---
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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This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).

## 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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### Source Data

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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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