We are very excited to join forces with MLCommons and OctoML.ai! Contact Grigori Fursin for more details!

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.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives


Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!