Speaking of Health: Leveraging Large Language Models to assess Exercise Motivation and Behavior of Rehabilitation Patients
Year: 2024
Type: conference-paper
Abstract: This paper aims to establish relationships between conversational markers and health outcomes using data from cardiopulmonary rehabilitation sessions.Specifically, we used speech and text data from conversations between patients and researchers to assess exercise compliance and psychological wellbeing.We trained a Multimodal Transformer (MMT) on speech, transcript, and ground-truth labels.We further evaluate MMT's predictive performance by using session summaries generated by three Large Language Models (LLMs), which focused on dialogue characteristics (e.g., sentiment, thematic content, and future planning).Our findings establish the feasibility of augmenting speech and language processing of clinical sessions to improve decision-making and health outcomes.
Source: Interspeech (USB)
Authors: Suhas BN, Amanda Rebar, Saeed Abdullah
Institutions: Pennsylvania State University, Central Queensland University
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4402112434