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        <title>Extracting relevant information from physician-patient dialogues for automated clinical note taking</title>
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        <namePart type="given">Serena</namePart>
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    <name type="personal">
        <namePart type="given">Faiza</namePart>
        <namePart type="family">Khan Khattak</namePart>
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    <name type="personal">
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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>
    <identifier type="citekey">jeblee-etal-2019-extracting</identifier>
    <identifier type="doi">10.18653/v1/D19-6209</identifier>
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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
%S Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
%D 2019
%8 nov
%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., EMNLP 2019)

• Extracting relevant information from physician-patient dialogues for automated clinical note taking (Jeblee et al., EMNLP 2019)

ACL

• Serena Jeblee, Faiza Khan Khattak, Noah Crampton, Muhammad Mamdani, and Frank Rudzicz. 2019. Extracting relevant information from physician-patient dialogues for automated clinical note taking. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), pages 65–74, Hong Kong. Association for Computational Linguistics.