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

Event-driven transformer for sepsis early warning: a novel deep learning architecture for sparse clinical time series analysis
Yassin Ben Youssef, Asma Amdouni, Ghaith Manita et al. Network Modeling Analysis in Health Informatics and Bioinformatics, 2026. Cites: paper

PREDIÇÃO PRECOCE DE SEPSE EM UNIDADES DE TERAPIA INTENSIVA A PARTIR DE SÉRIES TEMPORAIS CLÍNICAS: ANÁLISE COMPARATIVA DE MODELOS DE APRENDIZADO DE MÁQUINA NO MIMIC-IV
Fábio Fogliarini Brolesi, Helio Pedrini. Brazilian Journal of Health Review, 2026. Cites: paper

AEGIS: an operational infrastructure for post-market governance of adaptive medical AI under US and EU regulatory frameworks
Fardin Afdideh, Mehdi Astaraki, Fernando Seoane et al. npj Digital Medicine, 2026. Cites: paper

DEVELOPMENT AND PERFORMANCE EVALUATION OF MACHINE LEARNING MODELS FOR EARLY SEPSIS PREDICTION
Adish Imtiaz, Sajid Imtiaz. Veredas do Direito Direito Ambiental e Desenvolvimento Sustentável, 2026. Cites: paper

"Interpretable Machine Learning Framework for Early Sepsis Detection in Intensive Care Units Using SHAP and LIME: A Comparative Model Development and Explainability Study" (Preprint)
Abiha Ayub. 2026. Cites: paper

A continuous-time predictive analytics framework for quantifying information degradation in intensive care monitoring systems
Mahesha Herath, Martin Andersen. Healthcare Analytics, 2026. Cites: paper

Design Thinking for Healthcare
Daniel Steenstra. Productivity Press eBooks, 2026. Cites: paper

ITCD: implicit trajectory-constrained diffusion for consistent time series imputation
S.-L. Ma, Ziwei Xu, Ryutaro Ichise. Applied Intelligence, 2026. Cites: paper

Intelligence Preserves The System It Assists
M. Tony Ewing. Research Square, 2026. Cites: paper

Implementation Variability Makes Sepsis-3 an Inconsistent Measuring Model for Early AI Prediction on Clinical Data
Jan Mackensen. Research Square, 2026. Cites: paper

From PhysioNet to foundation models—a history and potential futures
Gari D. Clifford. Physiological Measurement, 2026. Cites: paper

Toward explainable clinical decision support for irregular and incomplete ICU EHRs: An observation-aware multi-level attention model
Kaibo Zhu, Xuan Wei, Xiuxian Wang et al. Decision Support Systems, 2026. Cites: paper

Privacy-Preserving Framework for Multi-Institutional Medical Time-Series Analysis via Homomorphic Encryption: Design and Development Study
Yao Lu, Yu Tian, Tianshu Zhou et al. JMIR Formative Research, 2026. Cites: dataset, paper

MACHINE LEARNING IN PREDICTION OF SEPSIS IN INTENSIVE CARE UNITS: A LITERATURE REVIEW OF ALGORITHMIC MODELS AND CLINICAL IMPLEMENTATION
Michał Parzniewski, Natalia Ostruszka, Maria Marusińska et al. International Journal of Innovative Technologies in Social Science, 2026. Cites: paper

Real-time clinical decision support system for early identification of infection and sepsis in the intensive care unit: a retrospective development and prospective deployment study
Fang‐Ju Sun, Yen‐Yu Liu, Li‐Kuo Kuo et al. BMJ Quality & Safety, 2026. Cites: paper

Cognitive Attention-Based Temporal Network for Multimodal Anomaly Detection in Wearable Healthcare Sensor Monitoring Applications
Bharathraj Kumar. M, Avinash. S, G. P. Hegde et al. International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), 2026. Cites: paper

Hierarchical GNN-Driven Edge-Cloud Partitioning for Real-Time Federated Healthcare Analytics: A Dynamic Computation Splitting Framework
Arushi Singh, Anubha Gaur, Zainab et al. International Conference on Integrated Circuits, Communication, and Computing Systems (ICIC3S), 2026. Cites: paper

