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        <title>Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations</title>
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        <namePart type="given">Nazmul</namePart>
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    <name type="personal">
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    <abstract>Electronic health records (EHRs) are notorious for reducing the face-to-face time with patients while increasing the screen-time for clinicians leading to burnout. This is especially problematic for psychiatry care in which maintaining consistent eye-contact and non-verbal cues are just as important as the spoken words. In this ongoing work, we explore the feasibility of automatically generating psychiatric EHR case notes from digital transcripts of doctor-patient conversation using a two-step approach: (1) predicting semantic topics for segments of transcripts using supervised machine learning, and (2) generating formal text of those segments using natural language processing. Through a series of preliminary experimental results obtained through a collection of synthetic and real-life transcripts, we demonstrate the viability of this approach.</abstract>
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    <identifier type="doi">10.18653/v1/W19-1918</identifier>
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        <date>2019-jun</date>
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            <start>140</start>
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%0 Conference Proceedings
%T Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations
%A Kazi, Nazmul
%A Kahanda, Indika
%S Proceedings of the 2nd Clinical Natural Language Processing Workshop
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F kazi-kahanda-2019-automatically
%X Electronic health records (EHRs) are notorious for reducing the face-to-face time with patients while increasing the screen-time for clinicians leading to burnout. This is especially problematic for psychiatry care in which maintaining consistent eye-contact and non-verbal cues are just as important as the spoken words. In this ongoing work, we explore the feasibility of automatically generating psychiatric EHR case notes from digital transcripts of doctor-patient conversation using a two-step approach: (1) predicting semantic topics for segments of transcripts using supervised machine learning, and (2) generating formal text of those segments using natural language processing. Through a series of preliminary experimental results obtained through a collection of synthetic and real-life transcripts, we demonstrate the viability of this approach.
%R 10.18653/v1/W19-1918
%U https://aclanthology.org/W19-1918
%U https://doi.org/10.18653/v1/W19-1918
%P 140-148

Markdown (Informal)

[Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations](https://aclanthology.org/W19-1918) (Kazi & Kahanda, 2019)

• Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations (Kazi & Kahanda, 2019)

ACL

• Nazmul Kazi and Indika Kahanda. 2019. Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 140–148, Minneapolis, Minnesota, USA. Association for Computational Linguistics.