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

Assessment of Machine Learning Methods to Predict Massive Blood Transfusion in Trauma
Matt Strickland, Anthony Nguyen, Shinyi Wu et al. World Journal of Surgery, 2023. Cites: paper

Desafios e Tendências na Predição de Sepse
Lourival G. Silva Jr., Rossana M. C. Andrade, Joaquim Celestino. 2023. Cites: paper

Learnable Path in Neural Controlled Differential Equations
Sheo Yon Jhin, Minju Jo, Seungji Kook et al. Proceedings of the AAAI Conference on Artificial Intelligence, 2023. Cites: paper

Machine Learning framework to predict sepsis disease using stacking ensemble algorithm
Laxmi Lydia, vijaya kumar, C. S. S. Anupama. Research Square, 2023. Cites: paper

A Time Series Clinical Data-driven Preprocessing Approach to Early Sepsis Diagnosis
Sadik Aref, Lachin Fernando, Sindhu Ghanta. IEEE Conference on Artificial Intelligence (CAI), 2023. Cites: paper

Regulatory role of the programmed cell death 1 signaling pathway in sepsis induced immunosuppression
Shubai Zhong, Yuanqin Yin. Frontiers in Immunology, 2023. Cites: paper

Early prediction of MODS interventions in the intensive care unit using machine learning
Chang Liu, Zhenjie Yao, Pengfei Liu et al. Journal Of Big Data, 2023. Cites: paper

Bringing the Promise of Artificial Intelligence to Critical Care: What the Experience With Sepsis Analytics Can Teach Us
Gabriel Wardi, Robert L. Owens, Christopher S. Josef et al. Critical Care Medicine, 2023. Cites: paper

New approaches to missing biomedical data recovery for machine learning
Victor Iapăscurtă, Ion Fiodorov. Journal of Engineering Science, 2023. Cites: paper

Combining C‐reactive protein and quick sequential organ failure assessment (qSOFA) to improve prognostic accuracy for sepsis and mortality in adult inpatients: A systematic review
Alexandra Zacharakis, Khalia Ackermann, Clifford F. Hughes et al. Health Science Reports, 2023. Cites: paper

Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning–Based Modeling Study
Jiang Li, Fengchan Xi, Wenkui Yu et al. JMIR Formative Research, 2023. Cites: paper

Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care
Suraj Rajendran, Zhenxing Xu, Weishen Pan et al. PLOS Digital Health, 2023. Cites: paper

Cross-Hospital Sepsis Early Detection via Semi-Supervised Optimal Transport With Self-Paced Ensemble
Ruiqing Ding, Yu Zhou, Xu Jie et al. IEEE Journal of Biomedical and Health Informatics, 2023. Cites: paper

Exploring a global interpretation mechanism for deep learning networks when predicting sepsis
Ethan A. T. Strickler, Joshua Thomas, Johnson P. Thomas et al. Scientific Reports, 2023. Cites: paper

Adaptive model training strategy for continuous classification of time series
Chenxi Sun, Hongyan Li, Moxian Song et al. Applied Intelligence, 2023. Cites: paper

Continuous diagnosis and prognosis by controlling the update process of deep neural networks
Chenxi Sun, Hongyan Li, Moxian Song et al. Patterns, 2023. Cites: paper

Deep Learning in Early Prediction of Sepsis and Diagnosis
Saroja Kumar Rout, Bibhuprasad Sahu, Ganesh Bhaiyya Regulwar et al. International Conference for Advancement in Technology (ICONAT), 2023. Cites: paper

Database-based machine learning in sepsis deserves attention
Wenhan Hu, Meicheng Yang, Hui Chen. Intensive Care Medicine, 2023. Cites: paper

Causal Graph Discovery From Self and Mutually Exciting Time Series
Song Wei, Yao Xie, Christopher S. Josef et al. IEEE Journal on Selected Areas in Information Theory, 2023. Cites: paper

Prediction and analysis of sepsis by using machine learning algorithms (Xg boost & light GBM)
Murugeshwari Adhiappan, Padmavathy Subramanian, Sivasankari Kannan et al. AIP conference proceedings, 2023. Cites: dataset

Supporting medical decision-making using machine learning
Richard Wainwright. Sheffield Hallam University, 2023. Cites: paper

Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study
Parker Rogers, Aaron E. Boussina, Supreeth Prajwal Shashikumar et al. Journal of Medical Internet Research, 2022. Cites: paper

Identifying high-risk phenotypes and associated harms of delayed time-to-antibiotics in patients with ICU onset sepsis: A retrospective cohort study
Wenhan Hu, Hui Chen, Haofei Wang et al. Journal of Critical Care, 2022. Cites: paper

Patient Health Representation Learning via Correlational Sparse Prior of Medical Features
Xinyu Ma, Yasha Wang, Xu Chu et al. IEEE Transactions on Knowledge and Data Engineering, 2022. Cites: paper

Features derived from blood pressure and intracranial pressure predict elevated intracranial pressure events in critically ill children
Kassi Ackerman, Akram Mohammed, Lokesh K. Chinthala et al. Scientific Reports, 2022. Cites: paper

Cloud-Based Sepsis Prediction System with Neural Architecture Search Service
Yen-Han Chiang, Hsinyu Lee, Yi-Wei Huang et al. International Conference on Computational Science and Computational Intelligence (CSCI), 2022. Cites: paper

