Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient-nurse verbal communications in home healthcare settings?

ABSTRACT:

Background

Identifying health problems in audio-recorded patient-nurse communication is important to improve outcomes in home healthcare patients who have complex conditions with increased risks of hospital utilization. Training machine learning classifiers for identifying problems requires resource-intensive human annotation.

Objective

To generate synthetic patient-nurse communication and to automatically annotate for common health problems encountered in home healthcare settings using GPT-4. We also examined whether augmenting real-world patient-nurse communication with synthetic data can improve the performance of machine learning to identify health problems.

Design

Secondary data analysis of patient-nurse verbal communication data in home healthcare settings.

M

SUBMITTER: Scroggins JK 

PROVIDER: S-EPMC12849707 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature