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Dataset collections of natural language processing for biomedicine/health domain

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BioMedical-NLP-Dataset

生物医学自然语言处理相关的数据集

Dataset collections of natural language processing for biomedicine/health domain

挑战榜单

Benchmark evaluation in NLP for Biomedicine or Health domain refers to the process of evaluating the performance of NLP models or algorithms on a standardized set of tasks and datasets that are specific to the biomedical or health domain. In this context, a benchmark is a set of tasks or datasets that are widely accepted by the research community as representative of the challenges faced in the biomedical or health domain. These benchmarks typically consist of tasks such as named entity recognition, relation extraction, text classification, and others that are relevant to the domain.

  • CBLUE

    中文医疗信息处理挑战榜CBLUE(Chinese Biomedical Language Understanding Evaluation)

    paper github website

  • BLURB

    BLURB is the Biomedical Language Understanding and Reasoning Benchmark. A collection of resources for biomedical natural language processing.

    paper website

信息抽取

Information extraction (IE) in Natural Language Processing (NLP) for the Biomedicine or Health domain refers to the process of automatically extracting structured information from unstructured or semi-structured biomedical or health-related texts such as electronic health records (EHRs), clinical trial reports, scientific publications, and social media posts. The goal of IE is to identify and extract specific pieces of information such as named entities (e.g., diseases, drugs, genes, proteins), relationships between them (e.g., drug-disease associations, gene-protein interactions), and events (e.g., adverse drug reactions, disease diagnoses) mentioned in the text.

实体识别

Named Entity Recognition (NER) is a natural language processing (NLP) task that involves identifying and categorizing named entities in unstructured text. In the context of biomedicine or health domain, NER specifically refers to identifying and categorizing named entities such as diseases, symptoms, treatments, drugs, genes, proteins, and other biomedical concepts.

  • 2019

    • BioNLP-OST 2019 CRAFT-CA task: Concept Annotation Task

      Chemical Entities of Biological Interest (CHEBI), Cell Ontology (CL), Gene Ontology Biological Process (GO_BP), Gene Ontology Cellular Component (GO_CC), Gene Ontology Molecular Function (GO_MF), Molecular Process Ontology (MOP), NCBI Taxonomy (NCBITaxon), Protein Ontology (PR), Sequence Ontology (SO), Uberon (UBERON).

    • BioNLP-OST 2019 PharmaCoNER task

      Entity types: Normalizables, No_Normalizables, Proteinas, Unclear

    • BioNLP-OST 2019 AGAC task

      Task 1 is a traditional NER for 12 labels, which cultivate molecular phenomena related to gene mutation. Variation (Var), Molecular Physiological Activity (MPA), Interaction, Pathway, Cell Physiological Activity (CPA), Regulation (Reg), Positive Regulation (PosReg), Negative Regulation (NegReg); Disease, Gene, Protein, Enzyme.

      Task 2 is a relation extraction task, which capture the thematic roles between entities. ThemeOf, CauseOf.

      Task 3 is a prediction task for the novel link discovery, which extract triple information among gene, function change, and disease out of the corpus texts. Gene;Function change;disease.

    • BioNLP-OST 2019 Bacteria-Biotope Task

      the BB task is an information extraction task involving entity recognition, entity normalization, and relation extraction.

      4 entity types: Microorganism, Habitat, Geographical, Phenotype.

      2 relation types: Lives_in, Exhibits.

    • CCKS 2019 面向中文电子病历的命名实体识别

      子任务1：医疗命名实体识别。实体包括疾病和诊断，检查，检验，手术，药物，解剖部位。子任务2：医疗实体及属性抽取（跨院迁移）。

      data

  • 2018
  • 2017
  • 2015

    • BioCreative V Track 2-CHEMDNER-patents

      automatic extraction of chemical and biological data from medicinal chemistry patents.

      The CHEMDNER-patents corpora will consist of a training, development and test set, each comprising a total of 7000 manually annotated records.

