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. Finally, emrQA-msquad dataset is publicy available at
this https URL

机器阅读理解在医疗问答系统中发挥着重要作用，本研究通过整合专门的医学数据集和创建专用数据集的方法，提高了问答系统的准确性，为临床决策和医学研究的进展做出贡献。通过对 BERT、RoBERTa 和 Tiny RoBERTa 等模型进行微调，显著提高了对医学内容的响应准确性。