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Found 9 papers, 4 papers with code
HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data
The recent success of machine learning methods applied to time series collected from Intensive Care Units (ICU) exposes the lack of standardized machine learning benchmarks for developing and comparing such methods.
Ranked #1 on
Remaining Length of Stay
on HiRID
Neighborhood Contrastive Learning Applied to Online Patient Monitoring
Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients.
Early prediction of respiratory failure in the intensive care unit
The development of respiratory failure is common among patients in intensive care units (ICU).
WRSE -- a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU
Dynamic assessment of mortality risk in the intensive care unit (ICU) can be used to stratify patients, inform about treatment effectiveness or serve as part of an early-warning system.
DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps
We show that DPSOM achieves superior clustering performance compared to current deep clustering methods on MNIST/Fashion-MNIST, while maintaining the favourable visualization properties of SOMs.
Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps
There are very performant deep clustering models on the one hand and interpretable representation learning techniques, often relying on latent topological structures such as self-organizing maps, on the other hand.
Machine learning for early prediction of circulatory failure in the intensive care unit
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems.
Forecasting intracranial hypertension using multi-scale waveform metrics
Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours.
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.
