Generating synthetic mixed-type tabular data by decoding samples from a latent-space: a case study in healthcare

• Published in: Procedia computer science Vol. 246; pp. 2254 - 2263
• Main Authors: Drapała, Jarosław, Świątek, Jerzy
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2024
• Subjects: Kernel density estimation; Multidimensional scaling; Random forest regression; Support Vector Machine; Support Vector Machine; Kernel density estimation; Multidimensional scaling; Random forest regression

Medical data are subject to privacy regulations, which severely limit AI specialists who wish to construct decision support systems for medicine. Large amounts of this data are tabular, indicating that they are organized into a table format, where patient records are represented in rows and measured...