Question Answering (QA) in clinical notes has gained a lot of attention in the past few years. Existing machine reading comprehension approaches in clinical domain can only handle questions about a single block of clinical texts and fail to retrieve information about multiple patients and their clinical notes. To handle more complex questions, we aim at creating knowledge base from clinical notes to link different patients and clinical notes, and performing knowledge base question answering (KBQA). Based on the expert annotations available in the n2c2 dataset, we first created the ClinicalKBQA dataset that includes around 9K QA pairs and covers questions about seven medical topics using more than 300 question templates. Then, we investigated an attention-based aspect reasoning (AAR) method for KBQA and analyzed the impact of different aspects of answers (e.g., entity, type, path, and context) for prediction. The AAR method achieves better performance due to the well-designed encoder and attention mechanism. From our experiments, we find that both aspects, type and path, enable the model to identify answers satisfying the general conditions and produce lower precision and higher recall. On the other hand, the aspects, entity and context, limit the answers by node-specific information and lead to higher precision and lower recall.
翻译:临床笔记中的问题解答(QA)在过去几年里引起了人们的极大关注。临床领域的现有机器阅读理解方法只能处理一个整块临床文本的问题,而不能检索关于多个病人及其临床笔记的信息。为了处理更复杂的问题,我们的目标是从临床笔记中建立知识库,将不同的病人和临床笔记联系起来,并进行知识基础问题解答(KBQA)。根据n2c2数据集中的专家说明,我们首先创建了临床KBQA数据集,其中包括大约9KQA对,并使用300多个问题模板覆盖了7个医学专题的问题。然后,我们调查了KBQA基于关注的推理方法(AAR),并分析了不同答案(例如实体、类型、路径和背景)对预测的影响。根据精心设计的编码和关注机制,AAR方法取得了更好的业绩。我们从实验中发现,两个方面、类型和路径都使得模型能够找到满足一般条件的答案,并得出更低精确和更高的答案。在另一方面,从更精确的方面和精确度方面,从另一个方面,从更高的方面,从更高的方面,从更高的方面和回顾,从更高的方面,从更高的方面,从更高的方面,从更高的方面,到更高的方面,从更高的方面,从更高的方面,从更高的方面,从更高的方面,从更高的方面,从更高的方面,从更高的方面,从更高的方面,从更高的方面,到更高的方面,从更高的方面,到更高的,到更高的,从更高的,从上,从上,从上,从上,从上,从上,从上,从上,从上,从上,从上,从上,从上,从上,从上,从上,从上到上到上,从上到上到上到上到上,从上,从上,从上到上到上到上到上。