33 citations · 71 across the 10 of their papers we have counts for
10 papers
Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data
Sujay Nagaraj, Andrew J. Goodwin, Dmytro Lopushanskyy +7
Central Venous Lines (C-Lines) and Arterial Lines (A-Lines) are routinely used in the Critical Care Unit (CCU) for blood sampling, medication administration, and high-frequency blo…
Individualized Dosing Dynamics via Neural Eigen Decomposition
Stav Belogolovsky, Ido Greenberg, Danny Eytan +1
Dosing models often use differential equations to model biological dynamics. Neural differential equations in particular can learn to predict the derivative of a process, which per…
Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Event
Jonathan A. Sobel, Ronit Almog, Leo Anthony Celi +3
A datathon is a time-constrained competition involving data science applied to a specific problem. In the past decade, datathons have been shown to be a valuable bridge between fie…
Machine Learning to Support Triage of Children at Risk for Epileptic Seizures in the Pediatric Intensive Care Unit
Raphael Azriel, Cecil D. Hahn, Thomas De Cooman +5
Objective: Epileptic seizures are relatively common in critically-ill children admitted to the pediatric intensive care unit (PICU) and thus serve as an important target for identi…
Enhancing Causal Estimation through Unlabeled Offline Data
Ron Teichner, Ron Meir, Danny Eitan
Consider a situation where a new patient arrives in the Intensive Care Unit (ICU) and is monitored by multiple sensors. We wish to assess relevant unmeasured physiological variable…
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
Sana Tonekaboni, Danny Eytan, Anna Goldenberg
Time series are often complex and rich in information but sparsely labeled and therefore challenging to model. In this paper, we propose a self-supervised framework for learning ge…
About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations
Dmitrii Shubin, Danny Eytan, Sebastian D. Goodfellow
Self-supervised learning methods for computer vision have demonstrated the effectiveness of pre-training feature representations, resulting in well-generalizing Deep Neural Network…
Using Deep Networks for Scientific Discovery in Physiological Signals
Tom Beer, Bar Eini-Porat, Sebastian Goodfellow +2
Deep neural networks (DNN) have shown remarkable success in the classification of physiological signals. In this study we propose a method for examining to what extent does a DNN's…
Generative ODE Modeling with Known Unknowns
Ori Linial, Neta Ravid, Danny Eytan +1
In several crucial applications, domain knowledge is encoded by a system of ordinary differential equations (ODE), often stemming from underlying physical and biological processes.…
Neuronal Response Clamp
Avner Wallach, Danny Eytan, Asaf Gal +3
Since the first recordings made of evoked action potentials it has become apparent that the responses of individual neurons to ongoing physiologically relevant input, are highly va…