Observational studyNature communications2025
Explainable AI unravels sepsis heterogeneity via coagulation-inflammation profiles for prognosis and stratification.
Observational study in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Deciphering sepsis molecular subtypes using large-scale data to identify subtype-specific drug repurposing.bioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence Models for Mortality and Outcome Prediction in Intensive Care Unit Sepsis: A Systematic Review.Journal of personalized medicine · 2026Review
- Revolutionizing Healthcare With Paper-Based Nucleic Acid Testing.Exploration (Beijing, China) · 2026Review
- A Comparative Evaluation of SIRS, NEWS2, SOFA, and the Novel SOFA-2 Score for Sepsis Classification Agreement and Outcome Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- In vivo CAR-cell therapy: current challenges and emerging therapeutic advances.Molecular biomedicine · 2026Review
- Interpretable machine learning for early prediction of sepsis-induced coagulopathy: a multicenter retrospective development and validation study.BMC medical informatics and decision making · 2026Article
- Translational Potential and Explainability of Artificial Intelligence-Based Clinical Decision Support for Adults in Intensive Care: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- Clinical phenotype identification based on inflammation-nutrition-coagulation biomarkers in advanced non-small cell lung cancer.Frontiers in nutrition · 2026Article
- Artificial intelligence-assisted phenotyping of sepsis: Research progress, clinical challenges, and translational prospects.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
Abstract
Sepsis is a leading cause of hospital mortality, and its significant heterogeneity complicates prognosis and stratification. To address this challenge, we developed an explainable artificial intelligence prognostic model (SepsisFormer, a transformer-based neural network) and an automated risk-stratification tool (SMART) for sepsis. In a multi-center retrospective study of 12,408 sepsis patients, SepsisFormer achieved high predictive accuracy (AUC: 0.9301, sensitivity: 0.9346, and specificity: 0.8312). SMART (AUC: 0.7360) surpassed most established scoring systems. Seven coagulation-inflammatory routine laboratory measurements and patient age were identified to classify patients' four risk levels (mild, moderate, severe, dangerous) and two subphenotypes (CIS1 and CIS2), each with distinct clinical characteristics and mortality rates. Notably, patients with moderate/severe levels or CIS2 derive more significant benefits from anticoagulant treatment. Our work, therefore, offers a set of simple, real-time executable tools for sepsis heterogeneity, demonstrating the potential to enhance sepsis clinical practice globally, particularly in resource-constrained healthcare settings.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.