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/functional-sections.
翻译:病人和医疗专业人员之间的医疗谈话含有隐含的职能部分, 如“ 历史取走”、“ 概括化” 、 “ 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。 一种直接的方法需要为此任务收集大量专家说明,由于这些部分之间的背景间和内部差异,这本来就是昂贵的。 本文提出了一种方法,解决学习将医疗对话分类为功能部分而不需要大量说明的问题。 我们的方法是把伪标签和人为的附加说明结合起来。 首先,我们用微弱的监管和假标签来生成对话转换等级的伪标签,并训练一个基于变压器的模型,然后用于个别的句子上创建吵闹的句级标签。 其次,我们反复地改进句级标签,使用基于集群的人在其中的语系内和圈内的对话, 只需要几十个说明性决定。 我们评估了100个对话专家附加说明的数据集的结果, 并且发现, 当我们的模型以69. 5% 的精确度开始, 我们用这个模型来进行纸质的实验。 我们用8 也可以使用这个代码到 。