Lacson Ronilda, Barzilay Regina
Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.
AMIA Annu Symp Proc. 2005;2005:420-4.
Spoken medical dialogue is a valuable source of information, and it forms a foundation for diagnosis, prevention and therapeutic management. However, understanding even a perfect transcript of spoken dialogue is challenging for humans because of the lack of structure and the verbosity of dialogues. This work presents a first step towards automatic analysis of spoken medical dialogue. The backbone of our approach is an abstraction of a dialogue into a sequence of semantic categories. This abstraction uncovers structure in informal, verbose conversation between a caregiver and a patient, thereby facilitating automatic processing of dialogue content. Our method induces this structure based on a range of linguistic and contextual features that are 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). This work demonstrates the feasibility of automatically processing spoken medical dialogue.
医学口语对话是宝贵的信息来源，它为诊断、预防和治疗管理奠定了基础。然而，由于对话缺乏结构且冗长，即使是完美的口语对话转录本，人类理解起来也具有挑战性。这项工作朝着自动分析医学口语对话迈出了第一步。我们方法的核心是将对话抽象为一系列语义类别。这种抽象揭示了护理人员与患者之间非正式、冗长对话中的结构，从而便于自动处理对话内容。我们的方法基于一系列语言和上下文特征来归纳这种结构，这些特征被整合到一个监督式机器学习框架中。我们的模型分类准确率为73%，而多数基线的准确率为33%（p<0.01）。这项工作证明了自动处理医学口语对话的可行性。
