 Connection
Debashis Ghosh to Machine Learning
This is a "connection" page, showing publications Debashis Ghosh has written about Machine Learning.
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Connection Strength |
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2.483 |
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Suresh K, Severn C, Ghosh D. Survival prediction models: an introduction to discrete-time modeling. BMC Med Res Methodol. 2022 07 26; 22(1):207.
Score: 0.679
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Severn C, Suresh K, G?rg C, Choi YS, Jain R, Ghosh D. A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features. Sensors (Basel). 2022 Jul 12; 22(14).
Score: 0.677
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Raghavan S, Josey K, Bahn G, Reda D, Basu S, Berkowitz SA, Emanuele N, Reaven P, Ghosh D. Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control. Ann Epidemiol. 2022 01; 65:101-108.
Score: 0.632
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Warsavage T, Xing F, Bar?n AE, Feser WJ, Hirsch E, Miller YE, Malkoski S, Wolf HJ, Wilson DO, Ghosh D. Quantifying the incremental value of deep learning: Application to lung nodule detection. PLoS One. 2020; 15(4):e0231468.
Score: 0.145
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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: 0.142
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Jensen AM, Tregellas JR, Sutton B, Xing F, Ghosh D. Kernel machine tests of association between brain networks and phenotypes. PLoS One. 2019; 14(3):e0199340.
Score: 0.134
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Edward JA, Josey K, Bahn G, Caplan L, Reusch JEB, Reaven P, Ghosh D, Raghavan S. Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis. Cardiovasc Diabetol. 2022 04 27; 21(1):58.
Score: 0.042
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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: 0.033
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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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