Synthesizing Electronic Health Records for Predictive Models in Low-Middle-Income Countries (LMICs).

ABSTRACT: The spread of machine learning models, coupled with by the growing adoption of electronic health records (EHRs), has opened the door for developing clinical decision support systems. However, despite the great promise of machine learning for healthcare in low-middle-income countries (LMICs), many data-specific limitations, such as the small size and irregular sampling, hinder the progress in such applications. Recently, deep generative models have been proposed to generate realistic-looking synthetic data, including EHRs, by learning the underlying data distribution without compromising patient privacy. In this study, we first use a deep generative model to generate synthetic data based on a small dataset (364 patients) from a LMIC setting. Next, we use synthetic data to build models that

SUBMITTER: Ghosheh GO 

PROVIDER: S-EPMC10295936 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature