records
GatorTron models scale up the clinical language model from 110 million to 8. 9 billion parameters and improve 5 clinical NLP tasks (e. g., 9. 6% and 9. 5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery.
A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extraction
In this paper, inspired by the tree-like relation structures in the medical text, we propose a novel scheme called Bidirectional Tree Tagging (BiTT) to form the medical relation triples into two two binary trees and convert the trees into a word-level tags sequence.
