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Pharmacovigilance
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Identifying adverse drug events and suspect drugs
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance
Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide
Task as Context Prompting for Accurate Medical Symptom Coding Using Large Language Models
PVLens: Enhancing Pharmacovigilance Through Automated Label Extraction
Ontology-based Semantic Similarity Measures for Clustering Medical Concepts in Drug Safety
HODDI: A Dataset of High-Order Drug-Drug Interactions for Computational Pharmacovigilance
MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilance
The Need for Guardrails with Large Language Models in Medical Safety-Critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystem
Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources
Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL
Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study
Extensive Evaluation of Transformer-based Architectures for Adverse Drug Events Extraction
BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance
PHEE: A Dataset for Pharmacovigilance Event Extraction from Text
Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation
AI-based Approach for Safety Signals Detection from Social Networks: Application to the Levothyrox Scandal in 2017 on Doctissimo Forum
Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?
Validation and Transparency in AI systems for pharmacovigilance: a case study applied to the medical literature monitoring of adverse events
Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies
Investigating ADR mechanisms with knowledge graph mining and explainable AI
Retrofitting Vector Representations of Adverse Event Reporting Data to Structured Knowledge to Improve Pharmacovigilance Signal Detection
Detecting Adverse Drug Reactions from Twitter through Domain-Specific Preprocessing and BERT Ensembling
Enhancing Pharmacovigilance with Drug Reviews and Social Media
A large-scale Twitter dataset for drug safety applications mined from publicly existing resources
GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries
Handling temporality of clinical events with application to Adverse Drug Event detection in Electronic Health Records: A scoping review
Did you take the pill? - Detecting Personal Intake of Medicine from Twitter
#phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter
Automated Detection of Adverse Drug Reactions in the Biomedical Literature Using Convolutional Neural Networks and Biomedical Word Embeddings
InfyNLP at SMM4H Task 2: Stacked Ensemble of Shallow Convolutional Neural Networks for Identifying Personal Medication Intake from Twitter
Multi-Task Pharmacovigilance Mining from Social Media Posts
Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction
Interpreting Classifiers through Attribute Interactions in Datasets
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.