dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_end
sequence: int64
- name: answer_start
sequence: int64
- name: text
sequence: string
splits:
- name: train
num_bytes: 309800906
num_examples: 130956
- name: validation
num_bytes: 77783988
num_examples: 32739
download_size: 54898541
dataset_size: 387584894
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
task_categories:
- question-answering
language:
- en
tags:
- medical
pretty_name: emrQA-msquad
Dataset Card for emrQA-msquad
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Dataset Details
Dataset Description
Machine Reading Comprehension (MRC) holds a pivotal role in shaping Medical Question Answering Systems (QAS) and transforming the landscape of accessing and applying medical information. However, the inherent challenges in the medical field, such as complex terminology and question ambiguity, necessitate innovative solutions. One key solution involves integrating specialized medical datasets and creating dedicated datasets. This strategic approach enhances the accuracy of QAS, contributing to advancements in clinical decision-making and medical research. To address the intricacies of medical terminology, a specialized dataset was integrated, exemplified by a novel Span extraction dataset derived from emrQA but restructured into 163,695 questions and 4,136 manually obtained answers, this new dataset was called emrQA-msquad dataset. Additionally, for ambiguous questions, a dedicated medical dataset for the Span extraction task was introduced, reinforcing the system's robustness. The fine-tuning of models such as BERT, RoBERTa, and Tiny RoBERTa for medical contexts significantly improved response accuracy within the F1-score range of 0.75 to 1.00 from 10.1% to 37.4%, 18.7% to 44.7% and 16.0% to 46.8%, respectively.
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Citation
BibTeX:
@misc{eladio2024emrqamsquad,
title={emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information},
author={Jimenez Eladio and Hao Wu},
year={2024},
eprint={2404.12050},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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