ICU Mortality
5 papers with code • 1 benchmarks • 2 datasets
Prediction of a patient mortality in the Intensive Care Unit (ICU) given its first hours of Electronic Health Record (EHR).
Benchmarks
These leaderboards are used to track progress in ICU Mortality
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
eICU-CRD
HiRID
Latest papers with no code
Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation
Thus, mortality prediction models using patient data from a particular ICU population may perform suboptimally in other populations because the features used to train such models have different distributions across the groups.
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing
Recordings in the first few hours of a patient's stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores within just 2 hours and achieving a state of the art Area Under the Receiver Operating Characteristic (AUROC) value of 0. 80 (95% CI 0. 79-0. 80) at 12 hours vs 0. 70 and 0. 66 for SAPS II and OASIS at 24 hours respectively.
Interpreting a Recurrent Neural Network's Predictions of ICU Mortality Risk
Deep learning has demonstrated success in many applications; however, their use in healthcare has been limited due to the lack of transparency into how they generate predictions.
The Impact of Extraneous Variables on the Performance of Recurrent Neural Network Models in Clinical Tasks
Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies.
Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk
We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
Deep Learning to Attend to Risk in ICU
At the reasoning layer, evidences across time steps are weighted and combined.
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models
This study makes contributions to time series classification and early ICU mortality prediction via identifying and enhancing temporal feature engineering and reduction methods for similarity-based time series classification.
PPMF: A Patient-based Predictive Modeling Framework for Early ICU Mortality Prediction
The first component captures dynamic changes of patients status in the ICU using their time series data (e. g., vital signs and laboratory tests).
The Dependence of Machine Learning on Electronic Medical Record Quality
There is growing interest in applying machine learning methods to Electronic Medical Records (EMR).
Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks
Viewing the trajectory of a patient as a dynamical system, a recurrent neural network was developed to learn the course of patient encounters in the Pediatric Intensive Care Unit (PICU) of a major tertiary care center.
