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

Irregularly Sampled Time Series Classification via Fusing Language Models and Multi‐Channel Image Representations
Zichen Li, Faming Lu, Zedong Lin et al. Concurrency and Computation Practice and Experience, 2026. Cites: paper

The Impact of Evaluation Strategy on Sepsis Prediction Model Performance Metrics in Intensive Care Data: Retrospective Cohort Study
Dang-Khoa Do, Patrick Rockenschaub, Sebastian Daniel Boie et al. Journal of Medical Internet Research, 2026. Cites: paper

DynaMamba: Multi-scale dynamic interacting Mamba network for irregular clinical time series classification
Hao Chen, Junjie Zhang, Xiaowei Yan et al. Journal of Biomedical Informatics, 2026. Cites: paper

Responsible AI for Sepsis Prediction: Bridging the Gap Between Machine Learning Performance and Clinical Trust
Thiago Queiroz de Oliveira, Leandro A. Carvalho, Flávio R. C. Sousa et al. Journal of Clinical Medicine, 2026. Cites: paper

Fine-grained dynamic graph learning for irregular multivariate time series
Jinquan Ji, Chunyi Hou, Yu Cao et al. Information Processing & Management, 2026. Cites: paper

Leakage-Aware Federated Learning for ICU Sepsis Early Warning: Fixed Alert-Rate Evaluation on PhysioNet/CinC 2019 and MIMIC-IV
Hyejin Jin, Hongchul Lee. Applied Sciences, 2026. Cites: paper

Evaluating the Predictive Potential of an AI-Driven Deep Learning Model for Pneumonia-Associated Sepsis
Ki-Byung Lee, Chang Youl Lee, Jaewon Jang et al. Journal of Clinical Medicine, 2026. Cites: paper

Artificial intelligence and robotic surgery in emergency gastrointestinal procedures: a review of current evidence and future directions
Rami Addasi, Wisam Al Safadi, Selina Sorour et al. Journal of Robotic Surgery, 2026. Cites: paper

Continuous Evaluation Frameworks for Retrospective Evaluation of Clinical Machine Learning Models
Annamalai Natarajan, Yale Chang, Brian P. McLaverty et al. Critical Care, 2026. Cites: paper

Conformal selective prediction with cost aware deferral for safe clinical triage under distribution shift
Hyun Kwon, Dae-Jin Kim. Scientific Reports, 2026. Cites: paper

Interpretable machine learning-based real-time sepsis diagnosis
Fahim Mahmud, Muhammad Quamruzzaman, Abdurrashid Ibrahim Sanka et al. Scientific Reports, 2026. Cites: paper

HealthProcessAI: a technical framework and proof-of-concept for LLM-enhanced healthcare process mining
Eduardo Illueca Fernández, K.C. Chen, Fernando Seoane et al. Frontiers in Artificial Intelligence, 2026. Cites: paper

Optimizing ED patient disposition predictions through clinical narratives with advanced pre-trained language models
Mei-Hui Lee, Ting-Yun Huang, Pei-Ying Yang et al. Health Information Science and Systems, 2026. Cites: paper

A gradual coarse-to-fine framework for irregularly sampled multivariate time series analysis
Jiexi Liu, Meng Cao, Songcan Chen. Science China Information Sciences, 2026. Cites: paper

Machine Learning Model for Sepsis Prediction in Prolonged and Chronic Critical Illness: Development and Validation Using Retrospective Real-World ICU Data
Mikhail Ya. Yadgarov, O. Yu. Rebrova, Берикашвили Леван Бондоевич et al. Journal of Clinical Medicine, 2026. Cites: paper

ReciPies: A Lightweight Data Transformation Pipeline for Reproducible ML
Robin P. van de Water, Hendrik Schmidt, Patrick Rockenschaub. The Journal of Open Source Software, 2026. Cites: paper

A Canonical Signature-Based Feature Set for Multivariate Time Series Classification
James Morrill, Adeline Fermanian, Patrick Kidger et al. Springer eBooks, 2026. Cites: paper

