Physiological Data Assimilation
Physiological Data Assimilation is a computational framework that integrates multi-scale physiological measurements with mechanistic models through Bayesian inference to optimally estimate and refine individualized physiological states and parameters.
9
Publications
467
Citations
27
Authors
14
Institutions
Publications per year
2017–2026
3
9
Personalized glucose forecasting for type 2 diabetes using data assimilation
David J. Albers, Matthew E. Levine, Bruce J. Gluckman et al. · PLoS Computational Biology · 2017 · 109 citations · Full text
Computing, Nutrition, Forecasting +16
David J. Albers, Matthew E. Levine, Andrew M. Stuart et al. · Journal of the American Medical Informatics Association · 2018 · 43 citations · Full text
Medicine, Endocrinology, Physiology +17
1–5 of 9
27
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14
California Institute of Technology
Pasadena, United States
3 h-index
7 publications
184 citations

Pennsylvania State University
State College, United States
3 h-index
4 publications
180 citations
Columbia University
New York, United States
3 h-index
10 publications
731 citations
Neurological Surgery
Rockville Centre, United States
2 h-index
6 publications
141 citations
University of Southern California
Los Angeles, United States
2 h-index
6 publications
141 citations
Creative Technologies (United States)
Los Angeles, United States
2 h-index
6 publications
141 citations

University of Leeds
Leeds, United Kingdom
1 h-index
2 publications
38 citations

Northeast Ohio Medical University
Ravenna, United States
1 h-index
3 publications
54 citations
1–8 of 14
2 publications · 47 citations
2
h-index
1 publications · 148 citations
1
h-index
1 publications · 69 citations
1
h-index
1 publications · 18 citations
1
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1 publications · 19 citations
1
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