Automated Drug Combination Extraction (DCE) from large-scale biomedical literature is crucial for advancing precision medicine and pharmacological research. However, existing relation extraction methods primarily focus on binary interactions and struggle to model variable-length n-ary drug combinations, where complex compatibility logic and distributed evidence need to be considered. To address these limitations, we propose RexDrug, an end-to-end reasoning-enhanced relation extraction framework for n-ary drug combination extraction based on large language models. RexDrug adopts a two-stage training strategy. First, a multi-agent collaborative mechanism is utilized to automatically generate high-quality expert-like reasoning traces for supervised fine-tuning. Second, reinforcement learning with a multi-dimensional reward function specifically tailored for DCE is applied to further refine reasoning quality and extraction accuracy. Extensive experiments on the DrugComb dataset show that RexDrug consistently outperforms state-of-the-art baselines for n-ary extraction. Additional evaluation on the DDI13 corpus confirms its generalizability to binary drugdrug interaction tasks. Human expert assessment and automatic reasoning metrics further indicates that RexDrug produces coherent medical reasoning while accurately identifying complex therapeutic regimens. These results establish RexDrug as a scalable and reliable solution for complex biomedical relation extraction from unstructured text. The source code and data are available at https://github.com/DUTIR-BioNLP/RexDrug
Curation and Extraction of Drug-Related Entities from Reddit Platform
Population Health Sciences, Weill Cornell Medicine, New York City, USA（威立·科恩医学中心人口健康科学系，纽约市，美国）；School of Computing and Information Systems, University of Melbourne, Melbourne, Australia（墨尔本大学计算机与信息系统学院，墨尔本，澳大利亚）；Emergency Medicine, Weill Cornell Medicine, New York City, USA（急诊医学，威立·科恩医学中心，纽约市，美国）
City University of Hong Kong（香港城市大学）；Hong Kong Institute for Data Science, City University of Hong Kong（香港城市大学香港数据科学研究院）；Jilin University（吉林大学）
The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the latest and novel knowledge in an efficient manner. To address such a problem, we propose EGFI for extracting and consolidating drug interactions from large-scale medical literature text data. Specifically, EGFI consists of two parts: classification and generation. In the classification part, EGFI encompasses the language model BioBERT which has been comprehensi…
This paper presents our approach for task 2 and task 3 of Social Media Mining for Health (SMM4H) 2020 shared tasks. In task 2, we have to differentiate adverse drug reaction (ADR) tweets from nonADR tweets and is treated as binary classification. Task3 involves extracting ADR mentions and then mapping them to MedDRA codes. Extracting ADR mentions is treated as sequence labeling and normalizing ADR mentions is treated as multi-class classification. Our system is based on pre-trained language model RoBERTa and it achieves a) F1-score of 58% in task2 which is 12% more than the average score b) re…
Traditional biomedical version of embeddings obtained from pre-trained language models have recently shown state-of-the-art results for relation extraction (RE) tasks in the medical domain. In this paper, we explore how to incorporate domain knowledge, available in the form of molecular structure of drugs, for predicting Drug-Drug Interaction from textual corpus. We propose a method, BERTChem-DDI, to efficiently combine drug embeddings obtained from the rich chemical structure of drugs along with off-the-shelf domain-specific BioBERT embedding-based RE architecture. Experiments conducted on th…
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）
CACER: Clinical Concept Annotations for Cancer Events and Relations
University of Washington（华盛顿大学）；George Mason University（乔治梅森大学）；Huntsman Cancer Institute（亨茨曼癌症研究所）；University of Utah（犹他大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）
Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models
Department of Computer Science（计算机科学系）；School of Nursing（护理学院）；Department of Information Sciences and Technology（信息科学与技术系）；Department of Biomedical Informatics & Medical Education（生物医学信息学与医学教育系）
The development of deep neural networks has improved representation learning in various domains, including textual, graph structural, and relational triple representations. This development opened the door to new relation extraction beyond the traditional text-oriented relation extraction. However, research on the effectiveness of considering multiple heterogeneous domain information simultaneously is still under exploration, and if a model can take an advantage of integrating heterogeneous information, it is expected to exhibit a significant contribution to many problems in the world. This th…
We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. We encode textual drug pairs with convolutional neural networks and their molecular pairs with graph convolutional networks (GCNs), and then we concatenate the outputs of these two networks. In the experiments, we show that GCNs can predict DDIs from the molecular structures of drugs in high accuracy and the molecular information can enhance text-based DDI extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set.
