Drug–drug Interaction Extraction
14 papers with code • 3 benchmarks • 3 datasets
Automatic extraction of Drug-drug interaction (DDI) information from the biomedical literature.
( Image credit: Using Drug Descriptions and Molecular Structures for Drug-Drug Interaction Extraction from Literature )
Libraries
Use these libraries to find Drug–drug Interaction Extraction models and implementationsMost implemented papers
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters
Due to the compelling improvements brought by BERT, many recent representation models adopted the Transformer architecture as their main building block, consequently inheriting the wordpiece tokenization system despite it not being intrinsically linked to the notion of Transformers.
SSI–DDI: Substructure–Substructure Interactions for Drug–Drug Interaction Prediction
A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug–drug interactions (DDIs), which can cause serious injuries to the organism.
A Dataset for N-ary Relation Extraction of Drug Combinations
Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task.
Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network
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.
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects.
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.
Using Drug Descriptions and Molecular Structures for Drug-Drug Interaction Extraction from Literature
Specifically, we focus on drug description and molecular structure information as the drug database information.
EGFI: Drug-Drug Interaction Extraction and Generation with Fusion of Enriched Entity and Sentence Information
To address such a problem, we propose EGFI for extracting and consolidating drug interactions from large-scale medical literature text data.