Generative deep learning for the development of a type 1 diabetes simulator.

ABSTRACT:

Background

Type 1 diabetes (T1D) simulators, crucial for advancing diabetes treatments, often fall short of capturing the entire complexity of the glucose-insulin system due to the imprecise approximation of the physiological models. This study introduces a simulation approach employing a conditional deep generative model. The aim is to overcome the limitations of existing T1D simulators by synthesizing virtual patients that more accurately represent the entire glucose-insulin system physiology.

Methods

Our methodology utilizes a sequence-to-sequence generative adversarial network to simulate virtual T1D patients causally. Causality is embedded in the model by introducing shifted input-output pairs during training, with a 90-min shift capturing the impact of input insulin an

SUBMITTER: Mujahid O 

PROVIDER: S-EPMC10944502 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature