ICU Mortality

3 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).

Latest papers with no code

Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards

no code yet • 17 Oct 2021

Electronic Health Record (EHR) systems provide critical, rich and valuable information at high frequency.

Early ICU Mortality Prediction and Survival Analysis for Respiratory Failure

no code yet • 6 Sep 2021

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

no code yet • 8 Apr 2021

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

no code yet • 24 Mar 2020

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.

Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation

no code yet • 20 Dec 2019

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

no code yet • 13 Sep 2019

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

no code yet • 23 May 2019

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

no code yet • 1 Apr 2019

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

no code yet • 27 Sep 2018

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

no code yet • 17 Jul 2017

At the reasoning layer, evidences across time steps are weighted and combined.