Off-the-shelf biomedical embeddings obtained from the recently released various pre-trained language models (such as BERT, XLNET) have demonstrated state-of-the-art results (in terms of accuracy) for the various natural language understanding tasks (NLU) in the biomedical domain. Relation Classification (RC) falls into one of the most critical tasks. In this paper, we explore how to incorporate domain knowledge of the biomedical entities (such as drug, disease, genes), obtained from Knowledge Graph (KG) Embeddings, for predicting Drug-Drug Interaction from textual corpus. We propose a new method, BERTKG-DDI, to combine drug embeddings obtained from its interaction with other biomedical entities along with domain-specific BioBERT embedding-based RC architecture. Experiments conducted on the DDIExtraction 2013 corpus clearly indicate that this strategy improves other baselines architectures by 4.1% macro F1-score.
Peking University（北京大学）；IER Business Development Center（IER业务发展中心）
Ontology can be used for the interpretation of natural language. To construct an anti-infective drug ontology, one needs to design and deploy a methodological step to carry out the entity discovery and linking. Medical synonym resources have been an important part of medical natural language processing (NLP). However, there are problems such as low precision and low recall rate. In this study, an NLP approach is adopted to generate candidate entities. Open ontology is analyzed to extract semantic relations. Six-word vector features and word-level features are selected to perform the entity lin…
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…
Minimizing adverse reactions caused by drug-drug interactions has always been a momentous research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discover various drug-drug interactions. However, these discoveries contain a huge amount of noise and provide knowledge bases far from complete and trustworthy ones to be utilized. Most existing studies focus on predicting binary drug-drug interactions between drug pairs but ignore other int…
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…
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）
Predicting drug-gene relations via analogy tasks with word embeddings
Kyoto University（京都大学）；Recruit Co., Ltd.（Recruit公司）；Institute for Protein Research（蛋白质研究所）；Osaka University（大阪大学）；Research Institute of Molecular Pathology（分子病理研究所）；RIKEN（理化学研究所）
Medical entity retrieval is an integral component for understanding and communicating information across various health systems. Current approaches tend to work well on specific medical domains but generalize poorly to unseen sub-specialties. This is of increasing concern under a public health crisis as new medical conditions and drug treatments come to light frequently. Zero-shot retrieval is challenging due to the high degree of ambiguity and variability in medical corpora, making it difficult to build an accurate similarity measure between mentions and concepts. Medical knowledge graphs (KG…
Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient's symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogenous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-II…
Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；RWTH Aachen University（亚琛工业大学）；University of Dhaka（达卡大学）
Interference between pharmacological substances can cause serious medical injuries. Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases but can also result in a reduction of drug development cost. Presently, most drug-related knowledge is the result of clinical evaluations and post-marketing surveillance; resulting in a limited amount of information. Existing data-driven prediction approaches for DDIs typically rely on a single source of information, while using information from multiple sources would help improve predictions. Machine learning (ML) tech…
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）
Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations
Interdisciplinary Program in Bioinformatics, Seoul National University（首尔国立大学生物信息学跨学科项目）；AIGENDRUG Co., Ltd.（爱真药物有限公司）；BK21 FOUR Intelligence Computing, Seoul National University（首尔国立大学BK21四号智能计算）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔国立大学人工智能跨学科项目）；Department of Artificial Intelligence, Inha University（仁荷大学人工智能系）
DiffER: Diffusion Entity-Relation Modeling for Reversal Curse in Diffusion Large Language Models
Chongqing University（重庆大学）；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Hong Kong University of Science and Technology（香港理工大学）
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…
Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）
Most existing methods for biomedical entity recognition task rely on explicit feature engineering where many features either are specific to a particular task or depends on output of other existing NLP tools. Neural architectures have been shown across various domains that efforts for explicit feature design can be reduced. In this work we propose an unified framework using bi-directional long short term memory network (BLSTM) for named entity recognition (NER) tasks in biomedical and clinical domains. Three important characteristics of the framework are as follows - (1) model learns contextua…
Concept normalization in free-form texts is a crucial step in every text-mining pipeline. Neural architectures based on Bidirectional Encoder Representations from Transformers (BERT) have achieved state-of-the-art results in the biomedical domain. In the context of drug discovery and development, clinical trials are necessary to establish the efficacy and safety of drugs. We investigate the effectiveness of transferring concept normalization from the general biomedical domain to the clinical trials domain in a zero-shot setting with an absence of labeled data. We propose a simple and effective…
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…
Learning to Describe for Predicting Zero-shot Drug-Drug Interactions
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Paradigm Inc.（第四范式公司）；Hong Kong University of Science and Technology (GZ)（香港科技大学（广州））；Peng Cheng Laboratory（鹏城实验室）
Knowledge bases store information about the semantic types of entities, which can be utilized in a range of information access tasks. This information, however, is often incomplete, due to new entities emerging on a daily basis. We address the task of automatically assigning types to entities in a knowledge base from a type taxonomy. Specifically, we present two neural network architectures, which take short entity descriptions and, optionally, information about related entities as input. Using the DBpedia knowledge base for experimental evaluation, we demonstrate that these simple architectur…
Universitas Indonesia（印度尼西亚大学）；Universitas Mercu Buana（默库布阿纳大学）
One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text is special and has unique characteristics. In addition, the medical text mining poses more challenges, e.g., more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug. The mining is even more challenging due to the lack of labeled dataset sources and external knowledge, as well as multiple token representations for a single drug name that is more common in the real application s…
We focus on improving the effectiveness of a Virtual Assistant (VA) in recognizing emerging entities in spoken queries. We introduce a method that uses historical user interactions to forecast which entities will gain in popularity and become trending, and it subsequently integrates the predictions within the Automated Speech Recognition (ASR) component of the VA. Experiments show that our proposed approach results in a 20% relative reduction in errors on emerging entity name utterances without degrading the overall recognition quality of the system.
