Abstract

In clinical visits, clinical note writing is a timeconsuming and cost-prohibitive manual task for clinicians. Although virtual medical scribes have been proposed to generate clinical notes (semi-) automatically, the data sparsity issue is still a challenging problem in practice. Identifying the topic of clinical utterances in doctorpatient conversations is one of the key strategies for automation. In this paper, we propose an utterance-level note section classification method for the situation of the limited amount of in-house data. We leverage an external, unsupervised corpus of medical conversations to transfer knowledge using the framework of Unsupervised Meta-learning with Task Augmentation (UMTA). Our experiments are performed on both manual transcripts and machine transcripts generated by automatic speech recognition (ASR). The results show that our strategies achieve substantial gains in prediction accuracy over several baseline approaches and are robust to ASR errors.

Date

January 1, 1970

Authors

Zhuohao Chen, Jangwon Kim, Yang Liu, Shrikanth Narayanan

Conference

Proceedings of the Third Workshop on NLP for Medical Conversations

Pages

1-12