A Deep Learning Model for Early Prediction of Sepsis from Intensive Care Unit Records

[1]  Yan Liu,et al.  Interpretable Deep Models for ICU Outcome Prediction , 2016, AMIA.

[2]  Jun S. Kim,et al.  An attention based deep learning model of clinical events in the intensive care unit , 2019, PloS one.

[3]  Moncef Gabbouj,et al.  Sepsis Prediction in Intensive Care Unit Using Ensemble of XGboost Models , 2019, 2019 Computing in Cardiology (CinC).

[4]  Philip de Chazal,et al.  Automated Prediction of Sepsis Onset Using Gradient Boosted Decision Trees , 2019, 2019 Computing in Cardiology (CinC).

[5]  James Morrill,et al.  The Signature-Based Model for Early Detection of Sepsis From Electronic Health Records in the Intensive Care Unit , 2019, 2019 Computing in Cardiology (CinC).

[6]  Shamim Nemati,et al.  Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019 , 2019, 2019 Computing in Cardiology (CinC).

[7]  Xing Liu,et al.  Early Prediction of Sepsis Using Multi-Feature Fusion Based XGBoost Learning and Bayesian Optimization , 2019, 2019 Computing in Cardiology Conference (CinC).

[8]  David M Kreindler,et al.  The effects of the irregular sample and missing data in time series analysis. , 2006, Nonlinear dynamics, psychology, and life sciences.

[9]  Jonathan Rubin,et al.  A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series , 2019, 2019 Computing in Cardiology (CinC).

[10]  Radovan Smisek,et al.  Sepsis Detection in Sparse Clinical Data Using Long Short-Term Memory Network with Dice Loss , 2019, 2019 Computing in Cardiology (CinC).

[11]  Yan Liu,et al.  Recurrent Neural Networks for Multivariate Time Series with Missing Values , 2016, Scientific Reports.

[12]  Shah Atiqur Rahman,et al.  Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data , 2015, J. Biomed. Informatics.