Generation of a Realistic Synthetic Laryngeal Cancer Cohort for AI Applications.

ABSTRACT:

Background

Obtaining large amounts of real patient data involves great efforts and expenses, and processing this data is fraught with data protection concerns. Consequently, data sharing might not always be possible, particularly when large, open science datasets are needed, as for AI development. For such purposes, the generation of realistic synthetic data may be the solution. Our project aimed to generate realistic cancer data with the use case of laryngeal cancer.

Methods

We used the open-source software Synthea and programmed an additional module for development, treatment and follow-up for laryngeal cancer by using external, real-world (RW) evidence from guidelines and cancer registries from Germany. To generate an incidence-based cohort view, we randomly drew laryngea

SUBMITTER: Katalinic M 

PROVIDER: S-EPMC10854797 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature