Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Turing Lab, Alibaba Group, Hangzhou, China.
Bioinformatics. 2019 Oct 15;35(20):4129-4139. doi: 10.1093/bioinformatics/btz195.
With the abundant medical resources, especially literature available online, it is possible for people to understand their own health status and relevant problems autonomously. However, how to obtain the most appropriate answer from the increasingly large-scale database, remains a great challenge. Here, we present a biomedical question answering framework and implement a system, Health Assistant, to enable the search process.
In Health Assistant, a search engine is firstly designed to rank biomedical documents based on contents. Then various query processing and search techniques are utilized to find the relevant documents. Afterwards, the titles and abstracts of top-N documents are extracted to generate candidate snippets. Finally, our own designed query processing and retrieval approaches for short text are applied to locate the relevant snippets to answer the questions.
Our system is evaluated on the BioASQ benchmark datasets, and experimental results demonstrate the effectiveness and robustness of our system, compared to BioASQ participant systems and some state-of-the-art methods on both document retrieval and snippet retrieval tasks.
A demo of our system is available at https://github.com/jinzanxia/biomedical-QA.
随着丰富的医疗资源,特别是在线上的文献,人们有可能自主地了解自己的健康状况和相关问题。然而,如何从越来越大规模的数据库中获得最合适的答案,仍然是一个巨大的挑战。在这里,我们提出了一个生物医学问答框架,并实现了一个系统,Health Assistant,以实现搜索过程。
在 Health Assistant 中,首先设计了一个搜索引擎,根据内容对生物医学文献进行排名。然后利用各种查询处理和搜索技术来找到相关的文献。之后,提取前 N 篇文献的标题和摘要,生成候选片段。最后,我们自己设计的用于短文本的查询处理和检索方法用于定位相关片段来回答问题。
我们的系统在 BioASQ 基准数据集上进行了评估,实验结果表明,与 BioASQ 参与者系统以及在文档检索和片段检索任务上的一些最新方法相比,我们的系统具有有效性和鲁棒性。
我们系统的演示版本可在 https://github.com/jinzanxia/biomedical-QA 上获得。