      CEMP (chemical entity mention in patents, main task)

      CPD (chemical passage detection, text classification task)

      GPRO (gene and protein related object task)

      paper

  • 2012
  • 2009

    • n2c2 2009: Medication Extraction Challenge

      Medication extraction challenge aims to encourage development of natural language processing systems for the extraction of medication-related information from narrative patient records. Information to be targeted includes medications, dosages, modes of administration, frequency of administration, and the reason for administration.

      paper

  • 2006
  • 2004

术语标准化

Entity normalization (EN) in Natural Language Processing (NLP) for the Biomedicine or Health domain refers to the process of mapping a specific named entity (e.g., a disease, a drug, a gene) mentioned in a text to a unique identifier in a reference knowledge base or ontology. The goal of EN is to resolve ambiguity and ensure consistency in entity representations across different texts and knowledge resources.

  • 2019
  • 2017

    • BioCreative VI Track 1: Interactive Bio-ID Assignment (IAT-ID)

      Bioentity normalization task. For gene/gene products, the identifier types are Entrez and UniProtKB. Small chemicals are identified using ChEBI as the primary identifier. Subcellular structures are identified using the Gene Ontology Cellular Component (GO CC) identifier. Cell lines are identified using Cellosaurus as the primary identifier. Cell types are identified using the Cell Ontology identifier. Tissues and organs are identified using Uberon as the identifier. Finally, organisms are identified using NCBI Taxon.

  • 2009

    • BioCreative III: GN: Gene Normalization

      link gene or gene products mentioned in the literature to standard database identifiers. However, in this challenge, there are two significant characteristics that make it unique: 1. Instead of using abstracts, full-length articles are provided. 2. Instead of being species-specific, no species information is provided.

  • 2006
  • 2004

关系抽取

Relation Extraction (RE) is a natural language processing (NLP) task that involves identifying and extracting semantic relationships between entities in text. In the biomedical or health domain, RE refers to the process of automatically identifying and extracting the relationships between biomedical entities such as genes, proteins, diseases, drugs, and biological processes mentioned in scientific literature. The goal of RE is to extract useful information from unstructured text, such as research articles, clinical notes, and electronic health records, which can be used for a variety of applications, including drug discovery, personalized medicine, and clinical decision support.

  • 2018

    • n2c2 2018 — Track 2: Adverse Drug Events and Medication Extraction in EHRs

      This task aims to answer the question: “Can NLP systems automatically discover drug to adverse event (ADE) relations in clinical narratives?”. three subtasks: 1) Concepts: Identifying drug names, dosages, durations and other entities. 2) Relations: Identifying relations of drugs with adverse drugs events (ADEs)[1] and other entities given gold standard entities. 3) End-to-end: Identifying relations of drugs with ADEs and other entities on system predicted entities.

      paper

  • 2017
  • 2013
  • 2012

    • n2c2 2012: Temporal Relations Challenge

      The 2012 i2b2 temporal relations challenge data include 310 discharge summaries consisting of 178 000 tokens. Clinically relevant events include clinical concepts, clinical departments, evidentials, occurrences. Temporal relations: BEFORE, AFTER, SIMULTANEOUS, OVERLAP, BEGUN_BY, ENDED_BY, DURING, BEFORE_OVERLAP.

      paper

  • 2011
    • BioNLP Shared Task 2011: Entity Relations Supporting Task (REL)

      The task concerns the detection of relations stated to hold between a gene or gene product and a related entity such as a protein domain or protein complex.

      Entities: human-annotated gene and gene product entities, annotated as "Protein"

      Relation Type: Subunit-Complex, Protein-Component

  • 2010

    • BioCreative III: PPI: Protein-Protein Interactions

      The aim of this task is to promote the development of automated systems that are able to extract biologically relevant information directly from the literature, in this case related to protein-protein interaction (PPI) annotation information.

    • n2c2 2010: Relations Challenge

      1. extraction of medical problems, tests, and treatments. 2) classification of assertions made on medical problems, present, absent, or possible. 3) relations of medical problems, tests, and treatments.

