@inproceedings{enarvi-etal-2020-generating,
title = "Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models",
author = "Enarvi, Seppo and
Amoia, Marilisa and
Del-Agua Teba, Miguel and
Delaney, Brian and
Diehl, Frank and
Hahn, Stefan and
Harris, Kristina and
McGrath, Liam and
Pan, Yue and
Pinto, Joel and
Rubini, Luca and
Ruiz, Miguel and
Singh, Gagandeep and
Stemmer, Fabian and
Sun, Weiyi and
Vozila, Paul and
Lin, Thomas and
Ramamurthy, Ranjani",
editor = "Bhatia, Parminder and
Lin, Steven and
Gangadharaiah, Rashmi and
Wallace, Byron and
Shafran, Izhak and
Shivade, Chaitanya and
Du, Nan and
Diab, Mona",
booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpmc-1.4/",
doi = "10.18653/v1/2020.nlpmc-1.4",
pages = "22--30",
abstract = "We discuss automatic creation of medical reports from ASR-generated patient-doctor conversational transcripts using an end-to-end neural summarization approach. We explore both recurrent neural network (RNN) and Transformer-based sequence-to-sequence architectures for summarizing medical conversations. We have incorporated enhancements to these architectures, such as the pointer-generator network that facilitates copying parts of the conversations to the reports, and a hierarchical RNN encoder that makes RNN training three times faster with long inputs. A comparison of the relative improvements from the different model architectures over an oracle extractive baseline is provided on a dataset of 800k orthopedic encounters. Consistent with observations in literature for machine translation and related tasks, we find the Transformer models outperform RNN in accuracy, while taking less than half the time to train. Significantly large wins over a strong oracle baseline indicate that sequence-to-sequence modeling is a promising approach for automatic generation of medical reports, in the presence of data at scale."
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<abstract>We discuss automatic creation of medical reports from ASR-generated patient-doctor conversational transcripts using an end-to-end neural summarization approach. We explore both recurrent neural network (RNN) and Transformer-based sequence-to-sequence architectures for summarizing medical conversations. We have incorporated enhancements to these architectures, such as the pointer-generator network that facilitates copying parts of the conversations to the reports, and a hierarchical RNN encoder that makes RNN training three times faster with long inputs. A comparison of the relative improvements from the different model architectures over an oracle extractive baseline is provided on a dataset of 800k orthopedic encounters. Consistent with observations in literature for machine translation and related tasks, we find the Transformer models outperform RNN in accuracy, while taking less than half the time to train. Significantly large wins over a strong oracle baseline indicate that sequence-to-sequence modeling is a promising approach for automatic generation of medical reports, in the presence of data at scale.</abstract>
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%0 Conference Proceedings
%T Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models
%A Enarvi, Seppo
%A Amoia, Marilisa
%A Del-Agua Teba, Miguel
%A Delaney, Brian
%A Diehl, Frank
%A Hahn, Stefan
%A Harris, Kristina
%A McGrath, Liam
%A Pan, Yue
%A Pinto, Joel
%A Rubini, Luca
%A Ruiz, Miguel
%A Singh, Gagandeep
%A Stemmer, Fabian
%A Sun, Weiyi
%A Vozila, Paul
%A Lin, Thomas
%A Ramamurthy, Ranjani
%Y Bhatia, Parminder
%Y Lin, Steven
%Y Gangadharaiah, Rashmi
%Y Wallace, Byron
%Y Shafran, Izhak
%Y Shivade, Chaitanya
%Y Du, Nan
%Y Diab, Mona
%S Proceedings of the First Workshop on Natural Language Processing for Medical Conversations
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F enarvi-etal-2020-generating
%X We discuss automatic creation of medical reports from ASR-generated patient-doctor conversational transcripts using an end-to-end neural summarization approach. We explore both recurrent neural network (RNN) and Transformer-based sequence-to-sequence architectures for summarizing medical conversations. We have incorporated enhancements to these architectures, such as the pointer-generator network that facilitates copying parts of the conversations to the reports, and a hierarchical RNN encoder that makes RNN training three times faster with long inputs. A comparison of the relative improvements from the different model architectures over an oracle extractive baseline is provided on a dataset of 800k orthopedic encounters. Consistent with observations in literature for machine translation and related tasks, we find the Transformer models outperform RNN in accuracy, while taking less than half the time to train. Significantly large wins over a strong oracle baseline indicate that sequence-to-sequence modeling is a promising approach for automatic generation of medical reports, in the presence of data at scale.
%R 10.18653/v1/2020.nlpmc-1.4
%U https://aclanthology.org/2020.nlpmc-1.4/
%U https://doi.org/10.18653/v1/2020.nlpmc-1.4
%P 22-30
Markdown (Informal)
[Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models](https://aclanthology.org/2020.nlpmc-1.4/) (Enarvi et al., NLPMC 2020)
ACL
- Seppo Enarvi, Marilisa Amoia, Miguel Del-Agua Teba, Brian Delaney, Frank Diehl, Stefan Hahn, Kristina Harris, Liam McGrath, Yue Pan, Joel Pinto, Luca Rubini, Miguel Ruiz, Gagandeep Singh, Fabian Stemmer, Weiyi Sun, Paul Vozila, Thomas Lin, and Ranjani Ramamurthy. 2020. Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models. In Proceedings of the First Workshop on Natural Language Processing for Medical Conversations, pages 22–30, Online. Association for Computational Linguistics.