2 citations · 2 across the 3 of their papers we have counts for
6 papers
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…
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records
Linying Zhang, Yixin Wang, Anna Ostropolets +3
The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Rece…
The Parameter Houlihan: a solution to high-throughput identifiability indeterminacy for brutally ill-posed problems
DJ Albers, M Levine, L Mamykina +1
One way to interject knowledge into clinically impactful forecasting is to use data assimilation, a nonlinear regression that projects data onto a mechanistic physiologic model, in…
Characterizing Design Patterns of EHR-Driven Phenotype Extraction Algorithms
Yizhen Zhong, Luke Rasmussen, Yu Deng +10
The automatic development of phenotype algorithms from Electronic Health Record data with machine learning (ML) techniques is of great interest given the current practice is very t…
Methodological variations in lagged regression for detecting physiologic drug effects in EHR data
Matthew E. Levine, David J. Albers, George Hripcsak
We studied how lagged linear regression can be used to detect the physiologic effects of drugs from data in the electronic health record (EHR). We systematically examined the effec…
Offline and online data assimilation for real-time blood glucose forecasting in type 2 diabetes
Matthew E Levine, George Hripcsak, Lena Mamykina +2
We evaluate the benefits of combining different offline and online data assimilation methodologies to improve personalized blood glucose prediction with type 2 diabetes self-monito…