      A total of 394 training reports, 477 test reports, and 877 unannotated reports were de-identified and released to challenge participants with data use agreements.

      paper

  • 2006
  • 2004

事件抽取

Event Extraction (EE) is a natural language processing (NLP) task that involves identifying and extracting the occurrence of specific events or processes in text. In the biomedical or health domain, EE refers to the process of automatically identifying and extracting events or processes related to biomedical entities such as genes, proteins, diseases, drugs, and biological processes mentioned in scientific literature. The goal of EE is to automatically detect and extract information about specific biomedical events or processes mentioned in text, such as the activation of a gene, the inhibition of a protein, or the progression of a disease. This information can be used for a variety of applications, including drug discovery, personalized medicine, and clinical decision support.

  • 2019

    • BioNLP-OST 2019 Seedev Task

      the SeeDev representation scheme defines 16 entity types. task1: Binary relation extraction task. task2: Full event extraction task, these entities participates in 21 types of events that can be grouped into five categories.

  • 2016
  • 2013

    • BioNLP-ST 2013: Cancer Genetics (CG) Task

      The CG task aims to advance the automatic extraction of information from statements on the biological processes relating to the development and progression of cancer.

    • BioNLP-ST-2013: Pathway Curation (PC) task

      The PC task aims to evaluate the applicability of event extraction systems to support the curation, evaluation and maintenance of biomolecular pathway models and to encourage the further development of methods for these tasks.

    • BioNLP-ST-2013: Bacteria Biotopes (BB)

      Entity recognition of bacteria taxa and bacteria habitats. Bacteria habitat categorization through the OntoBiotope-Habitat ontology. Extraction of localization relations between bacteria and habitats.

  • 2011

    • BioNLP Shared Task 2011: GENIA Event Extraction (GENIA)

      The GENIA task aims at extracting events occurring upon genes or gene products, which are typed as "Protein" without differentiating genes from gene products. Other types of physical entities, e.g. cells, cell components, are not differentiated from each other, and their type is given as "Entity"

    • BioNLP Shared Task 2011: Epigenetics and Post-translational Modifications Task (EPI)

      This task focuses on events relating to epigenetic change, including DNA methylation and histone modification, as well as other common post-translational protein modifications.

      Event type: Hydroxylation(羟基化), Phosphorylation(磷酸化), Ubiquitination(泛素化), DNA methylation(DNA甲基化), Glycosylation(糖基化), Acetylation(乙酰化), Methylation(甲基化), Catalysis(催化).

    • BioNLP Shared Task 2011: Infectious Diseases Task (ID)

      This tasks focuses on the biomolecular mechanisms of infectious diseases.

      Five entities: Genes and gene products, Two-component systems, Chemicals, Organisms, Regulons/Operons.

      Nine events: Gene expression, Transcription, Protein catabolism, Phosphorylation, Localization, Binding, Regulation, Positive regulation, Negative regulation, Process.

    • BioNLP Shared Task 2011: Bacteria Biotopes (BB)

      The task consists in extracting bacteria localization events, in other words, mentions of given species and the place where it lives.

      Entities: Host, HostPart, Geographical, Environment, Food, Medical, Soil, Water.

      Events: Localization, PartOf.

    • BioNLP Shared Task 2011: Bacteria Gene Interactions (BI)

      This task consists in a full extraction of genetic processes mentioned in scientific texts concerning the bacterium Bacillus subtilis.

      Entities: GeneProduct, Protein, PolymeraseComplex, Gene, ProteinFamily, GeneFamily, GeneComplex, Regulon, Site, Promoter, Action, Transcription, Expression.

      Events: RegulonDependence, BindTo, TranscriptionFrom, RegulonMember, SiteOf, TranscriptionBy, PromoterOf, PromoterDependence, ActionTarget, Interaction.

    • BioNLP Shared Task 2011: Bacteria Gene Renaming (RENAME)

      The task consists in extracting gene renaming acts and gene synonymy reminders in scientific texts about bacteria.

