论文标题
从医学对话中提取弱监督的药物治疗方案
Weakly Supervised Medication Regimen Extraction from Medical Conversations
论文作者
论文摘要
自动化药物方案（MR）从医疗对话中提取不仅可以改善召回率，并帮助患者遵循其护理计划，还可以减轻医生的文件负担。在本文中，我们专注于提取频率，路线和变化的跨度，与对话中讨论的药物相对应。我们首先描述了带注释的医生对话的独特数据集，然后提出一个弱监督的模型体系结构，可以使用嘈杂的分类数据执行跨度提取。该模型利用分类模型中的注意力瓶颈来执行提取。我们尝试了几种注意力评分和投影函数的变体，并提出了一种新型的基于变压器的注意力评分函数（TASCORE）。与添加剂评分和SoftMax投影的基线相比，提议的Tascore和Fusedmax投影的组合在最长的常见子字符线上增加了10点。
Automated Medication Regimen (MR) extraction from medical conversations can not only improve recall and help patients follow through with their care plan, but also reduce the documentation burden for doctors. In this paper, we focus on extracting spans for frequency, route and change, corresponding to medications discussed in the conversation. We first describe a unique dataset of annotated doctor-patient conversations and then present a weakly supervised model architecture that can perform span extraction using noisy classification data. The model utilizes an attention bottleneck inside a classification model to perform the extraction. We experiment with several variants of attention scoring and projection functions and propose a novel transformer-based attention scoring function (TAScore). The proposed combination of TAScore and Fusedmax projection achieves a 10 point increase in Longest Common Substring F1 compared to the baseline of additive scoring plus softmax projection.
