@inproceedings{meripo-konam-2021-extracting,
title = "Extracting Appointment Spans from Medical Conversations",
author = "Meripo, Nimshi Venkat and
Konam, Sandeep",
booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlpmc-1.6",
doi = "10.18653/v1/2021.nlpmc-1.6",
pages = "41--46",
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.",
}
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%0 Conference Proceedings
%T Extracting Appointment Spans from Medical Conversations
%A Meripo, Nimshi Venkat
%A Konam, Sandeep
%S Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations
%D 2021
%8 jun
%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)
ACL