      Entities: All gene and protein names have been annotated as text-bound entities of type Gene.

      Events: The only type of event is Renaming where both arguments are of type Gene.

  • 2004

共指消解

Coreference resolution is a natural language processing (NLP) task that involves identifying all the expressions (words, phrases, or pronouns) in a text that refer to the same entity. In the biomedical or health domain, coreference resolution is used to identify all the mentions of medical concepts or entities, such as diseases, treatments, drugs, or anatomical parts, that refer to the same thing.

文本分类

Text classification in NLP for the biomedicine or health domain refers to the process of automatically categorizing or labeling text data based on their content with the aim of extracting meaningful information and insights. In this domain, text classification is commonly used to classify various types of medical documents, such as clinical notes, discharge summaries, pathology reports, and research articles, into predefined categories, such as disease diagnosis, treatment options, medication information, and patient outcomes.

  • 2019
    • CHIP 2019 评测三：临床试验筛选标准短文本分类

      临床试验是指通过人体志愿者也称为受试者进行的科学研究，筛选标准是临床试验负责人拟定的鉴定受试者是否满足某项临床试验的主要指标，分为入组标准和排出标准，一般为无规则的自由文本语句。

      此评测任务的主要目标是针对临床试验筛选标准进行分类，所有预料均来自于真实临床试验，并经过了初步处理和人工标注。给定事先定义好的44种筛选标准类别和一系列中文临床试验筛选标准的描述句子，参赛者需返回每一条筛选标准的具体类别。

      训练集：22962；验证集：7682；测试集：7697。

      解决方案：第一名，第二名，第三名

      paper

  • 2008

    • n2c2 2008: Obesity Challenge

      The obesity challenge is a multi-class, multi-label classification task focused on obesity and its co-morbidities. The data for the challenge consist of discharge summaries from Partners Healthcare. All records have been fully de-identified. Obesity information and co-morbidities have been marked at a document level as present, absent, questionable, or unmentioned in the documents.

      paper

  • 2006

    • n2c2 2006: Deidentification and Smoking Challenge

      Study the two challenge questions on the same data. Task 2: identification of the smoking status of patients. Classify patient records into five possible smoking status categories: Past Smoker, Current Smoker, Smoker, Non-Smoker, Unknown.

      paper

文本相似度

Text similarity in NLP for the biomedicine or health domain refers to the process of quantifying the degree of semantic or syntactic similarity between two or more pieces of text data. In this domain, text similarity is commonly used to compare different medical documents, such as clinical notes, research articles, and patient records, to identify relevant information, track disease progression, and monitor treatment outcomes. Text similarity in biomedicine or health domain typically involves the use of various NLP techniques such as word embeddings, sentence embeddings, and document embeddings. These techniques transform text data into numerical representations that can be compared using various similarity metrics, such as cosine similarity, Jaccard similarity, and Euclidean distance.

  • 2019
    • CHIP 2019 评测二：平安医疗科技疾病问答迁移学习比赛

      本次评测任务的主要目标是针对中文的疾病问答数据，进行病种间的迁移学习。具体而言，给定来自5个不同病种的问句对，要求判定两个句子语义是否相同或者相近。所有语料来自互联网上患者真实的问题，并经过了筛选和人工的意图匹配标注。病种包括：diabetes，hypertension，hepatitis，aids，breast cancer。

      训练集，数据量分别为：10000，2500，2500，2500，2500。验证集，数据量分别为：2000，2000，2000，2000，2000。测试集，数据量为50000

      解决方案：第一名，第二名，第三名

  • 2018

文档检索

Document retrieval in NLP for the biomedicine or health domain refers to the process of retrieving relevant medical documents or articles from large collections of text data based on user queries or information needs. In this domain, document retrieval is commonly used to search for specific information related to diseases, treatments, medications, and patient outcomes. Document retrieval in biomedicine or health domain typically involves the use of information retrieval (IR) techniques such as keyword-based search, Boolean search, and vector space model. These techniques index the text data and match the query terms with the most relevant documents based on their similarity scores.

