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Dimitris Bertsimas

Showing results (31-40 of 68) with videos related to

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American Journal of Transplantation : Official Journal of the American Society of Transplantation and the American Society of Transplant Surgeons|November 10, 2018
Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantationDimitris Bertsimas, Jerry Kung, Nikolaos Trichakis, et al.
Journal of Vascular Surgery. Venous and Lymphatic Disorders|May 2, 2025
An artificial intelligence interpretable tool to predict risk of deep vein thrombosis after endovenous thermal ablationAzadeh Tabari, Yu Ma, Jesus Alfonso, et al.
JCO Clinical Cancer Informatics|August 31, 2021
Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning ApproachHolly Wiberg, Peter Yu, Pat Montanaro, et al.
Plos One|May 22, 2020
Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk scoreAgni Orfanoudaki, Emma Chesley, Christian Cadisch, et al.
Research Square|April 1, 2025
The R.O.A.D. to clinical trial emulationDimitris Bertsimas, Angelos Koulouras, Hiroshi Nagata, et al.
Journal of Law and the Biosciences|May 2, 2022
Ethics-by-design: efficient, fair and inclusive resource allocation using machine learningTheodore P Papalexopoulos, Dimitris Bertsimas, I Glenn Cohen, et al.
World Journal for Pediatric & Congenital Heart Surgery|November 16, 2021
Benchmarking in Congenital Heart Surgery Using Machine Learning-Derived Optimal Classification TreesDimitris Bertsimas, Daisy Zhuo, Jordan Levine, et al.
JCO Clinical Cancer Informatics|January 18, 2019
Applied Informatics Decision Support Tool for Mortality Predictions in Patients With CancerDimitris Bertsimas, Jack Dunn, Colin Pawlowski, et al.
The Annals of Thoracic Surgery|December 8, 2023
Congenital Heart Surgery Machine Learning-Derived In-Depth Benchmarking ToolGeorge E Sarris, Daisy Zhuo, Luca Mingardi, et al.
The Annals of Thoracic Surgery|August 7, 2022
Improving Quality in Cardiothoracic Surgery: Exploiting the Untapped Potential of Machine LearningAgni Orfanoudaki, Joseph A Dearani, David M Shahian, et al.
Pageof 7

Showing results (31-40 of 68) with videos related to

Sort By:
Pageof 7
American Journal of Transplantation : Official Journal of the American Society of Transplantation and the American Society of Transplant Surgeons|November 10, 2018
Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantationDimitris Bertsimas, Jerry Kung, Nikolaos Trichakis, et al.
Journal of Vascular Surgery. Venous and Lymphatic Disorders|May 2, 2025
An artificial intelligence interpretable tool to predict risk of deep vein thrombosis after endovenous thermal ablationAzadeh Tabari, Yu Ma, Jesus Alfonso, et al.
JCO Clinical Cancer Informatics|August 31, 2021
Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning ApproachHolly Wiberg, Peter Yu, Pat Montanaro, et al.
Plos One|May 22, 2020
Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk scoreAgni Orfanoudaki, Emma Chesley, Christian Cadisch, et al.
Research Square|April 1, 2025
The R.O.A.D. to clinical trial emulationDimitris Bertsimas, Angelos Koulouras, Hiroshi Nagata, et al.
Journal of Law and the Biosciences|May 2, 2022
Ethics-by-design: efficient, fair and inclusive resource allocation using machine learningTheodore P Papalexopoulos, Dimitris Bertsimas, I Glenn Cohen, et al.
World Journal for Pediatric & Congenital Heart Surgery|November 16, 2021
Benchmarking in Congenital Heart Surgery Using Machine Learning-Derived Optimal Classification TreesDimitris Bertsimas, Daisy Zhuo, Jordan Levine, et al.
JCO Clinical Cancer Informatics|January 18, 2019
Applied Informatics Decision Support Tool for Mortality Predictions in Patients With CancerDimitris Bertsimas, Jack Dunn, Colin Pawlowski, et al.
The Annals of Thoracic Surgery|December 8, 2023
Congenital Heart Surgery Machine Learning-Derived In-Depth Benchmarking ToolGeorge E Sarris, Daisy Zhuo, Luca Mingardi, et al.
The Annals of Thoracic Surgery|August 7, 2022
Improving Quality in Cardiothoracic Surgery: Exploiting the Untapped Potential of Machine LearningAgni Orfanoudaki, Joseph A Dearani, David M Shahian, et al.
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