 Connection
David Albers to Electronic Health Records
This is a "connection" page, showing publications David Albers has written about Electronic Health Records.
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Connection Strength |
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2.937 |
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Wang Y, Stroh JN, Hripcsak G, Low Wang CC, Bennett TD, Wrobel J, Der Nigoghossian C, Mueller SW, Claassen J, Albers DJ. A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation. J Biomed Inform. 2023 12; 148:104547.
Score: 0.558
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Albers D, Sirlanci M, Levine M, Claassen J, Nigoghossian C, Hripcsak G. Interpretable physiological forecasting in the ICU using constrained data assimilation and electronic health record data. J Biomed Inform. 2023 09; 145:104477.
Score: 0.548
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Albers DJ, Elhadad N, Claassen J, Perotte R, Goldstein A, Hripcsak G. Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms. J Biomed Inform. 2018 02; 78:87-101.
Score: 0.373
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Hripcsak G, Albers DJ. Correlating electronic health record concepts with healthcare process events. J Am Med Inform Assoc. 2013 Dec; 20(e2):e311-8.
Score: 0.274
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Albers DJ, Hripcsak G, Schmidt M. Population physiology: leveraging electronic health record data to understand human endocrine dynamics. PLoS One. 2012; 7(12):e48058.
Score: 0.262
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Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc. 2013 Jan 01; 20(1):117-21.
Score: 0.257
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Levine ME, Albers DJ, Hripcsak G. Methodological variations in lagged regression for detecting physiologic drug effects in EHR data. J Biomed Inform. 2018 10; 86:149-159.
Score: 0.097
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Levine ME, Albers DJ, Hripcsak G. Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data. AMIA Annu Symp Proc. 2016; 2016:779-788.
Score: 0.087
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Hripcsak G, Albers DJ, Perotte A. Parameterizing time in electronic health record studies. J Am Med Inform Assoc. 2015 Jul; 22(4):794-804.
Score: 0.076
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Albers DJ, Elhadad N, Tabak E, Perotte A, Hripcsak G. Dynamical phenotyping: using temporal analysis of clinically collected physiologic data to stratify populations. PLoS One. 2014; 9(6):e96443.
Score: 0.073
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Pivovarov R, Albers DJ, Hripcsak G, Sepulveda JL, Elhadad N. Temporal trends of hemoglobin A1c testing. J Am Med Inform Assoc. 2014 Nov-Dec; 21(6):1038-44.
Score: 0.073
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Pivovarov R, Albers DJ, Sepulveda JL, Elhadad N. Identifying and mitigating biases in EHR laboratory tests. J Biomed Inform. 2014 Oct; 51:24-34.
Score: 0.072
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Boland MR, Hripcsak G, Albers DJ, Wei Y, Wilcox AB, Wei J, Li J, Lin S, Breene M, Myers R, Zimmerman J, Papapanou PN, Weng C. Discovering medical conditions associated with periodontitis using linked electronic health records. J Clin Periodontol. 2013 May; 40(5):474-82.
Score: 0.067
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Hripcsak G, Albers DJ, Perotte A. Exploiting time in electronic health record correlations. J Am Med Inform Assoc. 2011 Dec; 18 Suppl 1:i109-15.
Score: 0.061
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Sottile PD, Albers D, DeWitt PE, Russell S, Stroh JN, Kao DP, Adrian B, Levine ME, Mooney R, Larchick L, Kutner JS, Wynia MK, Glasheen JJ, Bennett TD. Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study. J Am Med Inform Assoc. 2021 10 12; 28(11):2354-2365.
Score: 0.030
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Fu LH, Knaplund C, Cato K, Perotte A, Kang MJ, Dykes PC, Albers D, Collins Rossetti S. Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events. J Am Med Inform Assoc. 2021 08 13; 28(9):1955-1963.
Score: 0.030
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Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
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