Zhejiang University（浙江大学）；Hikvision（海康威视）；University of Southern California（南加州大学）
When patients need to take medicine, particularly taking more than one kind of drug simultaneously, they should be alarmed that there possibly exists drug-drug interaction. Interaction between drugs may have a negative impact on patients or even cause death. Generally, drugs that conflict with a specific drug (or label drug) are usually described in its drug label or package insert. Since more and more new drug products come into the market, it is difficult to collect such information by manual. We take part in the Drug-Drug Interaction (DDI) Extraction from Drug Labels challenge of Text Analy…
Drug-drug interaction (DDI) is a vital information when physicians and pharmacists intend to co-administer two or more drugs. Thus, several DDI databases are constructed to avoid mistakenly combined use. In recent years, automatically extracting DDIs from biomedical text has drawn researchers' attention. However, the existing work utilize either complex feature engineering or NLP tools, both of which are insufficient for sentence comprehension. Inspired by the deep learning approaches in natural language processing, we propose a recur- rent neural network model with multiple attention layers f…
Adverse reaction caused by drugs is a potentially dangerous problem which may lead to mortality and morbidity in patients. Adverse Drug Event (ADE) extraction is a significant problem in biomedical research. We model ADE extraction as a Question-Answering problem and take inspiration from Machine Reading Comprehension (MRC) literature, to design our model. Our objective in designing such a model, is to exploit the local linguistic context in clinical text and enable intra-sequence interaction, in order to jointly learn to classify drug and disease entities, and to extract adverse reactions cau…
Combination drug therapies are treatment regimens that involve two or more drugs, administered more commonly for patients with cancer, HIV, malaria, or tuberculosis. Currently there are over 350K articles in PubMed that use the "combination drug therapy" MeSH heading with at least 10K articles published per year over the past two decades. Extracting combination therapies from scientific literature inherently constitutes an $n$-ary relation extraction problem. Unlike in the general $n$-ary setting where $n$ is fixed (e.g., drug-gene-mutation relations where $n=3$), extracting combination therap…
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）
Simultaneous administration of multiple drugs can have synergistic or antagonistic effects as one drug can affect activities of other drugs. Synergistic effects lead to improved therapeutic outcomes, whereas, antagonistic effects can be life-threatening, may lead to increased healthcare cost, or may even cause death. Thus identification of unknown drug-drug interaction (DDI) is an important concern for efficient and effective healthcare. Although multiple resources for DDI exist, they are often unable to keep pace with rich amount of information available in fast growing biomedical texts. Most…
University of Pisa（比萨大学）；Institute of Informatics and Telematics (IIT-CNR)（信息与通信技术研究所（IIT-CNR））；LUCENSE SCaRL（LUCENSE 有限责任公司）
New Psychoactive Substances (NPS) are drugs that lay in a grey area of legislation, since they are not internationally and officially banned, possibly leading to their not prosecutable trade. The exacerbation of the phenomenon is that NPS can be easily sold and bought online. Here, we consider large corpora of textual posts, published on online forums specialized on drug discussions, plus a small set of known substances and associated effects, which we call seeds. We propose a semi-supervised approach to knowledge extraction, applied to the detection of drugs (comprising NPS) and effects from …
Adverse drug events (ADEs) are unexpected incidents caused by the administration of a drug or medication. To identify and extract these events, we require information about not just the drug itself but attributes describing the drug (e.g., strength, dosage), the reason why the drug was initially prescribed, and any adverse reaction to the drug. This paper explores the relationship between a drug and its associated attributes using relation extraction techniques. We explore three approaches: a rule-based approach, a deep learning-based approach, and a contextualized language model-based approac…
To minimize the accelerating amount of time invested in the biomedical literature search, numerous approaches for automated knowledge extraction have been proposed. Relation extraction is one such task where semantic relations between the entities are identified from the free text. In the biomedical domain, extraction of regulatory pathways, metabolic processes, adverse drug reaction or disease models necessitates knowledge from the individual relations, for example, physical or regulatory interactions between genes, proteins, drugs, chemical, disease or phenotype. In this paper, we study the …
University of Kentucky（肯塔基大学）；Lister Hill National Center for Biomedical Communications, National Library of Medicine（国家医学图书馆利斯特·希尔国家生物医学传播中心）
Preventable adverse drug reactions as a result of medical errors present a growing concern in modern medicine. As drug-drug interactions (DDIs) may cause adverse reactions, being able to extracting DDIs from drug labels into machine-readable form is an important effort in effectively deploying drug safety information. The DDI track of TAC 2018 introduces two large hand-annotated test sets for the task of extracting DDIs from structured product labels with linkage to standard terminologies. Herein, we describe our approach to tackling tasks one and two of the DDI track, which corresponds to nam…
