Year: 2024
Type: article
Abstract: Healthcare data accessibility for machine learning (ML) is encumbered by a range of stringent regulations and limitations. Using synthetic data that mirrors the underlying properties in the real data is emerging as a promising solution to overcome these barriers. We propose a fully automated synthetic tabular neural generator (STNG), which comprises multiple synthetic data generators and integrates an Auto-ML module to validate and comprehensively compare the synthetic datasets generated from different approaches. An empirical study was conducted to demonstrate the performance of STNG using twelve different datasets. The results highlight STNG's robustness and its pivotal role in enhancing the accessibility of validated synthetic healthcare data, thereby offering a promising solution to a critical barrier in ML applications in healthcare.
Source: Scientific Reports
Institutions: Cleveland Clinic, University of Pittsburgh
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,590
OpenAlex ID: W4403191965