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Biomedical NLP Corpus Collection

生物医学自然语言处理相关的数据集。

Biomedical NLP resources from papers and conferences.

挑战极限

  • CBLUE

    中文医疗信息处理挑战榜CBLUE(Chinese Biomedical Language Understanding Evaluation)是中国中文信息学会医疗健康与生物信息处理专业委员会在合法开放共享的理念下发起，由阿里云天池平台承办，并由医渡云（北京）技术有限公司、平安医疗科技、北京大学、郑州大学、鹏城实验室、哈尔滨工业大学(深圳）、同济大学、夸克、阿里巴巴达摩院等开展智慧医疗研究的单位共同协办，旨在推动中文医学NLP技术和社区的发展。 榜单在设计上综合考虑了任务类型和任务难度两个维度，目标是建设一个任务类型覆盖广、同时也要保证任务的难度的benchmark，因此榜单在吸收往届CHIP学术评测的同时也适当增加了业界数据集，业务数据集的特点是数据真实且有噪音，对模型的鲁棒性提出了更高的要求。一期榜单任务包括医学文本信息抽取（实体识别、关系抽取）、医学术语归一化、医学文本分类、医学句子关系判定和医学QA共5大类任务8个子任务。

    paper github website

  • BLURB

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

    paper website

  • TBD

    TBD

信息抽取

命名实体识别

  • 2004
  • 2006

  • 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

  • 2012
  • 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

  • 2017
  • 2018
  • 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）。

      数据下载链接

术语标准化

关系抽取

  • 2004
  • 2006
  • 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.

  • 2010

    • 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

  • 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

  • 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

  • 2013
  • 2017
  • 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

事件抽取

  • 2004
  • 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.

  • 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.

  • 2016
  • 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.

共指消解

文本分析

文本分类

  • 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

  • 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

  • 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

双句相似度分析

  • 2018
  • 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

文档检索

  • 2010
  • 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

  • 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.

问答系统

知识图谱

  • 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

  • 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

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Biomedical NLP Corpus or Datasets.

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nlp natural-language-processing text-mining bioinformatics dataset named-entity-recognition corpus-data medical-informatics

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