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…
University of Udine（乌迪内大学）；The Hong Kong Polytechnic University（香港理工大学）；DSIG - Bayer Pharmaceuticals（拜耳制药DSIG）
Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations. However, despite the recent advances in NLP, it is currently unknown if such models are robust in face of negation, which is pervasive across language varieties. In this paper we evaluate three state-of-the-art systems, showing their fragility against negation, and then we introduce two possible strategies to increase the robustness of these models: a pipeline approach, relying on a specific component fo…
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…
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…
Allen Institute for AI（艾伦人工智能研究所）；Carnegie Mellon University（卡内基梅隆大学）；Technion – Israel Institute of Technology（以色列理工学院）；Bar Ilan University（巴伊兰大学）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）
Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available in a situation. To assist medical professionals in identifying beneficial drug-combinations, we construct an expert-annotated dataset for extracting information about the efficacy of drug combinations from the scientific literature. Beyond its practical utility, the dataset also presents a unique NLP challenge, as the first relation extract…
International Institute of Information Technology-Hyderabad（海得拉巴国际信息技术研究所）；Microsoft（微软）；TCS Research（塔塔咨询服务研究中心）
Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care. Current ADR surveillance systems are often associated with a substantial time lag before such events are officially published. On the other hand, online social media such as Twitter contain information about ADR events in real-time, much before any official reporting. Current state-of-the-art in ADR mention extraction uses Recurrent Neural Networks (RNN), which typically need large labeled corpora. Towards this end, we propose a multi-task learning based method which can utilize a similar auxiliary task (a…
International Institute of Information Technology-Hyderabad（海得拉巴国际信息技术研究所）；TCS Research（塔塔咨询服务研究中心）
Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care. Current ADR surveillance systems are often associated with a substantial time lag before such events are officially published. On the other hand, online social media such as Twitter contain information about ADR events in real-time, much before any official reporting. Current state-of-the-art methods in ADR mention extraction use Recurrent Neural Networks (RNN), which typically need large labeled corpora. Towards this end, we propose a semi-supervised method based on co-training which can exploit a large p…
CHU Necker, AP-HP（巴黎公共医疗救助机构内克尔大学医院）；INSERM UMR-S 872（法国国家健康与医学研究院 UMR-S 872）；Sorbonne Paris Cité, Faculté de Médecine（索邦巴黎西岱大学医学院）；Université de Paris（巴黎大学）
In this study, we evaluated the RNNG, a neural top-down transition based parser, for medication information extraction in clinical texts. We evaluated this model on a French clinical corpus. The task was to extract the name of a drug (or a drug class), as well as attributes informing its administration: frequency, dosage, duration, condition and route of administration. We compared the RNNG model that jointly identifies entities, events and their relations with separate BiLSTMs models for entities, events and relations as baselines. We call seq-BiLSTMs the baseline models for relations extract…
In this research, we present our work participation for the DrugProt task of BioCreative VII challenge. Drug-target interactions (DTIs) are critical for drug discovery and repurposing, which are often manually extracted from the experimental articles. There are >32M biomedical articles on PubMed and manually extracting DTIs from such a huge knowledge base is challenging. To solve this issue, we provide a solution for Track 1, which aims to extract 10 types of interactions between drug and protein entities. We applied an Ensemble Classifier model that combines BioMed-RoBERTa, a state of art lan…
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 …
Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the best feature representations. In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforemen…
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 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…
Merged ChemProt-DrugProt for Relation Extraction from Biomedical Literature
San Diego Supercomputer Center, University of California San Diego 1；Department of Computer Science \& Engineering, University of California San Diego 2；Department of Computer Science, Virginia Commonwealth University 3
Current approaches for clinical information extraction are inefficient in terms of computational costs and memory consumption, hindering their application to process large-scale electronic health records (EHRs). We propose an efficient end-to-end model, the Joint-NER-RE-Fourier (JNRF), to jointly learn the tasks of named entity recognition and relation extraction for documents of variable length. The architecture uses positional encoding and unitary batch sizes to process variable length documents and uses a weight-shared Fourier network layer for low-complexity token mixing. Finally, we reach…
ReLyfe - Medical Intelligence（ReLyfe 医疗智能公司）；Ecole Polytechnique（巴黎综合理工学院）；BeCareLink（BeCareLink 公司）
Structuring medical data in France remains a challenge mainly because of the lack of medical data due to privacy concerns and the lack of methods and approaches on processing the French language. One of these challenges is structuring drug-related information in French clinical documents. To our knowledge, over the last decade, there are less than five relevant papers that study French prescriptions. This paper proposes a new approach for extracting drug-related information from French clinical scanned documents while preserving patients' privacy. In addition, we deployed our method in a healt…
