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

Impact of hourly serial SOFA score on signaling emerging sepsis
Bin-Shenq Ho, Yan‐Hwa Wu Lee, Yi‐Bing Lin. Informatics in Medicine Unlocked, 2022. Cites: paper

Machine Learning for Early Sepsis Prediction in Intensive Care Units from 2017 to 2021: A Systematic Review of Prediction Horizons, Vital Sign Modalities, and Validation Strategies
Lucas Meyer, Anna Schmid, Stefan Braun. Journal of Artificial Intelligence for Healthcare Systems, 2022. Cites: paper

Multiplexed Host Immune Response Biosensor for Rapid Sepsis Stratification and Endotyping at Point-of-Care
Ambalika Sanjeev Tanak, Abha Umesh Sardesai, Sriram Muthukumar et al. Biosensors and Bioelectronics X, 2022. Cites: paper

Preclinical Stage of Building a Machine Learning System for Sepsis Prediction: A Comparative Study of Four Algorithms
Victor Iapăscurtă, A. Belîi. World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 2022. Cites: dataset

On Missingness Features in Machine Learning Models for Critical Care: Observational Study
Janmajay Singh, Masahiro Sato, Tomoko Ohkuma. JMIR Medical Informatics, 2021. Cites: paper

Validity, Reliability, and Significance
Stefan Riezler, Michael Hagmann. Synthesis lectures on human language technologies, 2021. Cites: paper

Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring
Yijing Li, Wenyu Ye, Kang Yang et al. Computer Methods and Programs in Biomedicine, 2021. Cites: paper

Predicting Sepsis in the Intensive Care Unit (ICU) through Vital Signs using Support Vector Machine (SVM)
Zeina Rayan, Marco Alfonse, Abdel-Badeeh Mohamed Salem. The Open Bioinformatics Journal, 2021. Cites: paper

OnAI-Comp: An Online AI Experts Competing Framework for Early Sepsis Detection
Anni Zhou, Raheem Beyah, Rishikesan Kamaleswaran. IEEE Transactions on Computational Biology and Bioinformatics, 2021. Cites: paper

Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation
Yao-Yi Kuo, Shu-Tien Huang, Hung‐Wen Chiu. BMC Medical Informatics and Decision Making, 2021. Cites: paper

The impact of recency and adequacy of historical information on sepsis predictions using machine learning
Manaf Zargoush, Alireza Sameh, Mahdi Javadi et al. Scientific Reports, 2021. Cites: paper

Learning time-dependent data with the signature transform
Adeline Fermanian. 2021. Cites: paper

Timing of antibiotic therapy in the ICU
Marin H. Kollef, Andrew F. Shorr, Matteo Bassetti et al. Critical Care, 2021. Cites: paper

Graph-Guided Network for Irregularly Sampled Multivariate Time Series
Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis et al. arXiv (Cornell University), 2021. Cites: paper

Prediction Model Performance With Different Imputation Strategies: A Simulation Study Using a North American ICU Registry
Jonathan Steif, Rollin Brant, Rama Syamala Sreepada et al. Pediatric Critical Care Medicine, 2021. Cites: paper

New Insights on Implementing and Evaluating Artificial Intelligence in Cardiovascular Care
Steven Dykstra, James A. White, Marina L. Gavrilova. Intelligent systems reference library, 2021. Cites: paper

Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines
Azade Tabaie, Evan William Orenstein, Shamim Nemati et al. Frontiers in Pediatrics, 2021. Cites: paper

Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series
Asif Rahman, Yale Chang, Jonathan Rubin. arXiv (Cornell University), 2021. Cites: paper

Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
Supreeth Prajwal Shashikumar, Gabriel Wardi, Atul Malhotra et al. npj Digital Medicine, 2021. Cites: paper

An Artificial Neural Network–Based Pediatric Mortality Risk Score: Development and Performance Evaluation Using Data From a Large North American Registry
Niema Ghanad Poor, Nicholas West, Rama Syamala Sreepada et al. JMIR Medical Informatics, 2021. Cites: paper

Early prediction and monitoring of sepsis using sequential long short term memory model
Deepak Kumar Sharma, Parul Lakhotia, Paras Sain et al. Expert Systems, 2021. Cites: paper

A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning
Yuzhuo Zhao, Lijing Jia, Ruiqi Jia et al. Shock, 2021. Cites: paper

Beyond performance metrics: modeling outcomes and cost for clinical machine learning
James A. Diao, Leia Wedlund, Joseph C. Kvedar. npj Digital Medicine, 2021. Cites: paper

A Machine Learning Sepsis Prediction Algorithm for Intended Intensive Care Unit Use (NAVOY Sepsis): Proof-of-Concept Study
Inger Persson, Andreas Östling, Martin Arlbrandt et al. JMIR Formative Research, 2021. Cites: paper

An Explainable Machine Learning Model for Early Prediction of Sepsis Using ICU Data
Naimahmed Nesaragi, Shivnarayan Patidar. IntechOpen eBooks, 2021. Cites: paper

