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
Cluster trajectory of SOFA score in predicting mortality in sepsis
Sequential Organ Failure Assessment (SOFA) score is commonly used to assess organ dysfunction and predict ICU mortality, but it is taken as a static measurement and fails to capture dynamic changes.
ICU Mortality Prediction Using Long Short-Term Memory Networks
Extensive bedside monitoring in Intensive Care Units (ICUs) has resulted in complex temporal data regarding patient physiology, which presents an upscale context for clinical data analysis.
An empirical study of using radiology reports and images to improve ICU mortality prediction
Background: The predictive Intensive Care Unit (ICU) scoring system plays an important role in ICU management because it predicts important outcomes, especially mortality.
Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data
This study employed the MIMIC-IV database as data source to investigate the use of dynamic, high-frequency, multivariate time-series vital signs data, including temperature, heart rate, mean blood pressure, respiratory rate, and SpO2, monitored first 8 hours data in the ICU stay.
Computable Phenotypes to Characterize Changing Patient Brain Dysfunction in the Intensive Care Unit
We developed algorithms to quantify acute brain dysfunction status including coma, delirium, normal, or death at 12-hour intervals of each ICU admission and to identify acute brain dysfunction phenotypes using continuous acute brain dysfunction status and k-means clustering approach.
Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards
Electronic Health Record (EHR) systems provide critical, rich and valuable information at high frequency.
Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction
Complex deep learning models show high prediction tasks in various clinical prediction tasks but their inherent complexity makes it more challenging to explain model predictions for clinicians and healthcare providers.
Early ICU Mortality Prediction and Survival Analysis for Respiratory Failure
Respiratory failure is the one of major causes of death in critical care unit.
CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data
Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e. g., healthcare applications where the data can suffer from irregularity and asynchronicity as the time between consecutive data points can vary across features and samples, hindering the application of existing deep learning models that are constructed for complete, evenly spaced data with fixed sequence lengths.
Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction
Intensive Care Unit Electronic Health Records (ICU EHRs) store multimodal data about patients including clinical notes, sparse and irregularly sampled physiological time series, lab results, and more.