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；Dalian University of Technology（大连理工大学）
The biomedical literature provides a rich source of knowledge such as protein-protein interactions (PPIs), drug-drug interactions (DDIs) and chemical-protein interactions (CPIs). Biomedical relation extraction aims to automatically extract biomedical relations from biomedical text for various biomedical research. State-of-the-art methods for biomedical relation extraction are primarily based on supervised machine learning and therefore depend on (sufficient) labeled data. However, creating large sets of training data is prohibitively expensive and labor-intensive, especially so in biomedicine …
IBM Research（IBM研究院）；University of Melbourne（墨尔本大学）；Cures Within Reach for Cancer（癌症可及疗法组织）；Massachusetts Institute of Technology（麻省理工学院）；Harvard Medical School（哈佛医学院）
More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of generic drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs worldwide. Evidence on the efficacy of non-cancer generic drugs being tested for cancer exists in scientific publications, but trying to manually identify and extract such evidence is intractable. In this paper, we introduce a system…
Automated entity relation extraction (RE) from literature provides an important source for constructing biomedical database, which is more efficient and extensible than manual curation. However, existing RE models usually ignore the information contained in sentence structures and target entities. In this paper, we propose BERE, a deep learning based model which uses Gumbel Tree-GRU to learn sentence structures and joint embedding to incorporate entity information. It also employs word-level attention for improved relation extraction and sentence-level attention to suit the distantly supervise…
Most information extraction methods focus on binary relations expressed within single sentences. In high-value domains, however, $n$-ary relations are of great demand (e.g., drug-gene-mutation interactions in precision oncology). Such relations often involve entity mentions that are far apart in the document, yet existing work on cross-sentence relation extraction is generally confined to small text spans (e.g., three consecutive sentences), which severely limits recall. In this paper, we propose a novel multiscale neural architecture for document-level $n$-ary relation extraction. Our system …
Université Paris-Saclay, INRAE, MaIAGE（巴黎-萨克雷大学，法国国家农业食品与环境研究院，应用数学与计算机科学、基因组学与环境研究单位）；Université Paris-Saclay, CNRS, Laboratoire interdisciplinaire des sciences du numérique（巴黎-萨克雷大学，法国国家科学研究中心，数字科学跨学科实验室）
Recently many studies have been conducted on the topic of relation extraction. The DrugProt track at BioCreative VII provides a manually-annotated corpus for the purpose of the development and evaluation of relation extraction systems, in which interactions between chemicals and genes are studied. We describe the ensemble system that we used for our submission, which combines predictions of fine-tuned bioBERT, sciBERT and const-bioBERT models by majority voting. We specifically tested the contribution of syntactic information to relation extraction with BERT. We observed that adding constituen…
UniD$^3$: A Knowledge Graph-Enhanced RAG Framework for Drug-Disease Discovery and Reasoning
Department of Health Outcomes and Biomedical Informatics, University of Florida（佛罗里达大学健康成果与生物医学信息学系）；Department of Hematology, H. Lee Moffitt Cancer Center and Research Institute（血液科，H. Lee Moffitt癌症中心与研究院）；Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute（生物信息学与计算生物学中心，休斯顿方法主义研究学院）；Department of Cardiothoracic Surgery, Weill Cornell Medicine, Cornell University（心胸外科，Weill Cornell医学，康奈尔大学）；Department of Biostatistics and Health Data Science, Indiana University School of Medicine（生物统计学与健康数据科学系，印第安纳大学医学院）
The Joint Entity-Relation Extraction Model Based on Span and Interactive Fusion Representation for Chinese Medical Texts with Complex Semantics
School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；Cooperative Innovation Center of Internet Healthcare, Zhengzhou University（郑州大学互联网医疗协同创新中心）；Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家心血管病中心阜外医院）；Department of Geriatrics, Beijing Friendship Hospital, Capital Medical University（首都医科大学附属北京友谊医院老年医学科）
School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；School of Foreign Languages, Dalian University of Technology（大连理工大学外国语学院）；School of Mathematical Sciences, Dalian University of Technology（大连理工大学数学科学学院）
Background: Automatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development. Meanwhile, biomedical experts have built many highly-structured knowledge bases (KBs), which contain prior knowledge about chemicals and diseases. Prior knowledge provides strong support for CDR extraction. How to make full use of it is worth studying. Results: This paper proposes a novel model called "Knowledge-guided Convolutional Networks (KCN)" to leverage prior knowledge for CDR extraction. The proposed model first learns knowle…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Adobe Research（Adobe研究院）
Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any external knowledge during inference. Due to the exponential growth of biomedical publications, models that do not go beyond their fixed set of parameters will likely fall behind. Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowledge for joint entity and relation extraction named KECI (Knowledge-Enhanced Collective Inference). …
