ReviewEBioMedicine2022
Sepsis biomarkers and diagnostic tools with a focus on machine learning.
Review in EBioMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 3 of them syntheses that pooled it.
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
106 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Predictive enrichment using biomarkers in studies of critically-ill patients with sepsis: a systematic review.Critical care (London, England) · 2025Pooled it
- Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.BMC infectious diseases · 2025Pooled it
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Plasma matrix metalloproteinase-3 predicts mortality in acute respiratory distress syndrome: a biomarker analysis of a randomized controlled trial.Respiratory research · 2023Trial
- Approximating Molecular Sepsis Subtypes Using Bedside Data in Resource-Limited Settings: A Multicenter Analysis From Uganda.Critical care explorations · 2026Observational
- Comparative prognostic performance of SOFA-1 and SOFA-2 in patients with sepsis at ICU admission.Clinical medicine (London, England) · 2026Article
- Phenotype discovery and mortality prediction in sepsis-induced myocardial dysfunction: a deep learning and stratified modeling approach.BMC medical informatics and decision making · 2026Article
- Article
- Histone Lactylation in Immune Cells and Its Predictive Role in Sepsis Progression: A Prospective Observational Study.Shock (Augusta, Ga.) · 2026Observational
- The Dual Role of Interleukin-10 in Sepsis: Research Progress and Therapeutic Prospects.Inflammation · 2026Review
- Assessment of Bedside-Adaptable Models to Predict Molecular Sepsis Subtypes in a Resource-Limited Setting: A Multicenter Analysis from Uganda.medRxiv : the preprint server for health sciences · 2026Article
- Pathway-level profiling of the sepsis proteome reveals immune and transcriptional dysregulation.Molecular medicine (Cambridge, Mass.) · 2026Article
- Development and external validation of a machine learning model for predicting the 28-day mortality risk in patients with sepsis complicated by acute respiratory failure in the ICU.Journal of intensive medicine · 2026Article
- Contrastive Transformer-Driven Discovery of Temporal Hemodynamic Subphenotypes in Cardiac Surgery Patients.medRxiv : the preprint server for health sciences · 2026Article
- Development and multicenter validation of an explainable machine learning diagnostic criteria for pediatric abdominal sepsis.NPJ digital medicine · 2026Article
- Data-driven spatial-temporal framework for exploring the heterogeneity and temporality of sepsis.Chinese medical journal · 2026Review
- Artificial Intelligence- and Machine Learning-Assisted Subphenotyping for Personalized Immunotherapy in Sepsis.Journal of personalized medicine · 2026Review
- Micro/nanorobotics for sepsis diagnosis and therapy: a frontier in precision medicine.Theranostics · 2026Review
- Predicting sepsis prognosis using deep learning with routine biomarkers.Frontiers in medicine · 2026Article
- Protective role of mucosa-associated invariant T cells in sepsis-related liver injury.Frontiers in immunology · 2026Article
46 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers.
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.