Extraction of Lactation Frames from Drug Labels and LactMed
This paper describes a natural language processing (NLP) approach to extracting lactation-specific drug information from two sources: FDA-mandated drug labels and the NLM Drugs and Lactation Database (LactMed). A frame semantic approach is utilized, and the paper describes the selected frames, their annotation on a set of 900 sections from drug labels and LactMed articles, and the NLP system to extract such frame instances automatically. The ultimate goal of the project is to use such a system to identify discrepancies in lactation-related drug information between these resources.
Code (0)
등록된 구현이 없습니다.
Tasks
ArticlesSimilar Papers 제목 키워드 기반
A Multi-Task Learning Framework for Extracting Drugs and Their Interactions from Drug Labels
Preventable adverse drug reactions as a result of medical errors present a growing concern in modern medicine. As drug-drug interactions (DDIs) may cause adverse reactions, being able to extracting DDIs from drug labels …
Multi-Task Learningnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+4Two Step Joint Model for Drug Drug Interaction Extraction
When patients need to take medicine, particularly taking more than one kind of drug simultaneously, they should be alarmed that there possibly exists drug-drug interaction. Interaction between drugs may have a negative i…
DecoderDrug–drug Interaction Extractionnamed-entity-recognitionNamed Entity Recognition+4MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilance
In the era of Large Language Models (LLMs), given their remarkable text understanding and generation abilities, there is an unprecedented opportunity to develop new, LLM-based methods for trustworthy medical knowledge sy…
PharmacovigilanceRetrievalRetrieval-augmented GenerationAttention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels
Preventable adverse events as a result of medical errors present a growing concern in the healthcare system. As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug l…
Relation ExtractionTransfer LearningPVLens: Enhancing Pharmacovigilance Through Automated Label Extraction
Reliable drug safety reference databases are essential for pharmacovigilance, yet existing resources like SIDER are outdated and static. We introduce PVLens, an automated system that extracts labeled safety information f…
Pharmacovigilance