Danny Eytan

Orcid: 0000-0001-7684-1429

According to our database1, Danny Eytan authored at least 28 papers between 2008 and 2024.

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Bibliography

2024
Aiming for Relevance.
CoRR, 2024

2023
iCVS - Inferring Cardio-Vascular hidden States from physiological signals available at the bedside.
PLoS Comput. Biol., 2023

Making machine learning matter to clinicians: model actionability in medical decision-making.
npj Digit. Medicine, 2023

Individualized Dosing Dynamics via Neural Eigen Decomposition.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Tell me something interesting: Clinical utility of machine learning prediction models in the ICU.
J. Biomed. Informatics, 2022

Timing errors and temporal uncertainty in clinical databases - A narrative review.
Frontiers Digit. Health, 2022

An integration engineering framework for machine learning in healthcare.
Frontiers Digit. Health, 2022

Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Event.
CoRR, 2022

Machine Learning to Support Triage of Children at Risk for Epileptic Seizures in the Pediatric Intensive Care Unit.
CoRR, 2022

Continuous Forecasting via Neural Eigen Decomposition of Stochastic Dynamics.
CoRR, 2022

Enhancing Causal Estimation through Unlabeled Offline Data.
Proceedings of the 7th International Conference on Frontiers of Signal Processing, 2022

Learning Unsupervised Representations for ICU Timeseries.
Proceedings of the Conference on Health, Inference, and Learning, 2022

Get To The Point! Problem-Based Curated Data Views To Augment Care For Critically Ill Patients.
Proceedings of the CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022, 2022

2021
Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study.
J. Am. Medical Informatics Assoc., 2021

About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations.
CoRR, 2021

Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding.
Proceedings of the 9th International Conference on Learning Representations, 2021

Generative ODE modeling with known unknowns.
Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

2020
Generative ODE Modeling with Known Unknowns.
CoRR, 2020

Using deep networks for scientific discovery in physiological signals.
Proceedings of the Machine Learning for Healthcare Conference, 2020

Blood Pressure Estimation From PPG Signals Using Convolutional Neural Networks And Siamese Network.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Rhythm Classification of 12-Lead ECGs Using Deep Neural Networks and Class-Activation Maps for Improved Explainability.
Proceedings of the Computing in Cardiology, 2020

2019
Domain Adaptation Using Riemannian Geometry of Spd Matrices.
Proceedings of the IEEE International Conference on Acoustics, 2019

2018
Prediction of Cardiac Arrest from Physiological Signals in the Pediatric ICU.
Proceedings of the Machine Learning for Healthcare Conference, 2018

Towards Understanding ECG Rhythm Classification Using Convolutional Neural Networks and Attention Mappings.
Proceedings of the Machine Learning for Healthcare Conference, 2018

2017
Classification of Atrial Fibrillation Using Multidisciplinary Features and Gradient Boosting.
Proceedings of the Computing in Cardiology, 2017

2009
On the precarious path of reverse neuro-engineering.
Frontiers Comput. Neurosci., 2009

2008
Selective Adaptation in Networks of Heterogeneous Populations: Model, Simulation, and Experiment.
PLoS Comput. Biol., 2008

Order-Based Representation in Random Networks of Cortical Neurons.
PLoS Comput. Biol., 2008


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