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 https://huggingface.co/datasets/Eladio/emrqa-msquad.

翻译：机器阅读理解（MRC）在构建医学问答系统（QAS）以及改变医学信息获取与应用方式中起着关键作用。然而，医学领域固有的挑战，如复杂的术语和问题歧义性，需要创新的解决方案。其中一个关键方案涉及整合专业医学数据集并创建专用数据集。这一策略性方法提升了QAS的准确性，有助于临床决策和医学研究的进步。为应对医学术语的复杂性，我们整合了一个专业数据集，具体表现为从emrQA派生但重新构建的跨度抽取数据集，包含163,695个问题及4,136个手动获取的答案，这一新数据集被命名为emrQA-msquad数据集。此外，针对歧义性问题，我们引入了一个用于跨度抽取任务的专用医学数据集，增强了系统的鲁棒性。针对医学语境微调BERT、RoBERTa和Tiny RoBERTa等模型后，响应准确性在F1分数0.75至1.00范围内分别从10.1%提升至37.4%、从18.7%提升至44.7%以及从16.0%提升至46.8%。最后，emrQA-msquad数据集已在https://huggingface.co/datasets/Eladio/emrqa-msquad 公开提供。