  This paper aims to provide an approach for automatic coding of
physician-patient communication transcripts to improve patient-centered
communication (PCC). PCC is a central part of high-quality health care. To
improve PCC, dialogues between physicians and patients have been recorded and
tagged with predefined codes. Trained human coders have manually coded the
transcripts. Since it entails huge labor costs and poses possible human errors,
automatic coding methods should be considered for efficiency and effectiveness.
We adopted three machine learning algorithms (Na\"ive Bayes, Random Forest, and
Support Vector Machine) to categorize lines in transcripts into corresponding
codes. The result showed that there is evidence to distinguish the codes, and
this is considered to be sufficient for training of human annotators.