A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data
Chenxi Sun, Moxian Song, Derun Cai et al. Health Data Science, 2026. Cites: paper

Candlestick Is All You Need for Temporal Recognition in Finance
Anh Q. Nguyen, Tuan Anh Truong, Son Xuan Ha et al. SSRN Electronic Journal, 2026. Cites: paper

Early Prediction of Sepsis: A Comprehensive Analysis of Machine Learning and Deep Learning Approaches
Shraddha Kumar, Pooja Sabherwal, Mona Aggarwal. Lecture notes in electrical engineering, 2026. Cites: dataset

Edge-Deployed Clinical Monitoring and Intrusion Detection Secured by Hyperledger Blockchain Decentralized Storage in Medical IoT
Reema Shaheen, Adnan Shahid Khan, Dr Asiya Aman et al. International Journal of Artificial Intelligence and Machine Learning, 2026. Cites: paper

Federated Learning-Based XGBoost Framework for Privacy-Preserving Sepsis Prediction Using EHRs
Jayalaxmi Dash, Pratik Kumar Swain, Rabi Narayan Satpathy et al. Lecture notes in networks and systems, 2026. Cites: dataset

Lightweight, Explainable, and Efficiency-Aware Deep Learning for Early Sepsis Prediction from Structured Electronic Health Record Time-Series
Attaphongse Taparugssanagorn, Mariella Särestöniemi, Matti Hämäläinen et al. Communications in computer and information science, 2026. Cites: paper

Machine Learning for Early Sepsis Detection and Mortality Prediction Across Emergency and Critical Care: A Systematic Review and Meta-Analysis
Muhammad Garba, Ved Prakash Sharma. SSRN Electronic Journal, 2026. Cites: paper

Machine Learning for Predicting Sepsis in Hospitalized Patients: A Systematic Review of Model Types, Feature Engineering Approaches, Prediction Horizons, and Prospective Validation Studies
Ahmed Al-Sayed, Omar Khalid. Journal of Artificial Intelligence for Healthcare Systems, 2026. Cites: paper

MS-HNODE: Time-Aware Multi-branch Neural ODEs for Heterogeneous Latent Dynamics in Irregularly Sampled Multivariate Time Series
Xianan Wu, Zongyao Yin, Dan Hu et al. SSRN Electronic Journal, 2026. Cites: paper

Sepsis Prediction
Srinivas Kilambi, Mahesh K. Banavar. Synthesis lectures on technology, management, and entrepreneurship, 2026. Cites: paper

Towards Generalizable and Explainable Early Sepsis Prediction: A Hybrid Approach Using Multi-center EHR Data and Advanced Feature Engineering
Nandibhatla Haritha, V. Lakshmi Chetana. Smart innovation, systems and technologies, 2026. Cites: paper

Transforming Healthcare with Electronic Health Records: AI Integration, Evolution, and Future
Priyanka Chakraborty, Sabyasachi Kamila. Springer optimization and its applications, 2026. Cites: paper

Deep Spatiotemporal Point Processes: Advances and New Directions
Xiuyuan Cheng, Zheng Dong, Yao Xie. Annual Review of Statistics and Its Application, 2025. Cites: paper

Beyond Binary Alerts: A Multi-Faceted Framework for Clinically Actionable Sepsis Prediction
Fariha Jahan, Nusrat Hossain, Rashedul Arefin Ifty. International Conference on Computer and Information Technology (ICCIT), 2025. Cites: paper

DAWN: Dual-Axial Warped Networks for Sepsis Early Prediction
Chuanyi Jiao, Ying Yu, Chunping Ouyang et al. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2025. Cites: paper

FUSE-Sepsis: A Multi-Modal Fusion Framework for Early Sepsis Prediction Using LLM Fine-Tuning and Tabular Learning (Preprint)
Lijuan Jia, Junjie Zhu, Fanrui Yang. 2025. Cites: paper

