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MedMCQA: Medical MCQ Dataset

Deep Learning & AI for Improving Healthcare

By Huggingface Hub [source]

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About this dataset

> MedMCQA is a comprehensive large-scale Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. It is expected to drive further research into deep learning and AI in order to enhance knowledge surrounding healthcare. This dataset provides an invaluable resource for healthcare professionals, as it contains detailed multiple choice questions and answers from relevant medical exams. The columns contain careful categorizations such as the question, choices (merely A, B, C or D), the correct answer option (COK), the type of choice made available (single or multiple answer) along with subject name and specific topic name which will help determine complexity of each question. Finally, this dataset comes with an explanation for why each given option is right/wrong which serves as a powerful diagnostic tool to moot out any errors in understanding/interpretation of complex areas of medicine by users

More Datasets

> For more datasets, click here.

Featured Notebooks

> - 🚨 Your notebook can be here! 🚨!

How to use the dataset

> ## How to Use the MedMCQA Dataset > The MedMCQA dataset includes a collection of multiple-choice questions and answers related to medical entrance exams. It can be used in many ways, including creating virtual medical exams, developing question-answering bots for medical use, and providing healthcare professionals with additional resources. Here’s how you can get started using the MedMCQA dataset: > > - Download the entire dataset from Kaggle: https://www.kaggle.com/medicalmcqa/medical-multiple-choice-question-answering-dataset > - Unzip it and save it on your computer or server in an easily accessible location > - Open the dataset folder which contains 4 CSV files various topics of MCQs > - train.csv : This file contains 80% training data for building models - Test .csv : This file contains 10% testing data to evaluate trained models - validation .csv: This file contaisn 10% validation data to evaluate trained model - evaluation .csv : This file is used for submitting final results in kaggle kernel competition > > - Review each column within each CSV one by one and observe what kind of features they provide (such as questions, answer choices, correct answer choice etc) > > Columns: Question, OPA , OPB , OPC , OPD , COP (Correct Option) , Choice Type (Single / Multiple), Exp(Explanation) -- Subject name & Topic name > > 5. Download template notebook from Kaggle Kernel page: https://www.kaggle kernel competition template notebooks 6 Locate downloaded Jupyter notebook 7 Start writing code along with required libraries 8 Create input parameters passing feature columns names that we reviewed in step 4 9 Build model & start training 10 Evaluate result & using kfold techniques experiment continues margins 11 Using evaluation dataset check final accuracy of model 12 Submit results on Kaggle public form 13 Repeat steps every day learn more about derivatives 14 Refine model architecture

Research Ideas

> - Developing Natural Language Processing (NLP) models that are capable of accurately responding to medical questions. > - Creating AI-driven virtual medical exam simulations for practice and assessment purposes. > - Incorporating MedMCQA dataset into Intelligent Tutoring Systems (ITSs) to help students prepare for medical entrance exams more effectively

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > Data Source > >

License

> > > License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication > No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: validation.csv

Column name	Description
question	The question being asked. (String)
opa	Option A of the multiple choice question. (String)
opb	Option B of the multiple choice question. (String)
opc	Option C of the multiple choice question. (String)
opd	Option D of the multiple choice question. (String)
cop	The correct option of the multiple choice question. (String)
choice_type	The type of multiple choice question (single or multiple answer). (String)
exp	Explanation of the question. (String)
subject_name	The subject the question is related to. (String)
topic_name	The topic the question is related to. (String)

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File: train.csv

Column name	Description
question	The question being asked. (String)
opa	Option A of the multiple choice question. (String)
opb	Option B of the multiple choice question. (String)
opc	Option C of the multiple choice question. (String)
opd	Option D of the multiple choice question. (String)
cop	The correct option of the multiple choice question. (String)
choice_type	The type of multiple choice question (single or multiple answer). (String)
exp	Explanation of the question. (String)
subject_name	The subject the question is related to. (String)
topic_name	The topic the question is related to. (String)

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File: test.csv

Column name	Description
question	The question being asked. (String)
opa	Option A of the multiple choice question. (String)
opb	Option B of the multiple choice question. (String)
opc	Option C of the multiple choice question. (String)
opd	Option D of the multiple choice question. (String)
cop	The correct option of the multiple choice question. (String)
choice_type	The type of multiple choice question (single or multiple answer). (String)
exp	Explanation of the question. (String)
subject_name	The subject the question is related to. (String)
topic_name	The topic the question is related to. (String)

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit Huggingface Hub.