Adverse Event

Adverse event (AE) detection focuses on identifying and characterizing undesirable effects following interventions like drug administration or vaccination. Current research emphasizes leveraging machine learning, particularly deep learning models and large language models (LLMs), to automate AE extraction from diverse sources such as clinical notes, social media, and regulatory reports, often employing ensemble methods to improve accuracy and robustness. This work is crucial for enhancing pharmacovigilance, improving public health surveillance, and enabling more effective and timely responses to safety concerns related to medications and other interventions. The development of robust and generalizable models capable of handling diverse data types and languages remains a key challenge.

Papers

July 1, 2024

The Need for Guardrails with Large Language Models in Medical Safety-Critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystem
Joe B Hakim, Jeffery L Painter, Darmendra Ramcharran, Vijay Kara, Greg Powell, Paulina Sobczak, Chiho Sato, Andrew Bate, Andrew Beam
AI Application Guardrail Model Safety Critical Domain Pharmacovigilance Event Extraction Adverse Event Safety Case Medical Safety

June 26, 2024

Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources
Yiming Li, Deepthi Viswaroopan, William He, Jianfu Li, Xu Zuo, Hua Xu, Cui Tao
Large Language Model Deep Learning Entity Recognition Fine Tuned Large Language Model Multiple Source Vaccine Related Biomedical Natural Language Processing Adverse Event Adverse Drug Event Extraction

June 15, 2024

Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL
Jeffery L. Painter, Venkateswara Rao Chalamalasetti, Raymond Kassekert, Andrew Bate
Adverse Drug Pharmacovigilance Event Extraction Adverse Event Drug Safety

May 28, 2024

MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction
Xiang Dai, Sarvnaz Karimi, Abeed Sarker, Ben Hachey, Cecile Paris
Multi Domain Benchmark Adverse Event Adverse Drug Event Adverse Drug Event Extraction

March 27, 2024

A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
Lisa Raithel, Hui-Syuan Yeh, Shuntaro Yada, Cyril Grouin, Thomas Lavergne, Aurélie Névéol, Patrick Paroubek, Philippe Thomas, Tomohiro Nishiyama, Sebastian Möller, Eiji Aramaki, Yuji Matsumoto, Roland Roller, Pierre Zweigenbaum
Data Set Large Corpus Multilingual Language Model Unknown Language Multilingual Corpus Clinical Corpus Adverse Drug Pharmacovigilance Event Extraction Adverse Event

March 9, 2024

Institutional-Level Monitoring of Immune Checkpoint Inhibitor IrAEs Using a Novel Natural Language Processing Algorithmic Pipeline
Michael Shapiro, Herut Dor, Anna Gurevich-Shapiro, Tal Etan, Ido Wolf
Natural Language Processing Pipeline Adverse Event Model Monitoring Immune Checkpoint

November 15, 2023

Approaching adverse event detection utilizing transformers on clinical time-series
Helge Fredriksen, Per Joel Burman, Ashenafi Woldaregay, Karl Øyvind Mikalsen, Ståle Nymo
Anomaly Detection Transformer Megatron Decepticons Vital Sign Patient Trajectory Clinical Time Series Adverse Event Patient Phenotype

September 28, 2023

AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events
Yiming Li, Jianfu Li, Jianping He, Cui Tao
Vaccine Related Adverse Event Surveillance Application Influenza Virus

August 18, 2023

KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions
Yunzhi Qiu, Xiaokun Zhang, Weiwei Wang, Tongxuan Zhang, Bo Xu, Hongfei Lin
Knowledge Based Deep Transformer Shallow Water Adverse Drug Adverse Event Adverse Drug Reaction

March 27, 2023

From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU
Patrick Rockenschaub, Adam Hilbert, Tabea Kossen, Falk von Dincklage, Vince Istvan Madai, Dietmar Frey
Deep Learning Deep Learning Model Training Data Stronger Generalizability Intensive Care Unit Multi Center Early Warning Source Model Adverse Event

June 20, 2022

A Comparative Study on Application of Class-Imbalance Learning for Severity Prediction of Adverse Events Following Immunization
Ning Chen, Zhengke Sun, Tong Jia
Comparative Study Class Imbalance Severity Prediction ROC Curve Vaccine Related Adaboost Algorithm Adverse Event

April 19, 2022

Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning
Katsuhisa Morita, Tadahaya Mizuno, Hiroyuki Kusuhara
Machine Learning Comprehensive Investigation Data Splitting Adverse Event

December 21, 2021

Validation and Transparency in AI systems for pharmacovigilance: a case study applied to the medical literature monitoring of adverse events
Bruno Ohana, Jack Sullivan, Nicole Baker
Artificial Intelligence AI System Transparency Index Pharmacovigilance Event Extraction Adverse Event

Adverse Event

Papers

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

MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction

A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages

Institutional-Level Monitoring of Immune Checkpoint Inhibitor IrAEs Using a Novel Natural Language Processing Algorithmic Pipeline

Approaching adverse event detection utilizing transformers on clinical time-series

AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events

KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions

From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU

A Comparative Study on Application of Class-Imbalance Learning for Severity Prediction of Adverse Events Following Immunization

Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning

Validation and Transparency in AI systems for pharmacovigilance: a case study applied to the medical literature monitoring of adverse events