Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes

TL;DR

This paper introduces a new dataset and an attention-based aspect reasoning method for knowledge base question answering in clinical notes, improving the handling of complex, multi-patient queries.

Contribution

We created the ClinicalKBQA dataset with 9K QA pairs and proposed an attention-based aspect reasoning approach for enhanced clinical KBQA performance.

Findings

The AAR method outperforms baseline models.

Type and path aspects increase recall and reduce precision.

Entity and context aspects increase precision but lower recall.

Abstract

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.