12 citations · 23 across the 6 of their papers we have counts for
6 papers
Early Warning Index for Patient Deteriorations in Hospitals
Dimitris Bertsimas, Yu Ma, Kimberly Villalobos Carballo +5
Hospitals lack automated systems to harness the growing volume of heterogeneous clinical and operational data to effectively forecast critical events. Early identification of patie…
Prescribe-then-Select: Adaptive Policy Selection for Contextual Stochastic Optimization
Caio de Prospero Iglesias, Kimberly Villalobos Carballo, Dimitris Bertsimas
We address the problem of policy selection in contextual stochastic optimization (CSO), where covariates are available as contextual information and decisions must satisfy hard fea…
Patient Outcome Predictions Improve Operations at a Large Hospital Network
Liangyuan Na, Kimberly Villalobos Carballo, Jean Pauphilet +8
Problem definition: Access to accurate predictions of patients' outcomes can enhance medical staff's decision-making, which ultimately benefits all stakeholders in the hospitals. A…
Multistage Stochastic Optimization via Kernels
Dimitris Bertsimas, Kimberly Villalobos Carballo
We develop a non-parametric, data-driven, tractable approach for solving multistage stochastic optimization problems in which decisions do not affect the uncertainty. The proposed…
TabText: Language-Based Representations of Tabular Health Data for Predictive Modelling
Kimberly Villalobos Carballo, Liangyuan Na, Yu Ma +4
Tabular medical records remain the most readily available data format for applying machine learning in healthcare. However, traditional data preprocessing ignores valuable contextu…
Robust Upper Bounds for Adversarial Training
Dimitris Bertsimas, Xavier Boix, Kimberly Villalobos Carballo +1
Many state-of-the-art adversarial training methods for deep learning leverage upper bounds of the adversarial loss to provide security guarantees against adversarial attacks. Yet,…