A correlation matrix-based tensor decomposition method for early prediction of sepsis from clinical data
Naimahmed Nesaragi, Shivnarayan Patidar, Thangaraj Veerakumar. Journal of Applied Biomedicine, 2021. Cites: paper

A Locally Optimized Data-Driven Tool to Predict Sepsis-Associated Vasopressor Use in the ICU
Andre L. Holder, Supreeth Prajwal Shashikumar, Gabriel Wardi et al. Critical Care Medicine, 2021. Cites: paper

Predição de Sepse a partir de Dados do Atendimento Pré-Hospitalar
Naira Kaieski, Priscila Schmidt Lora, Cristiano André da Costa. 2021. Cites: paper

Inferring Granger Causality from Irregularly Sampled Time Series.
Wei Guo Song, Yao Xie, Christopher S. Josef et al. arXiv (Cornell University), 2021. Cites: paper

Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review
Michael Moor, Bastian Rieck, Max Horn et al. Frontiers in Medicine, 2021. Cites: paper

Kinematics approach with neural networks for early detection of sepsis (KANNEDS)
Márcio Freire Cruz, Naoaki Ono, Ming Huang et al. BMC Medical Informatics and Decision Making, 2021. Cites: paper

GRASP: Generic Framework for Health Status Representation Learning Based on Incorporating Knowledge from Similar Patients
Chaohe Zhang, Xin Gao, Liantao Ma et al. Proceedings of the AAAI Conference on Artificial Intelligence, 2021. Cites: paper

Med7: A transferable clinical natural language processing model for electronic health records
Andrey Kormilitzin, Nemanja Vaci, Qiang Liu et al. Artificial Intelligence in Medicine, 2021. Cites: paper

Distilling Knowledge from Publicly Available Online EMR Data to Emerging Epidemic for Prognosis
Liantao Ma, Xinyu Ma, Junyi Gao et al. International World Wide Web Conference (WWW), 2021. Cites: paper

Vital sign metrics of VLBW infants in three NICUs: implications for predictive algorithms
Amanda M. Zimmet, Brynne A. Sullivan, Karen D. Fairchild et al. Pediatric Research, 2021. Cites: paper

Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis
Fatemeh Amrollahi, Supreeth Prajwal Shashikumar, Fereshteh Razmi et al. medRxiv, 2021. Cites: dataset, paper

Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction
Sidney Le, Angier O. Allen, Jacob S. Calvert et al. Kidney International Reports, 2021. Cites: paper

Predicting presumed serious infection among hospitalized children on central venous lines with machine learning
Azade Tabaie, Evan William Orenstein, Shamim Nemati et al. Computers in Biology and Medicine, 2021. Cites: paper

Immune Deregulation in Sepsis and Septic Shock: Reversing Immune Paralysis by Targeting PD-1/PD-L1 Pathway
Yuki Nakamori, Eun Jeong Park, Motomu Shimaoka. Frontiers in Immunology, 2021. Cites: paper

High Inter-Patient Variability in Sepsis Evolution: A Hidden Markov Model Analysis
Jacquelyn Dawn Parente, J. Geoffrey Chase, Knut Möeller et al. Computer Methods and Programs in Biomedicine, 2021. Cites: paper

Early Prediction of Sepsis using Machine Learning
Anuraag Shankar, Mufaddal Diwan, Snigdha Singh et al. International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2021. Cites: paper

Assessment of the timeliness and robustness for predicting adult sepsis
Yuanfang Guan, Xueqing Wang, Xianghao Chen et al. iScience, 2021. Cites: paper

An interpretable deep-learning model for early prediction of sepsis in the emergency department
Dongdong Zhang, Changchang Yin, Katherine M. Hunold et al. Patterns, 2021. Cites: paper

Deep Learning Based Sepsis Intervention: The Modelling and Prediction of Severe Sepsis Onset
Gavin Tsang, Xianghua Xie. Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition, 2021. Cites: paper

A Machine Learning Approach to Predict the Sepsis Status: Analyzing the Connection Between Relevant Laboratory Values and Other Physiological Measurements Obtained in Intensive Care Unit
Sebahattin Babur, Sanam Moghaddamnia, Mehmet Recep Bozkurt. World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 2021. Cites: paper

Early Prediction of Sepsis Based on Machine Learning Algorithm
Xin Zhao, Wenqian Shen, Guanjun Wang. Computational Intelligence and Neuroscience, 2021. Cites: dataset, paper

Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning
Matthew Barren, Miloš Hauskrecht. Lecture notes in computer science, 2021. Cites: paper

Towards early sepsis detection from measurements at the general ward through deep learning
Sebastiaan Pascal Oei, Ruud J. G. van Sloun, Myrthe van der Ven et al. Intelligence-Based Medicine, 2021. Cites: paper

Voting of predictive models for clinical outcomes: consensus of algorithms for the early prediction of sepsis from clinical data and an analysis of the PhysioNet/Computing in Cardiology Challenge 2019
Matthew A. Reyna, Gari D. Clifford. arXiv (Cornell University), 2020. Cites: paper

Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation
Supreeth Prajwal Shashikumar, Gabriel Wardi, Paulina Paul et al. CHEST Journal, 2020. Cites: paper

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