论文标题
使用变压器语言模型的患者队列检索
Patient Cohort Retrieval using Transformer Language Models
论文作者
论文摘要
我们将基于学习的语言模型应用于患者队列检索（CR）的任务，目的是评估其功效。 CR的任务要求根据给定查询从电子健康记录（EHR）中提取相关文档。鉴于文档检索领域的最新进展，我们将CR的任务映射到文档检索任务，并应用针对通用域任务实施的各种深神经模型。在本文中，我们提出了一个框架，用于使用神经语言模型检索患者同类，而无需明确的功能工程和领域专业知识。我们发现，大多数模型在各种评估指标上的表现都优于BM25基线方法。
We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy. The task of CR requires the extraction of relevant documents from the electronic health records (EHRs) on the basis of a given query. Given the recent advancements in the field of document retrieval, we map the task of CR to a document retrieval task and apply various deep neural models implemented for the general domain tasks. In this paper, we propose a framework for retrieving patient cohorts using neural language models without the need of explicit feature engineering and domain expertise. We find that a majority of our models outperform the BM25 baseline method on various evaluation metrics.