Non-temporal tree-based models outperform temporal deep learning models in the prediction of chemotherapy-induced side effects from longitudinal laboratory data
Farnaz Rahimi, Christel Sirocchi, Julian Matschinske et al. medRxiv, 2025. Cites: paper

Robust Prototype-Driven Patient Representation Enhancement for Disease Prediction
Hongxu Yuan, Xiaozhu Jing, Yuzheng Yan et al. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2025. Cites: paper

WaveGNN: Integrating Graph Neural Networks and Transformers for Decay-Aware Classification of Irregular Clinical Time-Series
Arash Hajisafi, Maria Despoina Siampou, Bita Azarijoo et al. IEEE International Conference on Big Data (BigData), 2025. Cites: paper

Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Patients: A Systematic Review and Meta-Analysis
Ghulam Husain Abbas, Palash Sen, Oviya Anjali Giri et al. Critical Care Explorations, 2025. Cites: paper

EHR Based Patient’s Severity Prediction using Machine Learning
Faria Nourin, Barnabas Prentice, Rifat Al Mamun Rudro et al. IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), 2025. Cites: paper

A Graph-Based Approach for Early and Explainable Health Risk Assessment
Sonal Jha, Wu-chun Feng. IEEE International Conference on Bioinformatics and Bioengineering (BIBE), 2025. Cites: paper

HET-VQVAE: a novel encoder-decoder framework for irregularly-sampled multivariate time series of patients
Zhixuan Zeng, Minjie Lin, Yang Liu et al. Journal Of Big Data, 2025. Cites: paper

Dynamic Memory-Aware Causal Subgraph Learning for Irregular Clinical Time Series
Jinquan Ji, Yu Cao, Chunyi Hou et al. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2025. Cites: paper

An Interpretable Machine Learning Model for Early Multitemporal Prediction of Onset of Acute Kidney Injury in Intensive Care Unit Patients with Severe Trauma
Bingrui Gao, Hongliang Jin, Yan Zhang et al. Journal of Intensive Care Medicine, 2025. Cites: paper

Early Sepsis Prediction Using Publicly Available Data: High-Performance AI/ML Models with First-Hour Clinical Information
Hao Wang, Destiny Pounds, Wenhui Zhang et al. Diagnostics, 2025. Cites: paper

Time-Critical Sepsis Prediction: Advancing Healthcare Analytics Through Deep Learning Architectures
Jeevithesh Reddy Narravula Reddy, Dedeepya Sai Gondi, Pavan Kumar Reddy Yellela et al. Lecture notes in networks and systems, 2025. Cites: paper

Stacked Model Framework for Accurate and Early Sepsis Detection Using Ensemble Learning
Pavithra Sekar, Aryan Kapoor, Swapnil Sunil Baviskar et al. Communications in computer and information science, 2025. Cites: dataset

Artificial Intelligence in Infectious Disease Diagnostic Technologies
Chao Dong, Yujing Liu, Jiaqi Nie et al. Diagnostics, 2025. Cites: paper

Imputation Matters: Evaluating the Impact of Missing Data Strategies on Interpretability in Clinical Time Series Models
Malte Singerhoff, Torben Weis. ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2025. Cites: dataset, paper

DeepCEF: A Deep Causal Estimation Framework for Complex Biological Systems Integrating Local Scores, Independence Tests, and Relation Attributes
Zhenjiang Fan, Mengrui Zhang, Summer S. Han. bioRxiv (Cold Spring Harbor Laboratory), 2025. Cites: paper

Early prediction of sepsis in intensive care units: A comparative analysis based on optimization techniques and committees
André Luiz Vale de Araújo, Flávio Secco Fonseca, Arianne Sarmento Torcate et al. Research on Biomedical Engineering, 2025. Cites: paper

AI-driven transcriptomic biomarker discovery for early identification of pediatric deterioration in Acute Care
Qing Wang, Lina Sun, Meng Wei et al. SLAS TECHNOLOGY, 2025. Cites: paper

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