Adverse drug events (ADE) are prevalent and costly. Clinical trials are constrained in their ability to identify potential ADEs, motivating the development of spontaneous reporting systems for post-market surveillance. Statistical methods provide a convenient way to detect signals from these reports but have limitations in leveraging relationships between drugs and ADEs given their discrete count-based nature. A previously proposed method, aer2vec, generates distributed vector representations of ADE report entities that capture patterns of similarity but cannot utilize lexical knowledge. We address this limitation by retrofitting aer2vec drug embeddings to knowledge from RxNorm and developing a novel retrofitting variant using vector rescaling to preserve magnitude. When evaluated in the context of a pharmacovigilance signal detection task, aer2vec with retrofitting consistently outperforms disproportionality metrics when trained on minimally preprocessed data. Retrofitting with rescaling results in further improvements in the larger and more challenging of two pharmacovigilance reference sets used for evaluation.
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）
Pharmacovigilance is the field of science devoted to the collection, analysis and prevention of Adverse Drug Reactions (ADRs). Efficient strategies for the extraction of information about ADRs from free text resources are essential to support the work of experts, employed in the crucial task of detecting and classifying unexpected pathologies possibly related to drug assumptions. Narrative ADR descriptions may be collected in several way, e.g. by monitoring social networks or through the so called spontaneous reporting, the main method pharmacovigilance adopts in order to identify ADRs. The en…
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）
Adverse reactions caused by drugs following their release into the market are among the leading causes of death in many countries. The rapid growth of electronically available health related information, and the ability to process large volumes of them automatically, using natural language processing (NLP) and machine learning algorithms, have opened new opportunities for pharmacovigilance. Survey found that more than 70% of US Internet users consult the Internet when they require medical information. In recent years, research in this area has addressed for Adverse Drug Reaction (ADR) pharmaco…
University of Warwick（华威大学）；Northeastern University（东北大学）；AstraZeneca（阿斯利康）；King’s College London（伦敦国王学院）；The Alan Turing Institute（阿兰·图灵研究所）
The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health. Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of spontaneous reports from health professionals, physicians, and pharmacists, and information voluntarily submitted by patients. In this scenario, facilitating analysis of such reports via automation has the potential to rapidly identify safety signals. Unfortunately, public resource…
Social media has grown to be a crucial information source for pharmacovigilance studies where an increasing number of people post adverse reactions to medical drugs that are previously unreported. Aiming to effectively monitor various aspects of Adverse Drug Reactions (ADRs) from diversely expressed social medical posts, we propose a multi-task neural network framework that learns several tasks associated with ADR monitoring with different levels of supervisions collectively. Besides being able to correctly classify ADR posts and accurately extract ADR mentions from online posts, the proposed …
This paper explores whether the use of drug reviews and social media could be leveraged as potential alternative sources for pharmacovigilance of adverse drug reactions (ADRs). We examined the performance of BERT alongside two variants that are trained on biomedical papers, BioBERT7, and clinical notes, Clinical BERT8. A variety of 8 different BERT models were fine-tuned and compared across three different tasks in order to evaluate their relative performance to one another in the ADR tasks. The tasks include sentiment classification of drug reviews, presence of ADR in twitter postings, and na…
Université Claude Bernard - Lyon 1（里昂第一大学（克洛德·贝尔纳大学））；New York University Abu Dhabi（纽约大学阿布扎比分校）
Social media can be an important source of information facilitating the detection of new safety signals in pharmacovigilance. Various approaches have investigated the analysis of social media data using AI such as NLP techniques for detecting adverse drug events. Existing approaches have focused on the extraction and identification of Adverse Drug Reactions, Drug-Drug Interactions and drug misuse. However, non of the works tackled the detection of potential safety signals by taking into account the evolution in time of relevant indicators. Moreover, despite the success of deep learning in vari…
The Need for Guardrails with Large Language Models in Medical Safety-Critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystem
Harvard-MIT Department of Health Sciences and Technology（哈佛-麻省理工卫生科学与技术部）；GlaxoSmithKline（葛兰素史克公司）；London School of Hygiene and Tropical Medicine（伦敦卫生与热带医学院）；Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）
University of Udine（乌迪内大学）；CSAIL MIT（麻省理工学院计算机科学与人工智能实验室）；The Hong Kong Polytechnic University（香港理工大学）
