Connection
Michael Rosenberg to Machine Learning
This is a "connection" page, showing publications Michael Rosenberg has written about Machine Learning.
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Connection Strength |
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2.451 |
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Mandair D, Tiwari P, Simon S, Colborn KL, Rosenberg MA. Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data. BMC Med Inform Decis Mak. 2020 10 02; 20(1):252.
Score: 0.579
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Tiwari P, Colborn KL, Smith DE, Xing F, Ghosh D, Rosenberg MA. Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation. JAMA Netw Open. 2020 01 03; 3(1):e1919396.
Score: 0.549
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Levy AE, Biswas M, Weber R, Tarakji K, Chung M, Noseworthy PA, Newton-Cheh C, Rosenberg MA. Applications of machine learning in decision analysis for dose management for dofetilide. PLoS One. 2019; 14(12):e0227324.
Score: 0.549
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Rosenberg MA. Deep Learning and Biased Prediction: More Questions Than Answers? Circ Heart Fail. 2024 01; 17(1):e011368.
Score: 0.181
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Simon ST, Trinkley KE, Malone DC, Rosenberg MA. Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis. J Med Internet Res. 2022 12 01; 24(12):e42163.
Score: 0.168
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Rosenberg MA. Trusting Magic: Interpretability of Predictions From Machine Learning Algorithms. Circulation. 2021 03 30; 143(13):1299-1301.
Score: 0.150
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Simon ST, Mandair D, Tiwari P, Rosenberg MA. Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data. J Cardiovasc Pharmacol Ther. 2021 07; 26(4):335-340.
Score: 0.149
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Marzec L, Raghavan S, Banaei-Kashani F, Creasy S, Melanson EL, Lange L, Ghosh D, Rosenberg MA. Device-measured physical activity data for classification of patients with ventricular arrhythmia events: A pilot investigation. PLoS One. 2018; 13(10):e0206153.
Score: 0.127
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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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