Automating annotation of information-giving for analysis of clinical conversation.
Coding of clinical communication for fine-grained features such as speech acts has produced a substantial literature. However, annotation by humans is laborious and expensive, limiting application of these methods. We aimed to show that through machine learning, computers could code certain categories of speech acts with sufficient reliability to make useful distinctions among clinical encounters.
Author(s): Mayfield, Elijah, Laws, M Barton, Wilson, Ira B, Penstein Rosé, Carolyn
DOI: 10.1136/amiajnl-2013-001898