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）
Jesse Davis, Vitor Santos Costa, Peggy Peissig, Michael Caldwell, Elizabeth Berg, David Page
Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connections between related medications or diseases. One way to capture the latent structure is via a relational clustering of objects. We propose a novel approach that, instead of pre-clustering the objects, performs a demand-driven clustering during learning. We evaluate our algorithm on three real-world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication. We find that our approach is more accurate than performing no clustering, pre-clustering, and using expert-constructed medical heterarchies.
Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Goldman Sachs（高盛）；OPKO Diagnostics（OPKO诊断公司）
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）
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions
School of Electronics and Information Engineering, Xi’an Jiaotong University（西安交通大学电子与信息工程学院）；College of Geomatics, Xi’an University of Science and Technology（西安科技大学测绘学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
CrossADR: enhancing adverse drug reactions prediction for combination pharmacotherapy with cross-layer feature integration and cross-level associative learning
School of Mathematical Sciences（数学科学学院）；Shanghai Key Laboratory of PMMP（PMMP上海重点实验室）；East China Normal University（华东师范大学）；Shanghai（上海）；China（中国）
FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning
University of Southern California（南加利福尼亚大学）；National University of Singapore（新加坡国立大学）；University of Science and Technology of China（中国科学技术大学）；Westlake University（西湖大学）；Hunan University（湖南大学）
Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；Lochlann Quinn School of Business, University College Dublin（都柏林大学学院洛克兰·奎因商学院）；Faculty of Science, McGill University（麦吉尔大学理学院）
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）
Detect adverse drug reactions for drug Alendronate
Shandong Polytechnic University（山东理工大学）；University of Nottingham（诺丁汉大学）
药品不良反应（Adverse Drug Reaction，ADR）是一个备受关注的公共卫生问题。在这项研究中，我们提出了一个新的方法来检测的ADR使用特征矩阵和特征选择。实验是在药物辛伐他汀上进行的。检测药物的主要副作用，与其他计算机化方法相比，实现了更好的性能。检测的不良反应是基于计算机方法，需要进一步调查。
Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017
University of Manchester（曼彻斯特大学）；Manchester Institute of Innovation Research, Alliance Manchester Business School, The University of Manchester（曼彻斯特大学联盟曼彻斯特商学院曼彻斯特创新研究所）
Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database
Qilu University of Technology（齐鲁工业大学）；University of Nottingham（诺丁汉大学）
药品不良反应（Adverse Drug Reaction，ADR）是一个备受关注的公共卫生问题。药品不良反应是导致药品退出市场的最常见原因之一。处方事件监测是发现药品不良反应的重要手段。该方法的主要问题是如何从临床实践中收集的高通量医疗事件中自动提取医疗事件或副作用。在这项研究中，我们提出了一个新的概念的特征矩阵检测的ADR。从THIN数据库的大数据中提取特征矩阵，用于描述吸毒患者的医疗事件。特征矩阵为不规则、大规模的医学数据奠定了基础。然后对特征矩阵进行特征选择，检测出显著特征。最后，根据显著特征对不良反应进行定位。实验在三种药物上进行：阿托伐他汀、阿仑膦酸钠和甲氧氯普胺。每种药物的主要副作用被检测到，并且与其他计算机化方法相比，实现了更好的性能。检测到的不良反应是基于计算机方法，需要进一步调查。
Evaluating the Robustness of Adverse Drug Event Classification Models Using Templates
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Technische Universität Berlin（柏林工业大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）
Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction Classification
Kazan Federal University（喀山联邦大学）；Lomonosov Moscow State University（莫斯科罗蒙诺索夫国立大学）；Sber AI；National Research University Higher School of Economics（国立研究型大学高等经济学院）
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）
ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
Peking University（北京大学）；Yale University（耶鲁大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；University of Science and Technology of China（中国科学技术大学）
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
BIFOLD；TU Berlin（柏林工业大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Université Paris-Saclay（巴黎萨克雷大学）；Nara Institute of Science and Technology（奈良科学技术研究所）；RIKEN（理化学研究所）
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）
CASTER: Predicting Drug Interactions with Chemical Substructure Representation
Harvard T. H. Chan School of Public Health, Harvard University（哈佛大学陈曾熙公共卫生学院）；IQVIA（艾昆纬）；MIT-IBM Watson AI Lab, IBM Research（IBM研究院麻省理工学院-IBM沃森人工智能实验室）；Georgia Institute of Technology（佐治亚理工学院）
