1 citations · 1 across the 7 of their papers we have counts for
8 papers
Observing the unobserved confounding through its effects: toward randomized trial-like estimates from real-world survival data
Vasiliki Stoumpou, Dimitris Bertsimas, Samuel Singer +1
Background: Randomized controlled trials (RCTs) are costly, time-consuming, and often infeasible, while treatment-effect estimation from observational data is limited by unobserved…
An Interpretable AI Tool for SAVR vs TAVR in Low to Intermediate Risk Patients with Severe Aortic Stenosis
Vasiliki Stoumpou, Maciej Tysarowski, Talhat Azemi +4
Background. Treatment selection for low to intermediate risk patients with severe aortic stenosis between surgical (SAVR) and transcatheter (TAVR) aortic valve replacement remains…
Detection and Localization of Subdural Hematoma Using Deep Learning on Computed Tomography
Vasiliki Stoumpou, Rohan Kumar, Bernard Burman +3
Background. Subdural hematoma (SDH) is a common neurosurgical emergency, with increasing incidence in aging populations. Rapid and accurate identification is essential to guide tim…
Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach
Phevos Paschalidis, Vasiliki Stoumpou, Lisa Everest +11
Transcatheter Aortic Valve Replacement (TAVR) has emerged as a prominent, minimally invasive treatment for patients with severe aortic stenosis, a life-threatening cardiovascular c…
Holistic Artificial Intelligence in Medicine; improved performance and explainability
Periklis Petridis, Georgios Margaritis, Vasiliki Stoumpou +1
With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to so…
Multimodal Prescriptive Deep Learning
Dimitris Bertsimas, Lisa Everest, Vasiliki Stoumpou
We introduce a multimodal deep learning framework, Prescriptive Neural Networks (PNNs), that combines ideas from optimization and machine learning, and is, to the best of our knowl…
Deep Trees for (Un)structured Data: Tractability, Performance, and Interpretability
Dimitris Bertsimas, Lisa Everest, Jiayi Gu +2
Decision Trees have remained a popular machine learning method for tabular datasets, mainly due to their interpretability. However, they lack the expressiveness needed to handle hi…
Binary Classification: Is Boosting stronger than Bagging?
Dimitris Bertsimas, Vasiliki Stoumpou
Random Forests have been one of the most popular bagging methods in the past few decades, especially due to their success at handling tabular datasets. They have been extensively s…