David J. Albers, Ph.D.
Affiliations: | 2004 | University of Wisconsin, Madison, Madison, WI |
Area:
General Physics, MathematicsGoogle:
"David Albers"Parents
Sign in to add mentor| Julien Clinton Sprott | grad student | 2004 | UW Madison | |
| (A qualitative numerical study of high dimensional dynamical systems.) | ||||
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Publications
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| Burgermaster M, Son JH, Davidson PG, et al. (2020) A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study. International Journal of Medical Informatics. 139: 104158 |
| Albers DJ, Levine ME, Mamykina L, et al. (2019) The parameter Houlihan: A solution to high-throughput identifiability indeterminacy for brutally ill-posed problems. Mathematical Biosciences. 316: 108242 |
| Woldaregay AZ, Årsand E, Walderhaug S, et al. (2019) Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes. Artificial Intelligence in Medicine |
| Albers DJ, Levine ME, Stuart A, et al. (2018) Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype. Journal of the American Medical Informatics Association : Jamia. 25: 1392-1401 |
| Levine ME, Albers DJ, Hripcsak G. (2018) Methodological variations in lagged regression for detecting physiologic drug effects in EHR data. Journal of Biomedical Informatics |
| Albers DJ, Elhadad N, Claassen J, et al. (2018) Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms. Journal of Biomedical Informatics. 78: 87-101 |
| Hripcsak G, Albers DJ. (2017) High-fidelity phenotyping: richness and freedom from bias. Journal of the American Medical Informatics Association : Jamia |
| Albers DJ, Levine M, Gluckman B, et al. (2017) Personalized glucose forecasting for type 2 diabetes using data assimilation. Plos Computational Biology. 13: e1005232 |
| Albers DJ, Elhadad N, Tabak E, et al. (2014) Dynamical phenotyping: using temporal analysis of clinically collected physiologic data to stratify populations. Plos One. 9: e96443 |
| Albers DJ, Hripcsak G, Schmidt M. (2012) Population physiology: leveraging electronic health record data to understand human endocrine dynamics. Plos One. 7: e48058 |