Sepsis Prediction

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11 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Alert-List Concordance for Evaluating Imputation Strategies in ICU Sepsis Prediction
Oct 15, 2026Priyanka Siddappa, Muntaqim Ahmed Raju, Shifat Haider Al Amin +1Incomplete Data Imputation Clinical Risk Prediction
Miner School of Computer & Information Sciences, University of Massachusetts Lowell, Lowell, MA 01854, USA
SepsisLens: Structure-Preserving Sequence Modelling for Decomposable Early Sepsis Warning
Oct 6, 2026Yikun Ou, Wei LiSepsis Prediction Interpretable ML
School of Computer Science, University of Sydney Sydney, Australia
MTLiquid: Enabling Efficient Multi-Task Learning using Liquid Neural Networks for Lightweight Healthcare Monitoring Systems
Sep 27, 2026Rachmad Vidya Wicaksana Putra, Fahad Abdul Rauf, Muhammad ShafiqueSepsis Prediction Multi-Task Learning
eBRAIN Lab, New York University (NYU) Abu Dhabi, Abu Dhabi, United Arab Emirates
Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study
Aug 27, 2026Kevin Zhu, Ryan Zhang, Baraa Abed +18Sepsis Prediction Clinical Prediction
Department of Biomedical Engineering, Duke University, Durham, NC. · Department of Surgery, Duke University School of Medicine, Durham, NC. · Department of Electrical and Computer Engineering, Duke University, Durham, NC. +9
A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning
Jul 18, 2026Umair bin Mansoor, Munaf Rashid, Roomi NaqviSepsis Prediction Self-Supervised Pre-Training
Electrical Engg. Department, DHA Suffa University, Karachi, Pakistan · Salim Habib University, Karachi, Sindh, Pakistan · Intel Foundry Services, Intel, Tempe, Arizona, United States
Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving
Jun 3, 2026Xixi Tian, Di Wu, Xiang Liu +4Sepsis Prediction Privacy-Preserving ML
College of Computer and Information Science Southwest University Chongqing, China · Department of Anesthesiology Southwest Hospital Chongqing, China
SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection
May 21, 2026Santiago Ospitia, John Sanabria, John Garcia-HenaoSepsis Prediction
School of Systems Engineering and Computing, University of Valle, Cali, Colombia · Digital Medicine Unit, Balgrist University Hospital, Zurich, Switzerland · Nucleus-AI Research, Medellin, Colombia
Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics
Apr 22, 2026Weizhi Nie, Zhen Qu, Weijie Wang +4Interpretable ML LLM-Based Time Series Forecasting
School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, Tianjin, China · Department of Information Engineering and Computer Science, University of Trento, Trento, Trentino, Italy · Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, Jiangsu, China +3
CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction
Apr 16, 2026Honglin Guo, Rihao Chang, He Jiao +3Sepsis Prediction Clinical Prediction
Tianjin University, Tianjin 300072, China · Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China · Tianjin Medical University General Hospital, Tianjin 300052, China
Explainable Machine Learning for Sepsis Outcome Prediction Using a Novel Romanian Electronic Health Record Dataset
Apr 6, 2026Andrei-Alexandru Bunea, Ovidiu Ghibea, Dan-Matei Popovici +2Sepsis Prediction Clinical Outcome Prediction
POLITEHNICA Bucharest National University of Science and Technology, Bucharest, Romania · Carol Davila University of Medicine and Pharmacy, Faculty of Medicine, Bucharest, Romania · General Surgery Department, University Emergency Hospital of Bucharest, Bucharest, Romania +1
A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs
Mar 6, 2026Yue Chang, Guangsen Lin, Jyun Jie Chuang +3Sepsis Prediction Clinical Prediction
Chengdu Medical College, Chengdu, Sichuan 610500, China · Kunming Medical University, Kunming, Yunnan 650500, China · National Yang Ming Chiao Tung University, Taipei, Taiwan 900, China +2

Oct 15, 2026J/K move · Enter open · S save

Alert-List Concordance for Evaluating Imputation Strategies in ICU Sepsis Prediction

Priyanka Siddappa, Muntaqim Ahmed Raju, Shifat Haider Al Amin, Ruizhe Ma

Miner School of Computer & Information Sciences, University of Massachusetts Lowell, Lowell, MA 01854, USA

Incomplete Data Imputation Clinical Risk Prediction Time Series Imputation Sepsis Prediction

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Preprocessing is an under-measured reliability risk in clinical prediction: a model can keep a similar AUROC and still change who receives an alert. Imputation quality is commonly evaluated using reconstruction error or aggregate discrimination, neither of which directly measures agreement on which patients are flagged. At a matched alert rate, pipelines with similar aggregate performance can produce different patient lists. This question is especially relevant under the frequent, structured missingness of real ICU records. We therefore introduce alert-list concordance, which asks whether two pipelines identify the same high-risk patients, measured by the Jaccard similarity of their top-k% alert sets with patient-level bootstrap confidence intervals. On PhysioNet/CinC 2019 (40,336 patients, 71.9% missingness), we compare nine imputation strategies and a native-missingness baseline using a fixed XGBoost configuration, fitted and Platt-calibrated separately for each strategy. After Bonferroni correction, 22 of the 28 reconstruction-method pairs showed no statistically significant difference in patient-level AUROC, yet their alert lists overlap at Jaccard 0.518 to 0.750. Similar aggregate discrimination can therefore accompany different patient selections. ForwardFill is a separate outlier, pairing higher discrimination with the lowest overlap in the matrix, 0.40 to 0.45. The broad pattern holds within each hospital system and under six patient-level aggregation rules. At the row level, eight of ten methods fall within an AUROC range of 0.011. In this row-level pipeline, deep imputers did not outperform mean imputation on discrimination, and the sequential implementations cost 5 to 12 times mean imputation in total pipeline wall-clock time on our hardware.