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

A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series
Satya Narayan Shukla, Benjamin M. Marlin. arXiv (Cornell University), 2020. Cites: paper

Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology
Ahmed Youssef Ali Amer, Femke Wouters, Julie Vranken et al. Sensors, 2020. Cites: paper

Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020
Erick Andres Perez Alday, Annie Gu, Amit Jasvant Shah et al. Physiological Measurement, 2020. Cites: dataset, paper

SSP: Early prediction of sepsis using fully connected LSTM-CNN model
Alireza Rafiei, Alireza Rezaee, Farshid Hajati et al. Computers in Biology and Medicine, 2020. Cites: paper

Two-Step Imputation and AdaBoost-Based Classification for Early Prediction of Sepsis on Imbalanced Clinical Data
Atefeh Baniasadi, Sepideh Rezaeirad, Habil Zare et al. Critical Care Medicine, 2020. Cites: paper

Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm
Gabriel Wardi, Morgan Carlile, Andre L. Holder et al. Annals of Emergency Medicine, 2020. Cites: paper

Early Prediction of Sepsis From Clinical Data Using Ratio and Power-Based Features
Naimahmed Nesaragi, Shivnarayan Patidar. Critical Care Medicine, 2020. Cites: paper

Survival prediction of patients with sepsis from age, sex, and septic episode number alone
Davide Chicco, Giuseppe Jurman. Scientific Reports, 2020. Cites: paper

Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records
Zhengling He, Lidong Du, Pengfei Zhang et al. Critical Care Medicine, 2020. Cites: paper

Graph Convolutional Networks-Based Noisy Data Imputation in Electronic Health Record
Byeong Tak Lee, Oyeon Kwon, Hyunho Park et al. Critical Care Medicine, 2020. Cites: paper

Predição de sepse em unidade de terapia intensiva: uma abordagem de aprendizado de máquina
Diogo Hideki Shiraishi, Ernesto Marujo. 2020. Cites: paper

Quantifying misclassification and bias errors due to hierarchical sepsis scores in real-time sepsis diagnosis
Jacquelyn Dawn Parente, J. Geoffrey Chase, Knut Möeller et al. Biomedical Signal Processing and Control, 2020. Cites: paper

Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning
Christian Bock, Michael Moor, Catherine Ruth Jutzeler et al. Methods in molecular biology, 2020. Cites: paper

Automated prediction of sepsis using temporal convolutional network
Christopher Kok, V. Jahmunah, Shu Lih Oh et al. Computers in Biology and Medicine, 2020. Cites: paper

An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis
Meicheng Yang, Chengyu Liu, Xingyao Wang et al. Critical Care Medicine, 2020. Cites: dataset, paper

Utilization of the Signature Method to Identify the Early Onset of Sepsis From Multivariate Physiological Time Series in Critical Care Monitoring
James Morrill, Andrey Kormilitzin, Alejo J. Nevado‐Holgado et al. Critical Care Medicine, 2020. Cites: paper

Cytokine status in patients with sepsis
С. D. Shapoval, И. Л. Савон, О. V. Тrybushnyi et al. Zaporozhye Medical Journal, 2020. Cites: paper

The Future AI in Healthcare: A Tsunami of False Alarms or a Product of Experts?
Gari D. Clifford. arXiv (Cornell University), 2020. Cites: paper

CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis
Liantao Ma, Xinyu Ma, Junyi Gao et al. arXiv (Cornell University), 2020. Cites: paper

Two-Stage Monitoring of Patients in Intensive Care Unit for Sepsis Prediction Using Non-Overfitted Machine Learning Models
Vytautas Abromavičius, Darius Plonis, Deividas Tarasevičius et al. Electronics, 2020. Cites: paper

Wireless monitoring and real-time adaptive predictive indicator of deterioration
Heather P. Duncan, Balazs Krisztian Fule, Iain Rice et al. Scientific Reports, 2020. Cites: dataset

A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care
Xiang Li, Xiao Xu, Fei Xie et al. Critical Care Medicine, 2020. Cites: paper

Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model
Alejandro Schuler, Aashish Bhardwaj, Vincent X. Liu. arXiv (Cornell University), 2020. Cites: paper

Path Imputation Strategies for Signature Models of Irregular Time Series
Michael Moor, Max Horn, Christian Bock et al. arXiv (Cornell University), 2020. Cites: paper

Early Detection of In-Patient Deterioration: One Prediction Model Does Not Fit All
Jacob Blackwell, Jessica Keim‐Malpass, Matthew Clark et al. Critical Care Explorations, 2020. Cites: paper

Augmented intelligence in pediatric anesthesia and pediatric critical care
Matthias Görges, J. Mark Ansermino. Current Opinion in Anaesthesiology, 2020. Cites: paper

Composite bioscore is superior to routine biomarkers and established scoring systems in predicting mortality in adult critically ill patients with secondary sepsis
Goran Rondović, Maja Šurbatović, Dragan Djordjevic et al. Vojnosanitetski pregled, 2020. Cites: paper

