Lacson Ronilda C, Barzilay Regina, Long William J
Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.
J Biomed Inform. 2006 Oct;39(5):541-55. doi: 10.1016/j.jbi.2005.12.009. Epub 2006 Feb 2.
Spoken medical dialogue is a valuable source of information for patients and caregivers. This work presents a first step towards automatic analysis and summarization of spoken medical dialogue. We first abstract a dialogue into a sequence of semantic categories using linguistic and contextual features integrated in a supervised machine-learning framework. Our model has a classification accuracy of 73%, compared to 33% achieved by a majority baseline (p<0.01). We then describe and implement a summarizer that utilizes this automatically induced structure. Our evaluation results indicate that automatically generated summaries exhibit high resemblance to summaries written by humans. In addition, task-based evaluation shows that physicians can reasonably answer questions related to patient care by looking at the automatically generated summaries alone, in contrast to the physicians' performance when they were given summaries from a naïve summarizer (p<0.05). This work demonstrates the feasibility of automatically structuring and summarizing spoken medical dialogue.
医学口语对话是患者和护理人员的重要信息来源。这项工作朝着医学口语对话的自动分析和总结迈出了第一步。我们首先使用集成在监督式机器学习框架中的语言和上下文特征，将对话抽象为一系列语义类别。我们的模型分类准确率为73%，而多数基线模型的准确率为33%（p<0.01）。然后，我们描述并实现了一个利用这种自动归纳结构的摘要器。我们的评估结果表明，自动生成的摘要与人类撰写的摘要高度相似。此外，基于任务的评估表明，与使用简单摘要器生成的摘要相比，医生仅通过查看自动生成的摘要就能合理回答与患者护理相关的问题（p<0.05）。这项工作证明了自动构建和总结医学口语对话的可行性。
