@inproceedings{jeblee-etal-2019-extracting,
title = "Extracting relevant information from physician-patient dialogues for automated clinical note taking",
author = "Jeblee, Serena and
Khan Khattak, Faiza and
Crampton, Noah and
Mamdani, Muhammad and
Rudzicz, Frank",
editor = "Holderness, Eben and
Jimeno Yepes, Antonio and
Lavelli, Alberto and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6209",
doi = "10.18653/v1/D19-6209",
pages = "65--74",
abstract = "We present a system for automatically extracting pertinent medical information from dialogues between clinicians and patients. The system parses each dialogue and extracts entities such as medications and symptoms, using context to predict which entities are relevant. We also classify the primary diagnosis for each conversation. In addition, we extract topic information and identify relevant utterances. This serves as a baseline for a system that extracts information from dialogues and automatically generates a patient note, which can be reviewed and edited by the clinician.",
}
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<abstract>We present a system for automatically extracting pertinent medical information from dialogues between clinicians and patients. The system parses each dialogue and extracts entities such as medications and symptoms, using context to predict which entities are relevant. We also classify the primary diagnosis for each conversation. In addition, we extract topic information and identify relevant utterances. This serves as a baseline for a system that extracts information from dialogues and automatically generates a patient note, which can be reviewed and edited by the clinician.</abstract>
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%0 Conference Proceedings
%T Extracting relevant information from physician-patient dialogues for automated clinical note taking
%A Jeblee, Serena
%A Khan Khattak, Faiza
%A Crampton, Noah
%A Mamdani, Muhammad
%A Rudzicz, Frank
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F jeblee-etal-2019-extracting
%X We present a system for automatically extracting pertinent medical information from dialogues between clinicians and patients. The system parses each dialogue and extracts entities such as medications and symptoms, using context to predict which entities are relevant. We also classify the primary diagnosis for each conversation. In addition, we extract topic information and identify relevant utterances. This serves as a baseline for a system that extracts information from dialogues and automatically generates a patient note, which can be reviewed and edited by the clinician.
%R 10.18653/v1/D19-6209
%U https://aclanthology.org/D19-6209
%U https://doi.org/10.18653/v1/D19-6209
%P 65-74
Markdown (Informal)
[Extracting relevant information from physician-patient dialogues for automated clinical note taking](https://aclanthology.org/D19-6209) (Jeblee et al., Louhi 2019)
ACL