论文标题
医学对话中的学习功能部分：迭代伪标记和人类在循环的方法
Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach
论文作者
论文摘要
患者和医疗专业人员之间的医疗对话具有隐性功能部分，例如“历史记录”，“摘要”，“教育”和“护理计划”。在这项工作中，我们有兴趣学习自动提取这些部分。直接的方法将需要为此任务收集大量的专家注释，这本质上是由于这些部分之间的上下文之间的互相变异性。本文提出了一种方法，该方法解决了学习将医学对话分类为功能部分的问题，而无需大量注释。我们的方法结合了伪标签和人类在环境中。首先，我们使用伪标记的弱监督进行引导，以生成对话式伪标签并训练基于变压器的模型，然后将其应用于单个句子以创建嘈杂的句子级标签。其次，我们使用基于群集的人类在环境方法上迭代完善句子级标签。每次迭代只需要几十个注释者的决定。我们通过100个对话的专家注销数据集评估了结果，发现我们的模型以69.5％的精度开头，但我们可以迭代地将其提高到82.5％。可以在此处找到用于执行本文所述的所有实验的代码：https：//github.com/curai/curai-curai-research/tree/main/main/functional-sections。
Medical conversations between patients and medical professionals have implicit functional sections, such as "history taking", "summarization", "education", and "care plan." In this work, we are interested in learning to automatically extract these sections. A direct approach would require collecting large amounts of expert annotations for this task, which is inherently costly due to the contextual inter-and-intra variability between these sections. This paper presents an approach that tackles the problem of learning to classify medical dialogue into functional sections without requiring a large number of annotations. Our approach combines pseudo-labeling and human-in-the-loop. First, we bootstrap using weak supervision with pseudo-labeling to generate dialogue turn-level pseudo-labels and train a transformer-based model, which is then applied to individual sentences to create noisy sentence-level labels. Second, we iteratively refine sentence-level labels using a cluster-based human-in-the-loop approach. Each iteration requires only a few dozen annotator decisions. We evaluate the results on an expert-annotated dataset of 100 dialogues and find that while our models start with 69.5% accuracy, we can iteratively improve it to 82.5%. The code used to perform all experiments described in this paper can be found here: https://github.com/curai/curai-research/tree/main/functional-sections.
