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

SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing
Changchang Yin, Pin-Yu Chen, Bingsheng Yao et al. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024. Cites: paper

Improving sepsis prediction in intensive care with SepsisAI: A clinical decision support system with a focus on minimizing false alarms
Ankit Gupta, Ruchi Chauhan, Saravanan G et al. PLOS Digital Health, 2024. Cites: paper

Self-attention with temporal prior: can we learn more from the arrow of time?
Kyung Geun Kim, Byeong Tak Lee. Frontiers in Artificial Intelligence, 2024. Cites: paper

Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach
Russell Jeter, Raymond Greenfield, Stephen Nick Housley et al. JMIR Biomedical Engineering, 2024. Cites: paper

Eigen-entropy based time series signatures to support multivariate time series classification
Abhidnya Patharkar, Jiajing Huang, Teresa Wu et al. Scientific Reports, 2024. Cites: paper

OEHR: An Orthopedic Electronic Health Record Dataset
Yibo Xie, Kaifan Wang, Jiawei Zheng et al. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2024. Cites: paper

The Impact of Multi-Institution Datasets on the Generalizability of Machine Learning Prediction Models in the ICU
Patrick Rockenschaub, Adam Hilbert, Tabea Kossen et al. Critical Care Medicine, 2024. Cites: paper

Study of CD64 and HLA-DR as Early Detection Markers of Sepsis in Hepatic ICU Patients
Maha Elsabaawy, Gasser El-Azab, Fatma Omar Khalil et al. Afro-Egyptian Journal of Infectious and Endemic Diseases, 2024. Cites: paper

TEE4EHR: Transformer event encoder for better representation learning in electronic health records
Hojjat Karami, David Atienza, Anisoara Paraschiv-Ionescu. Artificial Intelligence in Medicine, 2024. Cites: paper

An attention-based bidirectional LSTM-CNN architecture for the early prediction of sepsis
Pronaya Prosun Das, Lena Wiese, Marcel Mast et al. International Journal of Data Science and Analytics, 2024. Cites: paper

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis
Kullaya Takkavatakarn, Wonsuk Oh, Lili Chan et al. Critical Care, 2024. Cites: paper

Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications
G Anjana, K L Nisha, Arun Sankar M.S. Journal of Critical Care, 2024. Cites: paper

Evaluating Explanations From AI Algorithms for Clinical Decision-Making: A Social Science-Based Approach
Suparna Ghanvatkar, Vaibhav Rajan. IEEE Journal of Biomedical and Health Informatics, 2024. Cites: paper

Development and validation of a machine-learning model for predicting the risk of death in sepsis patients with acute kidney injury
Lei Dong, Pei Liu, Zhili Qi et al. Heliyon, 2024. Cites: paper

Use of artificial intelligence in critical care: opportunities and obstacles
Michael R. Pinsky, Armando Bedoya, Azra Bihorac et al. Critical Care, 2024. Cites: paper

Early identification of suspected serious infection among patients afebrile at initial presentation using neural network models and natural language processing: A development and external validation study in the emergency department
Dong Hyun Choi, Sae Won Choi, Ki Hong Kim et al. The American Journal of Emergency Medicine, 2024. Cites: paper

A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care
Junyi Gao, Yinghao Zhu, Wenqing Wang et al. Patterns, 2024. Cites: paper

Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers
Guyu Zhang, Fei Shao, Yuan Wei et al. European journal of medical research, 2024. Cites: paper

Development and Validation of the VIOSync Sepsis Prediction Index: A Novel Machine Learning Model for Sepsis Prediction in ICU Patients
Sotirios G. Liliopoulos, Alexander Dejaco, Vasileios S. Dimakopoulos et al. medRxiv, 2024. Cites: paper

A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities
Alvaro G. Moreira, Ameena N. Husain, Lindsey A. Knake et al. Frontiers in Pediatrics, 2024. Cites: paper

Impact of a deep learning sepsis prediction model on quality of care and survival
Aaron E. Boussina, Supreeth Prajwal Shashikumar, Atul Malhotra et al. npj Digital Medicine, 2024. Cites: paper

Subtle variation in sepsis-III definitions markedly influences predictive performance within and across methods
Samuel N. Cohen, James Foster, Peter Foster et al. Scientific Reports, 2024. Cites: paper

Corticosteroid sensitivity detection in sepsis patients using a personalized data mining approach: A clinical investigation
Rahma Hellali, Zaineb Chelly Dagdia, Ahmed Ktaish et al. Computer Methods and Programs in Biomedicine, 2024. Cites: paper

Early Detection of Sepsis Utilizing Self-guided Generative Adversarial Network
Pei-Yi Hao. Lecture notes in networks and systems, 2024. Cites: paper

Enhancing Septic Shock Detection through Interpretable Machine Learning
Md Mahfuzur Rahman, Md. Solaiman Chowdhury, Mohammad Shorfuzzaman et al. Computer Modeling in Engineering & Sciences, 2024. Cites: paper