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…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
This paper presents a new task of predicting the coverage of a text document for relation extraction (RE): does the document contain many relational tuples for a given entity? Coverage predictions are useful in selecting the best documents for knowledge base construction with large input corpora. To study this problem, we present a dataset of 31,366 diverse documents for 520 entities. We analyze the correlation of document coverage with features like length, entity mention frequency, Alexa rank, language complexity and information retrieval scores. Each of these features has only moderate pred…
Korea University（高丽大学）；AIRS Company, Hyundai Motor Group（现代汽车集团AIRS公司）；AIGEN Sciences（AIGEN科学公司）
In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e.g. diseases and drugs) from the ever-growing biomedical literature. In this article, we present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by employing a multi-task NER model and neural network-based NEN models to achieve much faster and more accurate inference. We hope that our tool can help annotate large-scale biomedical…
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…
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Clinical Cohort Studies (CCS), such as randomized clinical trials, are a great source of documented clinical research. Ideally, a clinical expert inspects these articles for exploratory analysis ranging from drug discovery for evaluating the efficacy of existing drugs in tackling emerging diseases to the first test of newly developed drugs. However, more than 100 articles are published daily on a single prevalent disease like COVID-19 in PubMed. As a result, it can take days for a physician to find articles and extract relevant information. Can we develop a system to sift through the long list…
We present SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data. Our approach views biomedical resources like lexicons as function primitives for autogenerating weak supervision. We then use a generative model to unify and denoise this supervision and construct large-scale, probabilistically labeled datasets for training high-accuracy NER taggers. In three biomedical NER tasks, SwellShark achieves competitive scores with state-of-the-art supervised benchmarks using no hand-labeled training data. In a drug name extraction t…
Institut Polytechnique de Paris（巴黎综合理工学院）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）
Biomedical entity linking aims to map biomedical mentions, such as diseases and drugs, to standard entities in a given knowledge base. The specific challenge in this context is that the same biomedical entity can have a wide range of names, including synonyms, morphological variations, and names with different word orderings. Recently, BERT-based methods have advanced the state-of-the-art by allowing for rich representations of word sequences. However, they often have hundreds of millions of parameters and require heavy computing resources, which limits their applications in resource-limited s…
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…
L3S Research Center, Leibniz University of Hannover（莱布尼茨汉诺威大学L3S研究中心）；GESIS - Leibniz Institute for the Social Sciences（GESIS——莱布尼茨社会科学研究所）
Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real applications. A priori approximations of the difficulty to link a particular entity mention can facilitate flagging of critical cases as part of semi-au…
Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension
School of Cyber Science and Engineering, Qufu Normal University（曲阜师范大学网络空间安全学院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；Network and Information Center, Qufu Normal University（曲阜师范大学网络与信息中心）；School of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）电子与信息工程学院）
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…
Motivation: The proliferation of Biomedical research articles has made the task of information retrieval more important than ever. Scientists and Researchers are having difficulty in finding articles that contain information relevant to them. Proper extraction of biomedical entities like Disease, Drug/chem, Species, Gene/protein, can considerably improve the filtering of articles resulting in better extraction of relevant information. Performance on BioNer benchmarks has progressively improved because of progression in transformers-based models like BERT, XLNet, OpenAI, GPT2, etc. These models…
In Track-1 of the BioCreative VII Challenge participants are asked to identify interactions between drugs/chemicals and proteins. In-context named entity annotations for each drug/chemical and protein are provided and one of fourteen different interactions must be automatically predicted. For this relation extraction task, we attempt both a BERT-based sentence classification approach, and a more novel text-to-text approach using a T5 model. We find that larger BERT-based models perform better in general, with our BioMegatron-based model achieving the highest scores across all metrics, achievin…
University of Kentucky（肯塔基大学）；National Library of Medicine（美国国家医学图书馆）
Preventable adverse events as a result of medical errors present a growing concern in the healthcare system. As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable form is an important step toward effective dissemination of drug safety information. In this study, we tackle the problem of jointly extracting drugs and their interactions, including interaction outcome, from drug labels. Our deep learning approach entails composing various intermediate representations including sequence and graph based context…
University of Chicago（芝加哥大学）；Argonne National Laboratory（阿贡国家实验室）；Globus
Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of coronavirus research. We report here on a project that leverages both human and artificial intelligence to detect references to drug-like molecules in free text. We engage non-expert humans to create a corpus of labeled text, use this labeled corpus to train a named enti…
Logical reasoning over Knowledge Graphs (KGs) is a fundamental technique that can provide efficient querying mechanism over large and incomplete databases. Current approaches employ spatial geometries such as boxes to learn query representations that encompass the answer entities and model the logical operations of projection and intersection. However, their geometry is restrictive and leads to non-smooth strict boundaries, which further results in ambiguous answer entities. Furthermore, previous works propose transformation tricks to handle unions which results in non-closure and, thus, canno…
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…
