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Page 1
Congenital Heart Surgery Machine Learning-Derived In-Depth Benchmarking Tool.
Sarris GE, Zhuo D, Mingardi L, Dunn J, Levine J, Tobota Z, Maruszewski B, Fragata J, Bertsimas D. Sarris GE, et al. Among authors: dunn j. Ann Thorac Surg. 2023 Dec 6:S0003-4975(23)01242-0. doi: 10.1016/j.athoracsur.2023.10.034. Online ahead of print. Ann Thorac Surg. 2023. PMID: 38065331
Use of artificial intelligence for nonlinear benchmarking of surgical care.
Dorken-Gallastegi A, El Hechi M, Amram M, Naar L, Maurer LR, Gebran A, Dunn J, Zhuo YD, Levine J, Bertsimas D, Kaafarani HMA. Dorken-Gallastegi A, et al. Among authors: dunn j. Surgery. 2023 Dec;174(6):1302-1308. doi: 10.1016/j.surg.2023.08.025. Epub 2023 Sep 29. Surgery. 2023. PMID: 37778969
Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach.
Bertsimas D, Zhuo D, Dunn J, Levine J, Zuccarelli E, Smyrnakis N, Tobota Z, Maruszewski B, Fragata J, Sarris GE. Bertsimas D, et al. Among authors: dunn j. World J Pediatr Congenit Heart Surg. 2021 Jul;12(4):453-460. doi: 10.1177/21501351211007106. Epub 2021 Apr 28. World J Pediatr Congenit Heart Surg. 2021. PMID: 33908836
19 results