Search Results for author:
Found 8 papers, 5 papers with code
Aiming for Relevance
This research paves the way for clinically relevant machine learning model evaluation and optimization, promising to improve ICU patient care.
Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Event
A datathon is a time-constrained competition involving data science applied to a specific problem.
Machine Learning to Support Triage of Children at Risk for Epileptic Seizures in the Pediatric Intensive Care Unit
This research aims to develop a computer aided tool to improve seizures risk assessment in critically-ill children, using an ubiquitously recorded signal in the PICU, namely the electrocardiogram (ECG).
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
Time series are often complex and rich in information but sparsely labeled and therefore challenging to model.
About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations
Self-supervised learning methods for computer vision have demonstrated the effectiveness of pre-training feature representations, resulting in well-generalizing Deep Neural Networks, even if the annotated data are limited.
Using Deep Networks for Scientific Discovery in Physiological Signals
In this study we propose a method for examining to what extent does a DNN's performance rely on rediscovering existing features of the signals, as opposed to discovering genuinely new features.
Generative ODE Modeling with Known Unknowns
A motivating example is intensive care unit patients: the dynamics of vital physiological functions, such as the cardiovascular system with its associated variables (heart rate, cardiac contractility and output and vascular resistance) can be approximately described by a known system of ODEs.
Towards understanding ECG rhythm classification using convolutional neural networks and attention mappings
Access to electronic health record (EHR) data has motivated computational advances in medical research.
Ranked #1 on
Arrhythmia Detection
on The PhysioNet Computing in Cardiology Challenge 2017
(Accuracy (TRAIN-DB) metric)
