5 papers with code • 1 benchmarks • 1 datasets
Continuous prediction of onset of respiratory failure in the next 12h given the patient is not in failure now.
In settings with limited data, relaxed parameter sharing can lead to improved patient risk stratification performance.
In this paper, we propose a factored generalized additive model (F-GAM) to preserve the model interpretability for targeted features while allowing a rich model for interaction with features fixed within the individual.
TOF had superior performance compared to LOF and discord algorithms even in recognizing traditional outliers and it also recognized unique events that those did not.
Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure
Conclusions: Machine learning models combining chest radiographs and EHR data can accurately differentiate between common causes of acute respiratory failure.
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.