Explainable deep learning for early sepsis detection from ICU time-series data using XAI techniques
Anas Mahmoud, Hamza Abdelmoreed, Hossam Amir et al. Scientific Reports, 2026. Cites: paper

Evaluating Large Language Models Against Clinical Assessment Frameworks for Early Sepsis Detection in the ICU
Anvit More, Vishala Bodetti, Kishan Gor et al. International Journal for Research in Applied Science and Engineering Technology, 2026. Cites: paper

CausalGuard: A Counterfactual-Aware AI Framework for Robust Clinical Decision Support in Acute General Medicine
Idowu Paul Okuwobi, Jingyuan Liu. Artificial intelligence, 2026. Cites: paper

An Irregular Time-Aware Deep Learning Pipeline for Early Sepsis Prediction in Sparse ICU Trajectories
Annette Nayana Nellyet, Manpreet Kaur, Navdeep Kaur. Research Square, 2026. Cites: paper

Chance-corrected utility-weighted metrics for binary classification and information retrieval: A conceptual and analytical framework
Wen‐Chung Lee. Applied Soft Computing, 2026. Cites: paper

DeepTemporal-Sepsis: A Calibrated Transformer Framework for Early ICU Sepsis Prediction with Grad-CAM Temporal Phenotyping
Md Shahnawaj, Hamim Islam Hellol, Md. Rakibul Hasan et al. BioMedInformatics, 2026. Cites: paper

TNAM: Temporal neural additive models for multivariate clinical time-series
Da Huang, Tao Tan, Yue Sun. Information Fusion, 2026. Cites: paper

A Reproducible Clinical Decision-Support Suite on MIMIC-IV
Jayakanth S Kesan. medRxiv, 2026. Cites: paper

Comparative Analysis of Liquid Neural Networks and LSTM for Sequential Pattern Recognition: Robustness, Efficiency, and Clinical Utility
Ye Kyaw Thu, Thazin Myint Oo, Thepchai Supnithi. International Joint Conference on Computer Science and Software Engineering, 2026. Cites: paper

Online Sepsis Prediction Using Vital Signs and Multiscale Temporal-Aware Contrastive Learning: Model Development and Validation Study
Xiaoqiong Yang, Zezhong Lv, Hanming Lv et al. JMIR Medical Informatics, 2026. Cites: paper

From development to clinical practice: deployment of an interoperable and secure ML-based CDSS to aid in the early detection of sepsis
Ana Serrano García, David J. López, Eric Macias-Fassio et al. International Journal of Medical Informatics, 2026. Cites: paper

Hyper-inflammation and immunosuppression: redefining sepsis therapy using modern approaches
Mili Prajapati, Vaishnavi Singh, Akash Mishra et al. Frontiers in Pharmacology, 2026. Cites: paper

Multimodal Early Sepsis Prediction using Aligned Transformer Representations and Gradient Boosting
Anoushka Ruikar, Aditya Vivekanand, Reegan Pinto et al. Research Square, 2026. Cites: paper

Early Sepsis Detection Using Heterogeneous Structured ICU Data with Explainable Deep Learning
Attaphongse Taparugssanagorn, Mariella Särestöniemi, Matti Hämäläinen et al. Sensors, 2026. Cites: paper

DECISION-AWARE TWO-STAGE CLASSIFICATION FOR EARLY SEPSIS PREDICTION IN IMBALANCED ICU DATA
Ali Aslan, Emin DEMİR. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 2026. Cites: paper

Interpretable Machine Learning for Early Sepsis Prediction
Charithea Stylianides, Andria Nicolaou, Anna Vavlitou et al. Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2026. Cites: paper

TD-Hyper: Time-Density Guided Hypergraph Learning for Clinical Irregular Multivariate Time Series
Guohui Ding, Zejun Zhang, Jinwei Wang. International Joint Conference on Neural Networks (IJCNN), 2026. Cites: paper

Temporal Knowledge Extraction Through BayeStack with Multi-Level Explainability for Optimal Sepsis Classification
Anjana Geetha, K S Nisha, M. S. Arun Sankar et al. Machine Learning and Knowledge Extraction, 2026. Cites: paper

