Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations. Kazi, N. & Kahanda, I. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 140–148, Minneapolis, Minnesota, USA, June, 2019. Association for Computational Linguistics. Paper abstract bibtex 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.
@InProceedings{kazi-kahanda-2019-automatically,
author = {Kazi, Nazmul and Kahanda, Indika},
title = {Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations},
booktitle = {Proceedings of the 2nd Clinical Natural Language Processing Workshop},
year = {2019},
publisher = {Association for Computational Linguistics},
month = jun,
pages = {140--148},
url = {https://www.aclweb.org/anthology/W19-1918},
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.},
address = {Minneapolis, Minnesota, USA},
}
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