This research initiative endeavors to construct a speaker diarization framework tailored for the medical field, specifically targeting patient-doctor interactions. Presently, doctors dedicate a significant portion of their time to transcribing medical notes onto Electronic Health Records (EHR), leading to exhaustion. While professional medical scribes are often employed, they pose challenges in terms of training and retention. Moreover, the delay in receiving finalized notes, spanning between 30 minutes to an hour, disrupts workflow efficiency. The objective of this project is to develop an end-to-end deep learning driven speaker diarization system to improve the overall efficiency and workflow in medical setting. This system will seamlessly process raw audio sourced from either a microphone of computer/laptop or any mobile device and produce formatted medical notes.
Undergraduate
The project aims to yield a functional system (Web app/ Mobile app) capable of accurately transcribing conversations between medical professionals and patients.
A good background knowledge in deep learning, Python, PyTorch, and Flask and additional expertise in mobile app development would be highly desirable.
HASEL (405.662, Lab)