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license: mit
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About Dataset
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This dataset was scrapped from the MedQuAD repository and then converted it to a csv file. This file contains multiple categories being: 

1. Cancer
2. Senior Helth
3. Growth Hormones and Receptors
4. Heart, Lungs and Blood
5. Genetic and Rare Diseases
6. Disease Control and Prevention
7. Neurological Disorders and Stroke
8. Diabetes and Rare Diseases
9. Other

The above dataset belongs to https://github.com/abachaa/MedQuAD.git, so please refer to them for more information. I have attached their README.md file for ease of data literacy. 

This dataset was splitted 80% for the train and 20% for the test for each topic separetely and then merged to one file. 

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MedQuAD: Medical Question Answering Dataset
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MedQuAD includes 47,457 medical question-answer pairs created from 12 NIH websites (e.g. cancer.gov, niddk.nih.gov, GARD, MedlinePlus Health Topics). The collection covers 37 question types (e.g. Treatment, Diagnosis, Side Effects) associated with diseases, drugs and other medical entities such as tests.

They included additional annotations in the XML files, that could be used for diverse IR and NLP tasks, such as the question type, the question focus, its syonyms, its UMLS Concept Unique Identifier (CUI) and Semantic Type.
They added the category of the question focus (Disease, Drug or Other) in the 4 MedlinePlus collections. All other collections are about diseases.

The paper cited below describes the collection, the construction method as well as its use and evaluation within a medical question answering system.

N.B. They removed the answers from 3 subsets to respect the MedlinePlus copyright (https://medlineplus.gov/copyright.html):
(1) A.D.A.M. Medical Encyclopedia, (2) MedlinePlus Drug information, and (3) MedlinePlus Herbal medicine and supplement information.
-- They kept all the other information including the URLs in case you want to crawl the answers. Please contact them if you have any questions.

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QA Test Collection
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They used the test questions of the TREC-2017 LiveQA medical task: https://github.com/abachaa/LiveQA_MedicalTask_TREC2017/tree/master/TestDataset.

As described in their BMC paper, they have manually judged the answers retrieved by the IR and QA systems from the MedQuAD collection.
They used the same judgment scores as the LiveQA Track: 1-Incorrect, 2-Related, 3-Incomplete, and 4-Excellent.
-- Format of the qrels file: Question_ID judgment Answer_ID

The QA test collection contains 2,479 judged answers that can be used to evaluate the performance of IR & QA systems on the LiveQA-Med test questions: https://github.com/abachaa/MedQuAD/blob/master/QA-TestSet-LiveQA-Med-Qrels-2479-Answers.zip

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Reference
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If you use the MedQuAD dataset and/or the collection of 2,479 judged answers, please cite the following paper: "A Question-Entailment Approach to Question Answering". Asma Ben Abacha and Dina Demner-Fushman. BMC Bioinformatics, 2019.

@ARTICLE{BenAbacha-BMC-2019,    
      author    = {Asma {Ben Abacha} and Dina Demner{-}Fushman},
      title     = {A Question-Entailment Approach to Question Answering},
      journal = {{BMC} Bioinform.}, 
      volume    = {20},
        number    = {1},
           pages     = {511:1--511:23},
        year      = {2019},
  url       = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3119-4}
       }     

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License
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The MedQuAD dataset is published under a Creative Commons Attribution 4.0 International Licence (CC BY). https://creativecommons.org/licenses/by/4.0/