13 citations · 13 across the 3 of their papers we have counts for
6 papers
Controlling Level of Unconsciousness by Titrating Propofol with Deep Reinforcement Learning
Gabe Schamberg, Marcus Badgeley, Emery N. Brown
Reinforcement Learning (RL) can be used to fit a mapping from patient state to a medication regimen. Prior studies have used deterministic and value-based tabular learning to learn…
Quantification of BERT Diagnosis Generalizability Across Medical Specialties Using Semantic Dataset Distance
Mihir P. Khambete, William Su, Juan Garcia +1
Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new d…
Constructing a control-ready model of EEG signal during general anesthesia in humans
John H. Abel, Marcus A. Badgeley, Taylor E. Baum +3
Significant effort toward the automation of general anesthesia has been made in the past decade. One open challenge is in the development of control-ready patient models for closed…
Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables
Marcus A. Badgeley, John R. Zech, Luke Oakden-Rayner +7
Hip fractures are a leading cause of death and disability among older adults. Hip fractures are also the most commonly missed diagnosis on pelvic radiographs. Computer-Aided Diagno…
Confounding variables can degrade generalization performance of radiological deep learning models
John R. Zech, Marcus A. Badgeley, Manway Liu +3
Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one…
Wide and deep volumetric residual networks for volumetric image classification
Varun Arvind, Anthony Costa, Marcus Badgeley +2
3D shape models that directly classify objects from 3D information have become more widely implementable. Current state of the art models rely on deep convolutional and inception m…