Presentation Title
Detection and classification of connectional silences in palliative care conversations using deep learning
Abstract
Improving clinical conversations in serious illness is a priority for twenty-first century healthcare. Successful conversations between patients, families, and clinicians leave patients and families feeling heard and understood. However, these conversations are complex and dynamic. One potential feature of a successful palliative care conversation is the presence of “connectional silences” where a connection is made between the clinician and the patient and family. To help evaluate these complex interactions, we investigate the use of Deep-learning learning networks (DLNNs) to help automate the measurement of these connectional silences for use in large sample epidemiological studies. Specifically, we investigate whether DLNN can distinguish images (i.e., Gammatonegrams) of Connectional Silences from those representing other types of Conversational Pauses.
Primary Faculty Mentor Name
Dr. Donna Rizzo
Faculty/Staff Collaborators
Dr. Donna Rizzo (Advisor), Dr. Bob Gramling (PI), Dr. Safwan Wshah (Advisor), Larry Clarfeld (Collaborator), Brigitte Durieux (Collaborator), Cailin Gramling (Collaborator), Laura Hirsch (Collaborator), Jack Straton (Collaborator), Viktoria Manukyan (Collaborator)
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Complex Systems
Primary Research Category
Engineering & Physical Sciences
Detection and classification of connectional silences in palliative care conversations using deep learning
Improving clinical conversations in serious illness is a priority for twenty-first century healthcare. Successful conversations between patients, families, and clinicians leave patients and families feeling heard and understood. However, these conversations are complex and dynamic. One potential feature of a successful palliative care conversation is the presence of “connectional silences” where a connection is made between the clinician and the patient and family. To help evaluate these complex interactions, we investigate the use of Deep-learning learning networks (DLNNs) to help automate the measurement of these connectional silences for use in large sample epidemiological studies. Specifically, we investigate whether DLNN can distinguish images (i.e., Gammatonegrams) of Connectional Silences from those representing other types of Conversational Pauses.