Dealing With Missing Continuous Biomedical Data: a Data Recovery Method for Machine Learning Purposes
Victor Iapăscurtă. International Conference on Electronics, Communications and Computing (IC|ECCO), 2022. Cites: dataset

Confidence-Guided Learning Process for Continuous Classification of Time Series
Chenxi Sun, Moxian Song, Derun Cai et al. ACM International Conference on Information and Knowledge Management (CIKM), 2022. Cites: paper

Performance effectiveness of vital parameter combinations for early warning of sepsis—an exhaustive study using machine learning
Ekanath Srihari Rangan, Rahul Krishnan Pathinarupothi, Kanwaljeet J. S. Anand et al. JAMIA Open, 2022. Cites: paper

Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning
Trenton Chang, Michael W. Sjoding, Jenna Wiens. PubMed, 2022. Cites: paper

Integrating structured and unstructured data for timely prediction of bloodstream infection among children
Azade Tabaie, Evan William Orenstein, Swaminathan Kandaswamy et al. Pediatric Research, 2022. Cites: paper

Issues in the automated classification of multilead ecgs using heterogeneous labels and populations
Matthew A. Reyna, Nadi Sadr, Erick Andres Perez Alday et al. Physiological Measurement, 2022. Cites: paper

Rethinking Algorithm Performance Metrics for Artificial Intelligence in Diagnostic Medicine
Matthew A. Reyna, Elaine Okanyene Nsoesie, Gari D. Clifford. JAMA, 2022. Cites: paper

Variations among Electronic Health Record and Physiologic Streaming Vital Signs for Use in Predictive Algorithms in Pediatric Severe Sepsis
Adam C. Dziorny, Robert B. Lindell, Julie C. Fitzgerald et al. ACI Open, 2022. Cites: paper

Transformer-Based Unsupervised Learning for Early Detection of Sepsis (Student Abstract)
Yutao Dou, Wei Li, Albert Y. Zomaya. Proceedings of the AAAI Conference on Artificial Intelligence, 2022. Cites: paper

Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning–Based Approach
Donghun Yang, Jimin Kim, Junsang Yoo et al. JMIR Medical Informatics, 2022. Cites: paper

Risk assessment of acute respiratory failure requiring advanced respiratory support using machine learning
Angier O. Allen, Cecilia Zeng, Chak Foon Tso et al. Research Square, 2022. Cites: paper

Machine Learning Approach to Predicting Absence of Serious Bacterial Infection at PICU Admission
Blake Martin, Peter E. DeWitt, Halden F. Scott et al. Hospital Pediatrics, 2022. Cites: paper

Early prediction of moderate-to-severe condition of inhalation-induced acute respiratory distress syndrome via interpretable machine learning
Junwei Wu, Chao Liu, Lixin Xie et al. BMC Pulmonary Medicine, 2022. Cites: paper

Dynamic Sepsis Prediction for Intensive Care Unit Patients Using XGBoost-Based Model With Novel Time-Dependent Features
Shuhui Liu, Bo Fu, Wen Wang et al. IEEE Journal of Biomedical and Health Informatics, 2022. Cites: paper

Evaluation of Machine Learning Models for Clinical Prediction Problems*
Lazaro Nelson Sanchez-Pinto, Tellen D. Bennett. Pediatric Critical Care Medicine, 2022. Cites: paper

Performance Analysis of Class Imbalance Handling Techniques for Early Sepsis Prediction using Machine Learning Algorithms
Aparna Shenoy, Babu Rao. K. International Conference on Smart Structures and Systems (ICSSS), 2022. Cites: paper

A Machine Learning–Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction
Zeyu Liu, Anahita Khojandi, Xueping Li et al. INFORMS journal on computing, 2022. Cites: paper

Early Prediction of Sepsis Onset Using Neural Architecture Search Based on Genetic Algorithms
Jae Kwan Kim, Wonbin Ahn, Sangin Park et al. International Journal of Environmental Research and Public Health, 2022. Cites: paper

Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals
Yekanth Ram Chalumuri, Jacob P. Kimball, Azin Sadat Mousavi et al. Sensors, 2022. Cites: paper

Ground truth labels challenge the validity of sepsis consensus definitions in critical illness
Holger A. Lindner, Shigehiko Schamoni, Thomas Kirschning et al. Journal of Translational Medicine, 2022. Cites: paper

AI and Dynamic Prediction of Deterioration in Covid-19
Carolin Danker, Marat Murzabekov, David Forsberg et al. Springer eBooks, 2022. Cites: paper

A Temporal Convolutional Network with Attention for Sepsis Prediction: A Conceptual Framework for Analyzing High-Frequency Vital Signs in Intensive Care Units
Michael Turner, Sophia Nguyen, David Clark et al. Journal of Artificial Intelligence for Healthcare Systems, 2022. Cites: paper

Early Detection of Sepsis Using LSTM and Reinforcement Learning
R. Dhanalakshmi, T. Sudalaimuthu, K. R. Radhakrishnan. Lecture notes in mechanical engineering, 2022. Cites: paper

From Retrospective Models to Real-Time Sepsis Prediction: A Perspective on Continuous Vital Sign Monitoring and Edge AI–Enabled Clinical Decision Support
Hiroshi Tanaka, Yuki Sato, Kenji Mori et al. Journal of Artificial Intelligence for Healthcare Systems, 2022. Cites: paper

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