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Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations

lib:614ba61426ab25a2 (v1.0.0)

Authors: Nazmul Kazi,Kah,Indika a
Where published: WS 2019 6
Document:  PDF  DOI 
Abstract URL: https://www.aclweb.org/anthology/W19-1918/


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.

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