在Github上搜索整理了一波關(guān)于醫(yī)療NLP的數(shù)據(jù)集：

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中文評測數(shù)據(jù)集

1. Yidu-S4K：醫(yī)渡云結(jié)構(gòu)化4K數(shù)據(jù)集

2.瑞金醫(yī)院糖尿病數(shù)據(jù)集

3.Yidu-N7K：醫(yī)渡云標(biāo)準(zhǔn)化7K數(shù)據(jù)集

4.中文醫(yī)學(xué)問答數(shù)據(jù)集

5.平安醫(yī)療科技疾病問答遷移學(xué)習(xí)比賽

6.天池新冠肺炎問句匹配比賽

7.中文醫(yī)患問答對話數(shù)據(jù)

8.中文醫(yī)學(xué)問答數(shù)據(jù)

9.CHIP2020各項評測已開放

10.醫(yī)學(xué)數(shù)據(jù)挖掘與算法評測大賽

中文醫(yī)學(xué)知識圖譜

CMeKG

英文數(shù)據(jù)集

PubMedQA: A Dataset for Biomedical Research Question Answering

中文醫(yī)療領(lǐng)域語料

醫(yī)學(xué)教材 培訓(xùn)考試

哈工大《大詞林》開放75萬核心實體詞及相關(guān)概念、關(guān)系列表（包含中藥/醫(yī)院/生物 類別）

醫(yī)學(xué)embedding

開源英文醫(yī)學(xué)embedding

鏈接：https://github.com/lrs1353281004/Chinese_medical_NLP

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醫(yī)療行業(yè)專業(yè)詞匯語料

鏈接：https://github.com/xtea/chinese_medical_words

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中文醫(yī)學(xué)NLP公開資源整理：術(shù)語集/語料庫/詞向量/預(yù)訓(xùn)練模型/知識圖譜/命名實體識別/QA/信息抽取/etc

術(shù)語集/語料庫

medical-news中文醫(yī)學(xué)新聞爬蟲

medical-books中文LaTex開源醫(yī)學(xué)書籍

THUOCL清華大學(xué)thunlp組醫(yī)學(xué)詞匯

ICD-10-CNICD-10中文對應(yīng)

OMAHA七巧板醫(yī)學(xué)術(shù)語集樣例數(shù)據(jù)

中文糖尿病標(biāo)注數(shù)據(jù)集包含實體標(biāo)注和關(guān)系標(biāo)注

詞向量/預(yù)訓(xùn)練模型

ChineseEHRBert 中文電子病歷預(yù)訓(xùn)練Bert；用Bert測試命名實體識別，問答模型，關(guān)系提取任務(wù)

分詞

PKUSEGPKUSEG分詞工具，模型支持選擇醫(yī)學(xué)

知識圖譜 / 關(guān)系提取

cMeKGChinese Medical Knowledge Graph

瑞金醫(yī)院人工智能輔助構(gòu)建知識圖譜大賽糖尿病相關(guān)的學(xué)術(shù)論文以及糖尿病臨床指南的實體標(biāo)注和抽取實體關(guān)系任務(wù)

OMAHA知識圖譜（藥品適應(yīng)癥）開放醫(yī)療與健康聯(lián)盟（Open Medical and Healthcare Alliance，OMAHA）構(gòu)建的藥品與藥品適應(yīng)證的知識圖譜數(shù)據(jù)

醫(yī)療知識圖譜數(shù)據(jù)醫(yī)療知識圖譜數(shù)據(jù)（ownthink）

病人事件圖譜數(shù)據(jù)集病人事件圖譜是一種新的基于RDF的醫(yī)療觀察性數(shù)據(jù)表示模型，可以清晰地表示臨床檢查、診斷、治療等多種事件類型以及事件的時序關(guān)系。使用三家上海三甲醫(yī)院的電子病歷數(shù)據(jù)，構(gòu)建了包括3個專科、173395個醫(yī)療事件、501335個事件時序關(guān)系以及與5313個知識庫概念鏈接的醫(yī)療數(shù)據(jù)集。

中文癥狀庫這是一個包含癥狀實體和癥狀相關(guān)三元組的數(shù)據(jù)集。中文癥狀庫的數(shù)據(jù)來自8個主流的健康咨詢網(wǎng)站、3個中文百科網(wǎng)站和電子病歷。它還包含了中文癥狀與UMLS中概念的鏈接結(jié)果。

中醫(yī)醫(yī)案知識圖譜從醫(yī)案中抽取臨床知識構(gòu)建知識圖譜，幫助用戶了解中醫(yī)特色療法，以及疾病（如“慢性胃炎”）的臨床表現(xiàn)、相關(guān)療法、相關(guān)養(yǎng)生保健方法等

herbnet 面向中藥研究，根據(jù)中藥領(lǐng)域模型的特點，構(gòu)建了一個包括中醫(yī)疾病，方劑，中藥， 中藥化學(xué)成分，藥理作用，中藥實驗，化學(xué)實驗方法在內(nèi)的中藥本體。進(jìn)而，基于本體實現(xiàn)了一系列數(shù)據(jù)庫的集成，從而構(gòu)建了一個中藥知識圖譜。

