Conceptual scientific and medical advances have led to a recent realization that there may
be no single, one-size-fits-all diet and that differential human responses to dietary inputs …

The use of artificial intelligence, and the deep-learning subtype in particular, has been
enabled by the use of labeled big data, along with markedly enhanced computing power …

Background Artificial intelligence methods in combination with the latest technologies,
including medical devices, mobile computing, and sensor technologies, have the potential to …

Mathematical models of biological reactions at the system-level lead to a set of ordinary
differential equations with many unknown parameters that need to be inferred using …

Objective Implementation of machine learning (ML) may be limited by patients' right to
“meaningful information about the logic involved” when ML influences healthcare decisions …

Diabetes mellitus is characterized by elevated blood glucose levels, however patients with
diabetes may also develop hypoglycemia due to treatment. There is an increasing demand …

Highlights•People feature individualized glycemic responses to identical foods.•Genome
and microbiome data may explain individualized clinical features.•Personalized algorithms …

Background Closed-loop insulin delivery systems, which integrate continuous glucose
monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown …

Background: Type 2 diabetes mellitus (T2DM) is a major public health burden. Self-
management of diabetes including maintaining a healthy lifestyle is essential for glycemic …

This paper is focused on the optimization approach to the solution of inverse problems. We
introduce a stochastic dynamical system in which the parameter-to-data map is embedded …