Synthetic Generation of Patient Service Utilization Data: A Scalability Study.
Stud Health Technol Inform
; 316: 705-709, 2024 Aug 22.
Article
in En
| MEDLINE
| ID: mdl-39176892
ABSTRACT
To address privacy and ethical issues in using health data for machine learning, we evaluate the scalability of advanced synthetic data generation methods like GANs, VAEs, copulaGAN, and transformer models specifically for patient service utilization data. Our study examines five models on data from a Canadian health authority, focusing on training and generation efficiency, data resemblance, and practical utility. Our findings indicate that statistical models excel in efficiency, while most models produce synthetic data that closely mirrors real data, and is also useful for real-world applications.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Machine Learning
Limits:
Humans
Country/Region as subject:
America do norte
Language:
En
Journal:
Stud Health Technol Inform
/
Stud. health technol. inform.
/
Studies in health technology and informatics (Online)
Journal subject:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: