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.
Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations [link]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},
}

Downloads: 0