Publications citing Early Prediction of Sepsis from Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019 v1.0.0

Hybrid Feature Learning Using Autoencoders for Early Prediction of Sepsis
Jia Yao, Ming Lun Ong, Kar Kin Mun et al. Computing in cardiology, 2019. Cites: paper

Improving the Performance of a Neural Network for Early Prediction of Sepsis
ByeongTak Lee, KyungJae Cho, Oyeon Kwon et al. Computing in cardiology, 2019. Cites: paper

Machine Learning Algorithmic and System Level Considerations for Early Prediction of Sepsis
Lakshman Narayanaswamy, Devendra Garg, Bhargavi Narra et al. Computing in cardiology, 2019. Cites: paper

Memristor Models for Early Detection of Sepsis in ICU Patients
Vasileios Athanasiou, Zoran Konkoli. Computing in cardiology, 2019. Cites: paper

Novel Imputing Method and Deep Learning Techniques for Early Prediction of Sepsis in Intensive Care Units
Edwar Macias, Guillem Boquet, Javier Serrano et al. Computing in cardiology, 2019. Cites: paper

Prediction of Sepsis from Clinical Data Using Long Short-Term Memory and eXtreme Gradient Boosting
Yongchao Wang, Bin Xiao, Xiuli Bi et al. Computing in cardiology, 2019. Cites: paper

Randomly under Sampled Boosted Tree for Predicting Sepsis from Intensive Care Unit Databases
Peter Doggart, Megan Rutherford. Computing in cardiology, 2019. Cites: paper

Randomly Under Sampled Boosted Tree for Predicting Sepsis From Intensive Care Unit Databases
Peter Doggart, Megan J. Rutherford. Computing in cardiology, 2019. Cites: paper

Representation Learning for Early Sepsis Prediction
Luan Tran, Cyrus Shahabi, Manh Hung Nguyen. Computing in cardiology, 2019. Cites: paper

Sepsis Detection in Sparse Clinical Data Using Long Short-Term Memory Network with Dice Loss
Tomáš Vičar, Petra Novotná, Jakub Hejč et al. Computing in cardiology, 2019. Cites: paper

Sepsis Onset Prediction Applying a Stacked Combination of a Recurrent Neural Network and a Gradient Boosted Machine
Matthieu Scherpf, Miriam Goldammer, Hagen Malberg et al. Computing in cardiology, 2019. Cites: paper

Sepsis Prediction in Intensive Care Unit Using Ensemble of XGboost Models
Morteza Zabihi, Serkan Kiranyaz B, Moncef Gabbouj. Computing in cardiology, 2019. Cites: paper

TASP: A Time-Phased Model for Sepsis Prediction
Xiang Li, Yanni Kang, Xiaoyu Jia et al. Computing in cardiology, 2019. Cites: paper

The Signature-Based Model for Early Detection of Sepsis from Electronic Health Records in the Intensive Care Unit
James Morrill, Andrey Kormilitzin, Alejo J. Nevado‐Holgado et al. Computing in cardiology, 2019. Cites: paper

Time-Specific Metalearners for the Early Prediction of Sepsis
Marcus Vollmer, Christian F. Luz, Philipp Sodmann et al. Computing in cardiology, 2019. Cites: paper

Uncertainty-Aware Model for Reliable Prediction of Sepsis in the ICU
Marco A. F. Pimentel, Adam Mahdi, Oliver Redfern et al. Computing in cardiology, 2019. Cites: paper

Using Features Extracted From Vital Time Series for Early Prediction of Sepsis
Qiang Yu, Xiaolin Huang, Weifeng Li et al. Computing in cardiology, 2019. Cites: paper

Utilizing Informative Missingness for Early Prediction of Sepsis
Janmajay Singh, Kentaro Oshiro, Raghava Krishnan et al. Computing in cardiology, 2019. Cites: paper

Kernel density estimates for sepsis classification
Jacquelyn Dawn Parente, J. Geoffrey Chase, Knut Möller et al. Computer Methods and Programs in Biomedicine, 2019. Cites: paper

The Signature-Based Model for Early Detection of Sepsis From Electronic Health Records in the Intensive Care Unit
James Morrill, Andrey Kormilitzin, Alejo J. Nevado‐Holgado et al. Oxford University Research Archive (ORA) (University of Oxford), 2019. Cites: paper

Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregation
Luchen Liu, Haoxian Wu, Zichang Wang et al. arXiv (Cornell University), 2019. Cites: paper

Set Functions for Time Series
Max Horn, Michael Moor, Christian Bock et al. arXiv (Cornell University), 2019. Cites: paper

Predicting sepsis with a recurrent neural network using the MIMIC III database
Matthieu Scherpf, Felix Gräßer, Hagen Malberg et al. Computers in Biology and Medicine, 2019. Cites: paper

Early Prediction of Sepsis from Clinical Data Using Artificial Intelligence
R. Murat Demirer, Oya Demirer. Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), 2019. Cites: paper

Saving Lives With Algorithm-Enabled Process Innovation for Sepsis Care
Idris Adjerid, Mehmet Ayvaci, Özalp Özer. Manufacturing & Service Operations Management, 2019. Cites: paper

Sepsis Prediction in Intensive Care Unit Using Ensemble of XGboost Models
Morteza Zabihi, Kiranyaz, Mustafa Serkan, Moncef Gabbouj. Qatar University QSpace (Qatar University), 2019. Cites: paper

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