 Connection
David Albers to Machine Learning
This is a "connection" page, showing publications David Albers has written about Machine Learning.
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Connection Strength |
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1.590 |
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Albers DJ, Levine ME, Mamykina L, Hripcsak G. The parameter Houlihan: A solution to high-throughput identifiability indeterminacy for brutally ill-posed problems. Math Biosci. 2019 10; 316:108242.
Score: 0.393
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Albers DJ, Levine ME, Stuart A, Mamykina L, Gluckman B, Hripcsak G. Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype. J Am Med Inform Assoc. 2018 10 01; 25(10):1392-1401.
Score: 0.369
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Sirlanci M, Albers D, Kwak J, Smith C, Bennett TD, Bair SM. Navigating the landscape of personalized oncology: overcoming challenges and expanding horizons with computational modeling. J Am Med Inform Assoc. 2026 Jan 01; 33(1):242-251.
Score: 0.153
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Martin B, DeWitt PE, Payan M, Greer CH, Russell S, Gray C, Deakyne Davies SJ, Woods-Hill CZ, Scott HF, Parker S, Albers D, Bennett TD. Diagnostic Stewardship of Blood Cultures in the Pediatric ICU Using Machine Learning. Hosp Pediatr. 2025 Jun 01; 15(6):e240-e244.
Score: 0.147
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Mitchell EG, Tabak EG, Levine ME, Mamykina L, Albers DJ. Enabling personalized decision support with patient-generated data and attributable components. J Biomed Inform. 2021 01; 113:103639.
Score: 0.108
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Woldaregay AZ, Launonen IK, Albers D, Igual J, Årsand E, Hartvigsen G. A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism. J Med Internet Res. 2020 08 12; 22(8):e18912.
Score: 0.105
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Woldaregay AZ, Årsand E, Walderhaug S, Albers D, Mamykina L, Botsis T, Hartvigsen G. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes. Artif Intell Med. 2019 07; 98:109-134.
Score: 0.098
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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: 0.092
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Russell S, DeWitt PE, Helmkamp L, Colborn K, Gray C, Rebull M, Sierra YL, Greer R, Petruccelli L, Shankman S, Hankinson TC, Xing F, Albers DJ, Bennett TD. Predicting intracranial pressure monitor placement in children with traumatic brain injury: a prospective cohort study to develop a clinical decision support tool. J Am Med Inform Assoc. 2026 Jan 01; 33(1):182-192.
Score: 0.038
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Rossetti SC, Dykes PC, Knaplund C, Cho S, Withall J, Lowenthal G, Albers D, Lee RY, Jia H, Bakken S, Kang MJ, Chang FY, Zhou L, Bates DW, Daramola T, Liu F, Schwartz-Dillard J, Tran M, Bokhari SMA, Thate J, Cato KD. Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial. Nat Med. 2025 Jun; 31(6):1895-1902.
Score: 0.036
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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: 0.028
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Woldaregay AZ, Årsand E, Botsis T, Albers D, Mamykina L, Hartvigsen G. Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes. J Med Internet Res. 2019 05 01; 21(5):e11030.
Score: 0.024
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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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