ArticleScientific reports2024
Continuous sepsis trajectory prediction using tensor-reduced physiological signals.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- [Computer-assisted sensing in periprosthetic joint infection : Current evidence and future perspectives for wearables].Orthopadie (Heidelberg, Germany) · 2026Review
- MetaboSense: An Integrated Quantum Dot-Mediated Aptamer Assay and Modular Microfluidic Platform for Continuous In-Line Monitoring of Lung Metabolism During Ex Vivo Perfusion.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Transforming sepsis management: AI-driven innovations in early detection and tailored therapies.Critical care (London, England) · 2025Review
- Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review.Biomedical engineering letters · 2025Review
- A Hands-On Introduction to Data Analytics for Biomedical Research.Function (Oxford, England) · 2025Review
- Sepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
The quick Sequential Organ Failure Assessment (qSOFA) system identifies an individual's risk to progress to poor sepsis-related outcomes using minimal variables. We used Support Vector Machine, Learning Using Concave and Convex Kernels, and Random Forest to predict an increase in qSOFA score using electronic health record (EHR) data, electrocardiograms (ECG), and arterial line signals. We structured physiological signals data in a tensor format and used Canonical Polyadic/Parallel Factors (CP) decomposition for feature reduction. Random Forests trained on ECG data show improved performance after tensor decomposition for predictions in a 6-h time frame (AUROC 0.67 ± 0.06 compared to 0.57 ± 0.08,
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.