An Interpretable and Uncertainty-Aware Deep Learning Framework for Early Sepsis Prediction Using SHAP-Enhanced Attention and Continuous-Time Neural Networks
Rekha R. Nair, Tina Babu, Balamurugan Balusamy et al. Machine Learning and Knowledge Extraction, 2026. Cites: paper

Early Sepsis Prediction in ICU Patients Using Deep Learning: A Comparative Modeling Study of Transformer, TAGF, CNN–BiLSTM, and XGBoost
Sania Parveen¹, Sanjana Sanjana¹, Sakshi Sakshi¹ et al. Research Square, 2026. Cites: paper

From Data to Diagnosis: A Machine Learning-Enabled Framework for Early Sepsis Prediction and Prevention
Hassan Harb. Information, 2026. Cites: paper

Blood pressure response index trajectories identify distinct hemodynamic phenotypes and predict mortality in septic shock: a two-database retrospective cohort study
Yongshi Shen, W M Zhang, Kangni Lin et al. Critical Care, 2026. Cites: paper

A Hybrid Deep Learning Model Combining Tabular Transformers and Temporal Convolutional Network for Sepsis Prediction
Sona G, Anitha D., Narmatha B. Journal of Innovative Image Processing, 2026. Cites: paper

Lightweight large language models for early sepsis prediction via a semantic abstraction rule engine
Yang Yang, Bin Yi, Yuwen Chen. Scientific Reports, 2026. Cites: paper

VP-GNN: A Unified Graph Framework for Variable-Wise and Patch-Wise Modeling of Irregular Clinical Time Series
Yurong He, Boya Zhang, Longfei Liu et al. IEEE International Conference on Acoustics Speech and Signal Processing, 2026. Cites: paper

Stress Hyperglycemia Ratio and the Risk of Sepsis in Patients With Heart Failure: Retrospective Cohort Study From Medical Information Mart for Intensive Care-IV
Xianzhen Cai, Baoxin Yan, Jinhao Chen et al. JMIR Medical Informatics, 2026. Cites: paper

6-Hour Early Sepsis Prediction Using XGBoost with ADASYN: Cross-Hospital Validation on PhysioNet 2019
M D Sayedur Rahman, Tasmiah Binte Azad, Md Nahid Hasan et al. IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN), 2026. Cites: paper

Interpretable machine learning for early prediction of sepsis-induced coagulopathy: a multicenter retrospective development and validation study
Qingyun Peng, Yanzi Guo, Haoyuan Tang et al. BMC Medical Informatics and Decision Making, 2026. Cites: paper

Machine learning for early detection and prediction of sepsis: explainability and key sepsis biomarkers representation—A systematic review
Ioannis Papapanagiotou, Apostolos Karalis, Stelios Kokkoris et al. International Journal of Medical Informatics, 2026. Cites: paper

Engineering a Patient-Level Cost-Sensitive Framework for Temporal Machine Learning in Early Sepsis Detection
Marcos Luiz Cliton Bezerra, Joaquim Miguel Maia, Fábio de Araújo Motta. Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), 2026. Cites: paper

Machine Learning Algorithms for the Early Identification of Sepsis in Geriatric Post-Surgical Patients: A Bibliographic Review Integrating Internal Medicine and General Surgery Perspectives
Juan Carlos Lema Balla, Esther Salomé Loyola Cevallos, Evelyn Lisseth Alvarracin Vinueza et al. ASCE MAGAZINE, 2026. Cites: paper

Machine learning predicts sepsis deterioration trajectories
FAN Rui, ZHANG Yining, LI Xin, LI Juan, LI Qun, LI Zhiying, GU Shili, HU Feihan, GAO Shiyang, FENG Biyun, YAO Ruen, WANG Xiumin, Fang Long, Zhanqi Zhao et al. npj Digital Medicine, 2026. Cites: paper

Explainable Machine Learning for Multi-Horizon Early Sepsis Prediction in Intensive Care Units
Yetunde Esther Ogunwale, Johnson Tunde Fakoya, Oluyemisi Adenike Oyedemi et al. Cureus Journal of Computer Science., 2026. Cites: paper

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