Purpose: To develop a framework for identifying and incorporating candidate confounding interaction terms into a regularised cox regression analysis to refine adverse drug reaction signals obtained via longitudinal observational data. Methods: We considered six drug families that are commonly associated with myocardial infarction in observational healthcare data, but where the causal relationship ground truth is known (adverse drug reaction or not). We applied emergent pattern mining to find itemsets of drugs and medical events that are associated with the development of myocardial infarction. These are the candidate confounding interaction terms. We then implemented a cohort study design using regularised cox regression that incorporated and accounted for the candidate confounding interaction terms. Results The methodology was able to account for signals generated due to confounding and a cox regression with elastic net regularisation correctly ranked the drug families known to be true adverse drug reactions above those.
HELIOT: LLM-Based CDSS for Adverse Drug Reaction Management
Software Engineering (SeSa) Lab University of Salerno（软件工程（SeSa）实验室，萨勒诺大学）；Amsterdam UMC Locatie AMC Amsterdam, The Netherlands（阿姆斯特丹大学医学中心（AMC）阿姆斯特丹，荷兰）；Digital Health（数字健康；方法学；阿姆斯特丹公共卫生研究所阿姆斯特丹，荷兰）；Methodology（阿姆斯特丹大学数据科学中心阿姆斯特丹，荷兰）；Amsterdam Public Health Research Institute Amsterdam, The Netherlands；University of Amsterdam Data Science Center Amsterdam, The Netherlands
GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance
Department of Computer Science & Engineering, Indian Institute of Technology Patna（印度理工学院帕纳布分校计算机科学与工程系）；Indira Gandhi Institute of Medical Sciences, Patna（英伽丁医学科学学院）
Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning
University of Southampton（南安普顿大学）；University of Science and Technology of China（中国科学技术大学）；San Francisco Bay University（旧金山湾大学）；Trine University（特莱恩大学）；The Hong Kong University of Science and Technology（香港科技大学）；SouthWest JiaoTong University（西南交通大学）
Causal knowledge graph analysis identifies adverse drug effects
Computer, Electrical and Mathematical Sciences & Engineering（计算机、电气与数学科学与工程）；King Abdullah University of Science and Technology（卡布斯科学技术大学）；University of Cambridge（剑桥大学）；SDAIA–KAUST Center of Excellence in Data Science and Artificial Intelligence（数据科学与人工智能卓越中心）；KAUST Center of Excellence for Smart Health (KCSH)（智能健康卓越中心）
提出融合形式化因果语义与演绎推理的因果知识图谱 CKG，并基于 DD-CKG 在 UK Biobank 和 MIMIC-IV 中开展大规模中介分析，高精度复现并发现药物不良反应。
A data science approach to drug safety: Semantic and visual mining of adverse drug events from clinical trials of pain treatments
Université Sorbonne Paris Nord（索邦巴黎西岱大学）；Sorbonne Université（索邦大学）；INSERM（法国国家健康与医学研究院）；Laboratoire de Recherche en Informatique, CNRS/Université Paris-Sud/Université Paris-Saclay（计算机研究实验室，法国国家科学研究中心/巴黎南大学/巴黎萨克雷大学）
Lived Experience Not Found: LLMs Struggle to Align with Experts on Addressing Adverse Drug Reactions from Psychiatric Medication Use
College of Computing, Georgia Institute of Technology（佐治亚理工学院计算学院）；Hospital General Universitario Gregorio Marañón（格雷戈里奥·马拉尼翁大学综合医院）；Hofstra University（霍夫斯特拉大学）；Columbia University（哥伦比亚大学）
Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events
KU Leuven（鲁汶大学）；CRACS INESC-TEC and FCUP Universidade do Porto（CRACS INESC-TEC与波尔图大学理学院）；Marshfield Clinic（马什菲尔德诊所）；University of Wisconsin - Madison（威斯康星大学麦迪逊分校）
Evaluating the Effectiveness of Artificial Intelligence in Predicting Adverse Drug Reactions among Cancer Patients: A Systematic Review and Meta-Analysis
National Polytechnic School of Oran - Maurice Audin（奥兰国家理工学院 - 莫里斯·奥丹）
Extensive Evaluation of Transformer-based Architectures for Adverse Drug Events Extraction
University of Udine（乌迪内大学）；University of Naples Federico II（那不勒斯费德里科二世大学）；The Hong Kong Polytechnic University（香港理工大学）；Bayer Pharmaceuticals（拜耳制药）；Bloomberg（彭博）；Bayer（拜耳）
Utilizing AI and Social Media Analytics to Discover Adverse Side Effects of GLP-1 Receptor Agonists
The School of Business Administration（商学院）；Bar-Ilan University（巴伊兰大学）；Ben-Gurion University of the Negev（内盖夫本-古里安大学）；Questrom School of Business（奎斯特罗姆商学院）；Boston University（波士顿大学）
Decision Support System for Chronic Diseases Based on Drug-Drug Interactions
The Chinese University of Hong Kong（香港中文大学）；Tencent AI Lab（腾讯人工智能实验室）；Hunan Normal University（湖南师范大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Exploring Drug Safety Through Knowledge Graphs: Protein Kinase Inhibitors as a Case Study
David Jackson Institute of Informatics University of Amsterdam（大卫·杰克逊信息学研究所 阿姆斯特丹大学）；Michael Gertz Institute of Computer Science Heidelberg University（迈克尔·格茨计算机科学研究所海德堡大学）；Jürgen Hesser Data Analysis and Modeling in Medicine Mannheim Institute for Intelligent Systems in Medicine (MIISM) Medical Faculty Mannheim, Heidelberg University（朱尔根·赫瑟医学数据分析与建模 马尔姆研究所（MIISM）海德堡大学医学系）
Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations
Data Science Institute, School of Mathematics, Shandong University（数据科学研究院、数学学院、山东大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（数学与系统科学研究院、中国科学院）；Data Science Institute, Shandong University（数据科学研究院、山东大学）
ImageDDI: Image-enhanced Molecular Motif Sequence Representation for Drug-Drug Interaction Prediction
College of Computer science and Electronic Engineering, Hunan University（计算机科学与电子工程学院，湖南大学）；Department of Integrative Biotechnology, Yonsei University（整合生物技术系，延世大学）
Investigating ADR mechanisms with knowledge graph mining and explainable AI
Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；LORIA（洛林计算机科学及其应用实验室）；Orange Labs（奥兰治实验室）；LIMICS（LIMICS实验室）；Inserm（法国国家健康与医学研究院）；Université Sorbonne Paris Nord（索邦巴黎北大学）；Sorbonne Université（索邦大学）；CHU de St Etienne（圣艾蒂安大学附属医院）；NGERE（NGERE实验室）；Centre Régional de Pharmacovigilance（区域药物警戒中心）；CHRU de Nancy（南锡大学区域医院中心）；Inria Paris（法国国家信息与自动化研究所巴黎中心）；Université de Paris（巴黎大学）
