We present Contextual Discourse Vectors (CDV), a distributed document
representation for efficient answer retrieval from long healthcare documents.
Our approach is based on structured query tuples of entities and aspects from
free text and medical taxonomies. Our model leverages a dual encoder
architecture with hierarchical LSTM layers and multi-task training to encode
the position of clinical entities and aspects alongside the document discourse.
We use our continuous representations to resolve queries with short latency
using approximate nearest neighbor search on sentence level. We apply the CDV
model for retrieving coherent answer passages from nine English public health
resources from the Web, addressing both patients and medical professionals.
Because there is no end-to-end training data available for all application
scenarios, we train our model with self-supervised data from Wikipedia. We show
that our generalized model significantly outperforms several state-of-the-art
baselines for healthcare passage ranking and is able to adapt to heterogeneous
domains without additional fine-tuning.

本文提出了基于结构化查询元组和医疗术语的语境话语向量（CDV）来有效地从长篇医疗文档中检索答案的方法。该方法利用了层次 LSTM 神经网络，其位置编码的编码能力以及在句子层面上的近似最近邻搜索技术。研究结果表明，我们的方法在医疗段落排名方面表现优于现有的多个基准，并且适应多领域且不需要额外的微调。