65 citations · 85 across the 11 of their papers we have counts for
11 papers
Hybrid Neural ODE Causal Modeling and an Application to Glycemic Response
Bob Junyi Zou, Matthew E. Levine, Dessi P. Zaharieva +2
Hybrid models composing mechanistic ODE-based dynamics with flexible and expressive neural network components have grown rapidly in popularity, especially in scientific domains whe…
Interpretable Forecasting of Physiology in the ICU Using Constrained Data Assimilation and Electronic Health Record Data
David Albers, Melike Sirlanci, Matthew Levine +3
Prediction of physiologic states are important in medical practice because interventions are guided by predicted impacts of interventions. But prediction is difficult in medicine b…
Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates
Ke Alexander Wang, Matthew E. Levine, Jiaxin Shi +1
Traditional models of glucose-insulin dynamics rely on heuristic parameterizations chosen to fit observations within a laboratory setting. However, these models cannot describe glu…
Using Data Assimilation of Mechanistic Models to Estimate Glucose and Insulin Metabolism
Jami J. Mulgrave, Matthew E. Levine, David J. Albers +3
Motivation: There is a growing need to integrate mechanistic models of biological processes with computational methods in healthcare in order to improve prediction. We apply data a…
Enabling Personalized Decision Support with Patient-Generated Data and Attributable Components
Elliot G Mitchell, Esteban G Tabak, Matthew E Levine +2
Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition fal…
A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System
M. Sirlanci, M. E. Levine, C. C. Low Wang +2
In this paper, we build a new, simple, and interpretable mathematical model to estimate and forecast physiology related to the human glucose-insulin system, constrained by availabl…