CHIP2020中文醫(yī)學(xué)文本實體關(guān)系抽取

命名實體識別

CCKS2017面向中文電子病歷的醫(yī)療實體識別及屬性抽取數(shù)據(jù)集

CCKS2018面向中文電子病歷的醫(yī)療實體識別及屬性抽取數(shù)據(jù)集

CCKS2019數(shù)據(jù)下載面向中文電子病歷的醫(yī)療實體識別及屬性抽取數(shù)據(jù)集

CHIP2020中文醫(yī)學(xué)文本命名實體識別

CHIP2020中藥說明書實體識別

QA

CCIR2019CCIR 2019 基于電子病歷的數(shù)據(jù)查詢類問答

cMedQA中文醫(yī)學(xué)QA數(shù)據(jù)集

cMedQA2中文醫(yī)學(xué)QA數(shù)據(jù)集

CMID中文醫(yī)學(xué)QA意圖理解數(shù)據(jù)集

KGQA基于醫(yī)藥知識圖譜的智能問答系統(tǒng)

chatbot-base-on-Knowledge-Graph使用深度學(xué)習(xí)方法解析問題 知識圖譜存儲 查詢知識點 基于醫(yī)療垂直領(lǐng)域的對話系統(tǒng)

中文醫(yī)療對話數(shù)據(jù)集Chinese medical dialogue data 中文醫(yī)療對話數(shù)據(jù)集

webMedQAwebMedQA

MedDialogThe MedDialog dataset contains conversations (in Chinese) between doctors and patients. It has 1.1 million dialogues and 4 million utterances.

CHIP2020中醫(yī)文獻(xiàn)問題生成

術(shù)語標(biāo)準(zhǔn)化

CHIP2019臨床術(shù)語標(biāo)準(zhǔn)化任務(wù):醫(yī)渡云標(biāo)準(zhǔn)化7K數(shù)據(jù)集

CHIP2020臨床術(shù)語標(biāo)準(zhǔn)化任務(wù)

相似句對判斷

“公益AI之星”挑戰(zhàn)賽-新冠疫情相似句對判定大賽比賽整理近萬條真實語境下疫情相關(guān)的肺炎、支原體肺炎、支氣管炎、上呼吸道感染、肺結(jié)核、哮喘、胸膜炎、肺氣腫、感冒、咳血等患者提問句對，要求選手通過自然語言處理技術(shù)識別相似的患者問題。

其他

CHIP2018針對中文的真實患者健康咨詢語料，進(jìn)行問句意圖匹配

CHIP2019平安醫(yī)療科技疾病問答遷移學(xué)習(xí)比賽

鏈接：https://github.com/GanjinZero/awesome_Chinese_medical_NLP

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Data_數(shù)據(jù)中有6個文件夾分別是:

94596個問答對220606個問答對183751個問答對75553個問答對101602個問答對115991個問答對 總計 792099個問答對

每個文件夾下有一個csv文件，其中的數(shù)據(jù)格式為:

鏈接：https://github.com/Toyhom/Chinese-medical-dialogue-data

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This dataset is used for Chinese medical QA intent understanding task.

Dataset format:

All the data is stored in a JSON file. There are 5 fields in the file. An example as follows:

{ "originalText": "間質(zhì)性肺炎的癥狀?", "entities": [{"label_type": "疾病和診斷", "start_pos": 0, "end_pos": 5}], "seg_result": ["間質(zhì)性肺炎", "的", "癥狀", "?"], "label_4class": ["病癥"], "label_36class": ["臨床表現(xiàn)"] }

鏈接：https://github.com/liutongyang/CMID

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This is the dataset for Chinese community medical question answering. The dataset is in version 1.0 and is available for non-commercial research. We will update and expand the database from time to time. In order to protect the privacy, the data is anonymized and no personal information is included.

鏈接：https://github.com/zhangsheng93/cMedQA

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COVID19 Language Resources: Datasets

鏈接：https://github.com/lwgkzl/Covid19-NLP

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Datasets

BioCreative V chemical-disease relation (CDR) corpus (in short, BC5CDR corpus) (13, 14, 16, 34): It consists of 1,500 PubMed articles with 4,409 annotated chemicals, 5,818 diseases, and 3,116 chemical-disease interactions. The relation task data is publicly available through BioCreative V athttps://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus/.

Traditional Chinese medicine (TCM) literature corpus (in short, TCM corpus) (32): The abstracts of all 106,150 papers published in the 114 most popular Chinese TCM journals between 2011 to 2016 are collected. 3024 herbs, 4957 formulae, 1126 syndromes, and 1650 diseases are found. 5 types of relations are annotated. The entire dataset is available online athttp://arnetminer.org/TCMRelExtr.

The 2012 informatics for integrating biology and the bedside (i2b2) project temporal relations challenge corpus (in short, i2b2 temporal corpus) (29, 30): It contains 310 de-identified discharge summaries of more than 178,000 tokens, with annotations of clinically significant events, temporal expressions and temporal relations in clinical narratives. On average, each discharge summary in the corpus contains 86.6 events, 12.4 temporal expressions, and 176 raw temporal relations. In this corpus, 8 kinds of temporal relations between events and temporal expressions are defined: BEFORE, AFTER, SIMULTANEOUS, OVERLAP, BEGUN_BY, ENDED_BY, DURING, BEFORE_OVERLAP. The entire annotations are available athttp://i2b2.org/NLP/DataSets.

鏈接：https://github.com/chentao1999/MedicalRelationExtraction