README.md

metadata

dataset_info:
  features:
    - name: Question
      dtype: string
    - name: Focus (Drug)
      dtype: string
    - name: Question Type
      dtype: string
    - name: Answer
      dtype: string
    - name: Section Title
      dtype: string
    - name: URL
      dtype: string
  splits:
    - name: train
      num_bytes: 403030
      num_examples: 690
  download_size: 0
  dataset_size: 403030

Dataset Card for "medicationqa"

MedicationQA: https://github.com/abachaa/Medication_QA_MedInfo2019

The gold standard corpus for medication question answering introduced in the MedInfo 2019 paper "Bridging the Gap between Consumers’ Medication Questions and Trusted Answers": http://ebooks.iospress.nl/publication/51941

If you use this dataset, please cite the following paper: Bridging the Gap between Consumers’ Medication Questions and Trusted Answers. Asma Ben Abacha, Yassine Mrabet, Mark Sharp, Travis Goodwin, Sonya E. Shooshan and Dina Demner-Fushman. MEDINFO 2019.

@inproceedings{BenAbacha:MEDINFO19, author = {Asma {Ben Abacha} and Yassine Mrabet and Mark Sharp and Travis Goodwin and Sonya E. Shooshan and Dina Demner{-}Fushman},
title = {Bridging the Gap between Consumers’ Medication Questions and Trusted Answers}, booktitle = {MEDINFO 2019},
year = {2019}, abstract = {This paper addresses the task of answering consumer health questions about medications. To better understand the challenge and needs in terms of methods and resources, we first introduce a gold standard corpus for Medication Question Answering created using real consumer questions. The gold standard consists of six hundred and seventy-four question-answer pairs with annotations of the question focus and type and the answer source. We first present the manual annotation and answering process. In the second part of this paper, we test the performance of recurrent and convolutional neural networks in question type identification and focus recognition. Finally, we discuss the research insights from both the dataset creation process and our experiments. This study provides new resources and experiments on answering consumers’ medication questions and discusses the limitations and directions for future research efforts.}}