  • 2019

    • BioNLP-OST 2019 RDoc Task

      task1 (RDoC-IR) is on retrieving PubMed Abstracts related to RDoC constructs. 250 abstracts for train and 200 abstracts for test. task 2 (RDoC-SE) is on extracting the most relevant sentences for an RDoC construct from a relevant abstract. 250 abstracts for train and 50 abstracts for test.

  • 2018

    • n2c2 2018 — Track 1: Cohort Selection for Clinical Trials

      This task aims to answer the question, “Can NLP systems use narrative medical records to identify which patients meet selection criteria for clinical trials?” The task requires NLP systems to compare each patient to a list of selection criteria, and determine if the patients meet, do not meet, or possibly meet each criterion.

      paper

  • 2010

问答系统

Question answering in NLP for the biomedicine or health domain refers to the process of automatically answering natural language questions related to medical information or knowledge. In this domain, question answering is commonly used to support clinical decision-making, patient care, and medical research.

知识图谱

A knowledge graph in NLP for biomedicine or health domain refers to a structured representation of medical knowledge and information in the form of a graph. It represents entities and their relationships in the medical domain, such as diseases, symptoms, treatments, medications, and clinical trials, as nodes and edges in a graph. The nodes represent the entities, and the edges represent the relationships between them. Knowledge graphs in biomedicine or health domain typically involve the extraction and integration of information from various sources, such as medical literature, clinical trials, electronic health records, and medical ontologies. The information is then organized into a graph structure, which enables efficient navigation, querying, and reasoning over the medical knowledge. Knowledge graphs in biomedicine or health domain can be used for various tasks such as information retrieval, question answering, and decision support. They can also be used to support clinical research, drug discovery, and personalized medicine.

  • 2020

    • CCKS 2020 新冠知识图谱构建与问答

      四个子任务：1）新冠百科知识图谱类型推断， 2）新冠概念图谱的上下位关系预测，3）新冠科研抗病毒药物图谱的链接预测，4）新冠百科知识图谱问答评测。

预训练语言模型

Pre-trained language model in NLP for the biomedical or health domain is a model that has been trained on a large corpus of text data in this domain. The purpose of pre-training is to enable the model to learn the underlying patterns and relationships of language in the specific domain. Pre-training typically involves training the model on a large amount of text data and using techniques like unsupervised learning to learn the relationships between words and phrases. Once the pre-training phase is complete, the model can be fine-tuned on a specific task, such as text classification or named entity recognition, using a smaller, labeled dataset. Pre-trained language models have become increasingly popular in NLP because they enable researchers and practitioners to achieve state-of-the-art results on a wide range of tasks with minimal data and computational resources. In the biomedical and health domain, pre-trained language models have been used to extract information from medical records, analyze scientific literature, and develop predictive models for disease diagnosis and treatment.

  • BioBERT: a pre-trained biomedical language representation model for biomedical text mining

    paper github

  • BERTCNER: Chinese clinical named entity recognition (CNER) using pre-trained BERT model

    paper github

  • BlueBERT: pre-trained on PubMed abstracts and clinical notes (MIMIC-III)

    paper github

  • ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission

    paper github

  • LinkBERT: Pretraining Language Models with Document Links

    paper github

  • SciBERT: A Pretrained Language Model for Scientific Text

    paper github

  • PubMedBERT: Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

    paper website

大语言模型

Large language models have the potential to revolutionize the field of biomedical health in a number of ways. These models have been trained on vast amounts of data related to various aspects of health and medicine, and can assist with tasks such as medical diagnosis, drug discovery, clinical decision-making, patient engagement, medical research, education, natural language processing, clinical trials, and public health. Overall, large language models have the potential to improve patient outcomes by providing more accurate diagnoses, more effective treatments, and more personalized care. As the field of biomedical health continues to evolve, we can expect to see more innovative applications of these powerful tools.

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Dataset collections of natural language processing for biomedicine/health domain