In recent years, Internet users are reporting Adverse Drug Events (ADE) on social media, blogs and health forums. Because of the large volume of reports, pharmacovigilance is seeking to resort to NLP to monitor these outlets. We propose for the first time the use of the SpanBERT architecture for the task of ADE extraction: this new version of the popular BERT transformer showed improved capabilities with multi-token text spans. We validate our hypothesis with experiments on two datasets (SMM4H and CADEC) with different text typologies (tweets and blog posts), finding that SpanBERT combined wit…
University of Udine（乌迪内大学）；Università degli Studi di Napoli 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（拜耳）
In the last decade, an increasing number of users have started reporting Adverse Drug Events (ADE) on social media platforms, blogs, and health forums. Given the large volume of reports, pharmacovigilance has focused on ways to use Natural Language Processing (NLP) techniques to rapidly examine these large collections of text, detecting mentions of drug-related adverse reactions to trigger medical investigations. However, despite the growing interest in the task and the advances in NLP, the robustness of these models in face of linguistic phenomena such as negations and speculations is an open…
The automation of adverse drug reaction (ADR) detection in social media would revolutionize the practice of pharmacovigilance, supporting drug regulators, the pharmaceutical industry and the general public in ensuring the safety of the drugs prescribed in daily practice. Following from the published proceedings of the Social Media Mining for Health (SMM4H) Applications Workshop & Shared Task in August 2019, we aimed to develop a deep learning model to classify ADRs within Twitter tweets that contain drug mentions. Our approach involved fine-tuning $BERT_{LARGE}$ and two domain-specific BERT im…
Development of a European Union Time-Indexed Reference Dataset for Assessing the Performance of Signal Detection Methods in Pharmacovigilance using a Large Language Model
Department of Drug Design and Pharmacology, University of Copenhagen（哥本哈根大学药物设计与药理学系）；GSK（葛兰素史克）
The collection of narrative spontaneous reports is an irreplaceable source for the prompt detection of suspected adverse drug reactions (ADRs): qualified domain experts manually revise a huge amount of narrative descriptions and then encode texts according to MedDRA standard terminology. The manual annotation of narrative documents with medical terminology is a subtle and expensive task, since the number of reports is growing up day-by-day. MagiCoder, a Natural Language Processing algorithm, is proposed for the automatic encoding of free-text descriptions into MedDRA terms. MagiCoder procedure…
Monitoring the biomedical literature for cases of Adverse Drug Reactions (ADRs) is a critically important and time consuming task in pharmacovigilance. The development of computer assisted approaches to aid this process in different forms has been the subject of many recent works. One particular area that has shown promise is the use of Deep Neural Networks, in particular, Convolutional Neural Networks (CNNs), for the detection of ADR relevant sentences. Using token-level convolutions and general purpose word embeddings, this architecture has shown good performance relative to more traditional…
Social media is an useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from twitter. Medical information extraction from social media is challenging, mainly due to short and highly information nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction relies on supervised learning methods, which suffers from …
Social media is becoming an increasingly important source of information to complement traditional pharmacovigilance methods. In order to identify signals of potential adverse drug reactions, it is necessary to first identify medical concepts in the social media text. Most of the existing studies use dictionary-based methods which are not evaluated independently from the overall signal detection task. We compare different approaches to automatically identify and normalise medical concepts in consumer reviews in medical forums. Specifically, we implement several dictionary-based methods popular…
School of Computer Science, The University of Manchester（曼彻斯特大学计算机学院）；Health eResearch Center, The University of Manchester（曼彻斯特大学健康电子研究中心）；The Alan Turing Institute（艾伦·图灵研究所）
Monitoring the administration of drugs and adverse drug reactions are key parts of pharmacovigilance. In this paper, we explore the extraction of drug mentions and drug-related information (reason for taking a drug, route, frequency, dosage, strength, form, duration, and adverse events) from hospital discharge summaries through deep learning that relies on various representations for clinical named entity recognition. This work was officially part of the 2018 n2c2 shared task, and we use the data supplied as part of the task. We developed two deep learning architecture based on recurrent neura…
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）
Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources
McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦克威廉姆斯生物医学信息学院）；Pratt School of Engineering, Duke University（杜克大学普拉特工程学院）；Mayo Clinic（梅奥诊所）；School of Medicine, Yale University（耶鲁大学医学院）
