Order-Based Representation in Random Networks of Cortical Neurons. - Goded Shahaf; Danny Eytan; Asaf Gal; Einat Kermany; Vladimir Lyakhov; Christoph Zrenner; Shimon Marom (PLoS Comput. Biol., 2008)

Selective Adaptation in Networks of Heterogeneous Populations: Model, Simulation, and Experiment. - Avner Wallach; Danny Eytan; Shimon Marom; Ron Meir (PLoS Comput. Biol., 2008)

On the precarious path of reverse neuro-engineering. - Shimon Marom; Ron Meir; Erez Braun; Asaf Gal; Einat Kermany; Danny Eytan (Frontiers Comput. Neurosci., 2009)

Classification of Atrial Fibrillation Using Multidisciplinary Features and Gradient Boosting. - Andrew J. Goodwin; Sebastian Goodfellow; Danny Eytan; Robert Greer; Mjaye Mazwi; Peter Laussen (CinC, 2017)

Towards Understanding ECG Rhythm Classification Using Convolutional Neural Networks and Attention Mappings. - Sebastian D. Goodfellow; Andrew J. Goodwin; Robert Greer; Peter C. Laussen; Mjaye Mazwi; Danny Eytan (MLHC, 2018)

Prediction of Cardiac Arrest from Physiological Signals in the Pediatric ICU. - Sana Tonekaboni; Mjaye Mazwi; Peter Laussen; Danny Eytan; Robert Greer; Sebastian D. Goodfellow; Andrew J. Goodwin; Michael Brudno; Anna Goldenberg (MLHC, 2018)

Domain Adaptation Using Riemannian Geometry of Spd Matrices. - Gal Maman; Or Yair; Danny Eytan; Ronen Talmon (ICASSP, 2019)

Rhythm Classification of 12-Lead ECGs Using Deep Neural Networks and Class-Activation Maps for Improved Explainability. - Sebastian D. Goodfellow; Dmitrii Shubin; Robert W. Greer; Sujay Nagaraj; Carson McLean; Will Dixon; Andrew J. Goodwin; Azadeh Assadi; Anusha Jegatheeswaran; Peter C. Laussen; Mjaye Mazwi; Danny Eytan (CinC, 2020)

Blood Pressure Estimation From PPG Signals Using Convolutional Neural Networks And Siamese Network. - Oded Schlesinger; Nitai Vigderhouse; Danny Eytan; Yair Moshe (ICASSP, 2020)

Using deep networks for scientific discovery in physiological signals. - Tom Beer; Bar Eini-Porat; Sebastian Goodfellow; Danny Eytan; Uri Shalit (MLHC, 2020)

Generative ODE Modeling with Known Unknowns. - Ori Linial; Danny Eytan; Uri Shalit (CoRR, 2020)

Generative ODE modeling with known unknowns. - Ori Linial; Neta Ravid; Danny Eytan; Uri Shalit (CHIL, 2021)

Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. - Sana Tonekaboni; Danny Eytan; Anna Goldenberg (ICLR, 2021)

About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations. - Dmitrii Shubin; Danny Eytan; Sebastian D. Goodfellow (CoRR, 2021)

Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study. - Michael Roimi; Rom Gutman; Jonathan Somer; Asaf Ben Arie; Ido Calman; Yaron Bar-Lavie; Udi Gelbshtein; Sigal Liverant-Taub; Arnona Ziv; Danny Eytan; Malka Gorfine; Uri Shalit (J. Am. Medical Informatics Assoc., 2021)

Get To The Point! Problem-Based Curated Data Views To Augment Care For Critically Ill Patients. - Minfan Zhang; Daniel Ehrmann; Mjaye Mazwi; Danny Eytan; Marzyeh Ghassemi; Fanny Chevalier (CHI, 2022)

Learning Unsupervised Representations for ICU Timeseries. - Addison Weatherhead; Robert Greer; Michael-Alice Moga; Mjaye Mazwi; Danny Eytan; Anna Goldenberg; Sana Tonekaboni (CHIL, 2022)

Enhancing Causal Estimation through Unlabeled Offline Data. - Ron Teichner; Danny Eytan; Ron Meir (ICFSP, 2022)

Continuous Forecasting via Neural Eigen Decomposition of Stochastic Dynamics. - Stav Belogolovsky; Ido Greenberg; Danny Eytan; Shie Mannor (CoRR, 2022)

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; Sabine Van Huffel; Eric T. Payne; Kristin L. McBain; Danny Eytan; Joachim A. Behar (CoRR, 2022)

Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Event. - Jonathan A. Sobel; Ronit Almog; Leo Anthony Celi; Michal Gaziel Yablowitz; Danny Eytan; Joachim A. Behar (CoRR, 2022)

An integration engineering framework for machine learning in healthcare. - Azadeh Assadi; Peter C. Laussen; Andrew J. Goodwin; Sebastian Goodfellow; Will Dixon; Robert W. Greer; Anusha Jegatheeswaran; Devin Singh; Melissa D. McCradden; Sara N. Gallant; Anna Goldenberg; Danny Eytan; et al. (Frontiers Digit. Health, 2022)

Timing errors and temporal uncertainty in clinical databases - A narrative review. - Andrew J. Goodwin; Danny Eytan; Will Dixon; Sebastian D. Goodfellow; Zakary Doherty; Robert W. Greer; Alistair Lee McEwan; Mark B. Tracy; Peter C. Laussen; Azadeh Assadi; Mjaye Mazwi (Frontiers Digit. Health, 2022)

Tell me something interesting: Clinical utility of machine learning prediction models in the ICU. - Bar Eini-Porat; Ofra Amir; Danny Eytan; Uri Shalit (J. Biomed. Informatics, 2022)

Individualized Dosing Dynamics via Neural Eigen Decomposition. - Stav Belogolovsky; Ido Greenberg; Danny Eytan; Shie Mannor (NeurIPS, 2023)

Making machine learning matter to clinicians: model actionability in medical decision-making. - Daniel Ehrmann; Shalmali Joshi; Sebastian D. Goodfellow; Mjaye Mazwi; Danny Eytan (npj Digit. Medicine, 2023)

iCVS - Inferring Cardio-Vascular hidden States from physiological signals available at the bedside. - Neta Ravid Tannenbaum; Omer Gottesman; Azadeh Assadi; Mjaye Mazwi; Uri Shalit; Danny Eytan (PLoS Comput. Biol., 2023)

Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Sensor Data. - Sujay Nagaraj; Andrew J. Goodwin; Dmytro Lopushanskyy; Sebastian D. Goodfellow; Danny Eytan; Hadrian Balaci; Robert Greer; Anand Jayarajan; Azadeh Assadi; Mjaye Leslie Mazwi; Anna Goldenberg (MLHC, 2024)

Aiming for Relevance. - Bar Eini-Porat; Danny Eytan; Uri Shalit (CoRR, 2024)

Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data. - Sujay Nagaraj; Andrew J. Goodwin; Dmytro Lopushanskyy; Danny Eytan; Robert W. Greer; Sebastian D. Goodfellow; Azadeh Assadi; Anand Jayarajan; Anna Goldenberg; Mjaye L. Mazwi (CoRR, 2024)