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 assimilation in the context of Type 2 diabetes to understand parameters associated with the disease. Results: The data assimilation method captures how well patients improve glucose tolerance after their surgery. Data assimilation has the potential to improve phenotyping in Type 2 diabetes.
@article{arxiv.2003.06541,
title = {Using Data Assimilation of Mechanistic Models to Estimate Glucose and Insulin Metabolism},
author = {Jami J. Mulgrave and Matthew E. Levine and David J. Albers and Joon Ha and Arthur Sherman and George Hripcsak},
journal= {arXiv preprint arXiv:2003.06541},
year = {2020}
}