Towards The Automatic Coding of Medical Transcripts to Improve Patient-Centered Communication

• URL: http://arxiv.org/abs/2109.10514v1
• Date: Wed, 22 Sep 2021 04:37:05 GMT
• Title: Towards The Automatic Coding of Medical Transcripts to Improve Patient-Centered Communication
• Authors: Gilchan Park, Julia Taylor Rayz, Cleveland G. Shields
• Abstract summary: We adopt three machine learning algorithms to categorize lines in transcripts into corresponding codes. There is evidence to distinguish the codes, and this is considered to be sufficient for training of human annotators.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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