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    <abstract>Extracting structured information from medical conversations can reduce the documentation burden for doctors and help patients follow through with their care plan. In this paper, we introduce a novel task of extracting appointment spans from medical conversations. We frame this task as a sequence tagging problem and focus on extracting spans for appointment reason and time. However, annotating medical conversations is expensive, time-consuming, and requires considerable domain expertise. Hence, we propose to leverage weak supervision approaches, namely incomplete supervision, inaccurate supervision, and a hybrid supervision approach and evaluate both generic and domain-specific, ELMo, and BERT embeddings using sequence tagging models. The best performing model is the domain-specific BERT variant using weak hybrid supervision and obtains an F1 score of 79.32.</abstract>
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%0 Conference Proceedings
%T Extracting Appointment Spans from Medical Conversations
%A Meripo, Nimshi Venkat
%A Konam, Sandeep
%Y Shivade, Chaitanya
%Y Gangadharaiah, Rashmi
%Y Gella, Spandana
%Y Konam, Sandeep
%Y Yuan, Shaoqing
%Y Zhang, Yi
%Y Bhatia, Parminder
%Y Wallace, Byron
%S Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F meripo-konam-2021-extracting
%X Extracting structured information from medical conversations can reduce the documentation burden for doctors and help patients follow through with their care plan. In this paper, we introduce a novel task of extracting appointment spans from medical conversations. We frame this task as a sequence tagging problem and focus on extracting spans for appointment reason and time. However, annotating medical conversations is expensive, time-consuming, and requires considerable domain expertise. Hence, we propose to leverage weak supervision approaches, namely incomplete supervision, inaccurate supervision, and a hybrid supervision approach and evaluate both generic and domain-specific, ELMo, and BERT embeddings using sequence tagging models. The best performing model is the domain-specific BERT variant using weak hybrid supervision and obtains an F1 score of 79.32.
%R 10.18653/v1/2021.nlpmc-1.6
%U https://aclanthology.org/2021.nlpmc-1.6
%U https://doi.org/10.18653/v1/2021.nlpmc-1.6
%P 41-46

Markdown (Informal)

[Extracting Appointment Spans from Medical Conversations](https://aclanthology.org/2021.nlpmc-1.6) (Meripo & Konam, NLPMC 2021)

• Extracting Appointment Spans from Medical Conversations (Meripo & Konam, NLPMC 2021)

ACL

• Nimshi Venkat Meripo and Sandeep Konam. 2021. Extracting Appointment Spans from Medical Conversations. In Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations, pages 41–46, Online. Association for Computational Linguistics.