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D J Albers

Showing results (1-10 of 9) with videos related to

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Chaos, Solitons, and Fractals|April 27, 2012
Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-seriesD J Albers, George Hripcsak
Physics Letters. A|June 15, 2010
A statistical dynamics approach to the study of human health data: resolving population scale diurnal variation in laboratory dataD J Albers, George Hripcsak
Chaos (Woodbury, N.Y.)|April 3, 2012
Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populationsD J Albers, George Hripcsak
Plos One|December 29, 2012
Population physiology: leveraging electronic health record data to understand human endocrine dynamicsD J Albers, George Hripcsak, Michael Schmidt
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|February 7, 2007
Persistent chaos in high dimensionsD J Albers, J C Sprott, J P Crutchfield
Plos One|June 17, 2014
Dynamical phenotyping: using temporal analysis of clinically collected physiologic data to stratify populationsD J Albers, Noémie Elhadad, E Tabak, et al.
Journal of Biomedical Informatics|January 26, 2018
Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithmsD J Albers, N Elhadad, J Claassen, et al.
Medrxiv : the Preprint Server for Health Sciences|September 4, 2023
A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimationYanran Wang, J N Stroh, George Hripcsak, et al.
Journal of Biomedical Informatics|November 20, 2023
A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimationYanran Wang, J N Stroh, George Hripcsak, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Chaos, Solitons, and Fractals|April 27, 2012
Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-seriesD J Albers, George Hripcsak
Physics Letters. A|June 15, 2010
A statistical dynamics approach to the study of human health data: resolving population scale diurnal variation in laboratory dataD J Albers, George Hripcsak
Chaos (Woodbury, N.Y.)|April 3, 2012
Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populationsD J Albers, George Hripcsak
Plos One|December 29, 2012
Population physiology: leveraging electronic health record data to understand human endocrine dynamicsD J Albers, George Hripcsak, Michael Schmidt
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|February 7, 2007
Persistent chaos in high dimensionsD J Albers, J C Sprott, J P Crutchfield
Plos One|June 17, 2014
Dynamical phenotyping: using temporal analysis of clinically collected physiologic data to stratify populationsD J Albers, Noémie Elhadad, E Tabak, et al.
Journal of Biomedical Informatics|January 26, 2018
Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithmsD J Albers, N Elhadad, J Claassen, et al.
Medrxiv : the Preprint Server for Health Sciences|September 4, 2023
A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimationYanran Wang, J N Stroh, George Hripcsak, et al.
Journal of Biomedical Informatics|November 20, 2023
A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimationYanran Wang, J N Stroh, George Hripcsak, et al.
Pageof 1