Sepsis Prediction using Continuous and Categorical Features on Sporadic Data
Varsha Sharma, Chirayata Bhattacharyya, Tanuka Bhattacharjee et al. IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2020. Cites: paper

Grappling With Real-Time Diagnosis and Public Health Surveillance in Sepsis: Can Clinical Data Provide the Answer?*
Matthias Görges, Niranjan “Tex” Kissoon. Pediatric Critical Care Medicine, 2020. Cites: paper

A Deep Learning Model for Early Prediction of Sepsis from Intensive Care Unit Records
Rui Zhao, Tao Wan, Deyu Li et al. Communications in computer and information science, 2020. Cites: paper

A Multi-task LSTM Framework for Improved Early Sepsis Prediction
Theodoros Tsiligkaridis, Jennifer Sloboda. Lecture notes in computer science, 2020. Cites: dataset

Early detection of Sepsis in critical patients using Random Forest Classifier
Fahim Mahmud, Naqib Sad Pathan, Muhammad Quamruzzaman. IEEE Region 10 Symposium (TENSYMP), 2020. Cites: paper

A Comparative Analysis of HMM and CRF for Early Prediction of Sepsis
Saman Noorzadeh, Shahrooz Faghihroohi, Mojtaba Zarei. Computing in cardiology, 2019. Cites: paper

A Comparison of Machine Learning Tools for Early Prediction of Sepsis from ICU Data
Po‐Ya Hsu, Chester Holtz. Computing in cardiology, 2019. Cites: paper

A Low Dimensional Algorithm for Detection of Sepsis from Electronic Medical Record Data
Aruna Deogire. Computing in cardiology, 2019. Cites: paper

An Algorithm for Early Detection of Sepsis Using Traditional Statistical Regression Modeling
Roshan Pawar, Jeffrey N. Bone, J. Mark Ansermino et al. Computing in cardiology, 2019. Cites: paper

An Ensemble LSTM Architecture for Clinical Sepsis Detection
Sven Schellenberger, Kilin Shi, Jan Philipp Wiedemann et al. tub.dok (Hamburg University of Technology), 2019. Cites: paper

Anomaly Detection Semi-Supervised Framework for Sepsis Treatment
Ines Krissaane, K. K. Hampton, Jumanah Yousef Alshenaifi et al. Computing in cardiology, 2019. Cites: paper

A Recurrent Neural Network for the Prediction of Vital Sign Evolution and Sepsis in ICU
Benjamin Roussel, Joachim A. Behar, Julien Oster. Computing in cardiology, 2019. Cites: paper

Automated Prediction of Sepsis Onset Using Gradient Boosted Decision Trees
John Du, Nadi Sadr, Philip de Chazal. Computing in cardiology, 2019. Cites: paper

Cracking the “Sepsis” Code: Assessing Time Series Nature of EHR Data, and Using Deep Learning for Early Sepsis Prediction
Soodabeh Sarafrazi, Rohini S Choudhari, Chiral Mehta et al. Computing in cardiology, 2019. Cites: paper

Demographic Information Initialized Stacked Gated Recurrent Unit for an Early Prediction of Sepsis
Naoki Nonaka, Jun Seita. Computing in cardiology, 2019. Cites: paper

Early Detection of Sepsis Using Ensemblers
Shailesh Nirgudkar, Tianyu Ding. Computing in cardiology, 2019. Cites: paper

Early Prediction of Sepsis from Clinical Data: the PhysioNet/Computing in Cardiology Challenge 2019
Matthew A. Reyna, Christopher S. Josef, Salman S. Seyedi et al. Computing in cardiology, 2019. Cites: paper

Early Prediction of Sepsis from Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019
Matthew A. Reyna, Supreeth Prajwal Shashikumar, Benjamin Moody et al. Computing in cardiology, 2019. Cites: paper

Early Prediction of Sepsis From Clinical Data via Heterogeneous Event Aggregation
Luchen Liu, Haoxian Wu, Zichang Wang et al. Computing in cardiology, 2019. Cites: paper

Early Prediction of Sepsis Using Gradient Boosting Decision Trees with Optimal Sample Weighting
Ibrahim Hammoud, I. V. Ramakrishnan, Mark C. Henry. Computing in cardiology, 2019. Cites: paper

Early Prediction of Sepsis Using Multi-Feature Fusion Based XGBoost Learning and Bayesian Optimization
Meicheng Yang, Xingyao Wang, Hongxiang Gao et al. Computing in cardiology, 2019. Cites: paper

Early Prediction of Sepsis Using Random Forest Classification for Imbalanced Clinical Data
Simon Lyra, Steffen K. Leonhardt, Christoph Hoog Antink. Computing in cardiology, 2019. Cites: paper

Early Prediction of Sepsis: Using State-of-the-art Machine Learning Techniques on Vital Sign Inputs
Manmay Nakhashi, Anoop Toffy, P.V. Achuth et al. Computing in cardiology, 2019. Cites: paper

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