Estimation of Kolmogorov-Chaitin Complexity on Continuous Biomedical Data for Machine Learning Purposes
Victor Iapăscurtă. Lecture notes in networks and systems, 2024. Cites: dataset

Machine Learning Based Disease Classification and Prediction
N. Sandhya, Pushkara Annaldas, Sai Aashritha Kalakancheri et al. Studies in systems, decision and control, 2024. Cites: paper

Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment
Sapiah Binti Sakri, Shakila Basheer, Zuhaira Muhammad Zain et al. Computers, materials & continua/Computers, materials & continua (Print), 2024. Cites: paper

Time Series Classification With Large Language Models via Linguistic Scaffolding
Hyeongwon Jang, June Yong Yang, Jaeryong Hwang et al. IEEE Access, 2024. Cites: paper

Validity
Stefan Riezler, Michael Hagmann. Synthesis lectures on human language technologies, 2024. Cites: paper

An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals
Weishen Pan, Zhenxing Xu, Suraj Rajendran et al. Patterns, 2023. Cites: paper

Unleashing the power of explainable AI: sepsis sentinel's clinical assistant for early sepsis identification
Snehashis Chakraborty, Komal Kumar, Kalyan Tadepalli et al. Multimedia Tools and Applications, 2023. Cites: paper

Comparing artificial intelligence strategies for early sepsis detection in the ICU: an experimental study
Javier Solís-García, Belén Vega-Márquez, Juan A. Nepomuceno et al. Applied Intelligence, 2023. Cites: paper

Second-order Confidence Network for Early Classification of Time Series
Junwei Lv, Yuqi Chu, Jun Hu et al. ACM Transactions on Intelligent Systems and Technology, 2023. Cites: paper

Time pattern reconstruction for classification of irregularly sampled time series
Chenxi Sun, Hongyan Li, Moxian Song et al. Pattern Recognition, 2023. Cites: paper

Point-process-based Representation Learning for Electronic Health Records
Hojjat Karami, Anisoara Paraschiv-Ionescu, David Atienza. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...), 2023. Cites: paper

Early Prediction of Sepsis Using Time Series Forecasting
Jinghua Xu, Natalia Minakova, Pablo Ortega Sanchez et al. IEEE International Conference on eScience, 2023. Cites: paper

A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit
Jinhu Zhuang, Haofan Huang, Song Jiang et al. BMC Medical Informatics and Decision Making, 2023. Cites: paper

Heart murmur detection from phonocardiogram recordings: The George B. Moody PhysioNet Challenge 2022
Matthew A. Reyna, Yashar Kiarashi, Andoni Elola et al. PLOS Digital Health, 2023. Cites: paper

Feature augmentation and semi-supervised conditional transfer learning for early detection of sepsis
Yutao Dou, Wei Li, Yucen Nan et al. Computers in Biology and Medicine, 2023. Cites: paper

Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review
Khandaker Reajul Islam, Johayra Prithula, Jaya Kumar et al. Journal of Clinical Medicine, 2023. Cites: paper

Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review
Sarah A Pungitore, Vignesh Subbian. Journal of Healthcare Informatics Research, 2023. Cites: paper

Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes
Song Wei, Yao Xie, Christopher S. Josef et al. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023. Cites: paper

An Explainable AI based Clinical Assistance Model for Identifying Patients with the Onset of Sepsis
Snehashis Chakraborty, Komal Kumar, Balakrishna Reddy Pailla et al. IEEE International Conference on Information Reuse and Integration (IRI), 2023. Cites: paper

Predicting sepsis using deep learning across international sites: a retrospective development and validation study
Michael Moor, Nicolas Bennett, Drago Plečko et al. EClinicalMedicine, 2023. Cites: paper

Identification of indications for albumin administration in septic patients with liver cirrhosis
Wenhan Hu, Hui Chen, Chencheng Ma et al. Critical Care, 2023. Cites: paper

Early Prediction of Neonatal Sepsis From Synthetic Clinical Data Using Machine Learning
Simon Lyra, Jinyi Jin, Steffen K. Leonhardt et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2023. Cites: paper

Predicting Hospital Readmission among Patients with Sepsis Using Clinical and Wearable Data
Fatemeh Amrollahi, Supreeth Prajwal Shashikumar, Aaron E. Boussina et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2023. Cites: paper

Analysis of machine learning and deep learning prediction models for sepsis and neonatal sepsis: A systematic review
Safiya Parvin A., B. Saleena. ICT Express, 2023. Cites: paper

Remote Medication Status Prediction for Individuals with Parkinson’s Disease using Time-series Data from Smartphones
Weijian Li, Wei Zhu, Earl Ray Dorsey et al. IEEE International Conference on Digital Health (ICDH), 2023. Cites: paper

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