ReviewBiomedicines2024
Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests-A Systematic Review.
Review in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Coagulation Risk Prediction in Patients With Liver Failure: Integrated Meta-Analysis and Machine Learning Model Study.JMIR medical informatics · 2025Pooled it
- Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care.World journal of critical care medicine · 2026Review
- Explainable Retrospective Sepsis Classification Based on Elixhauser Comorbidity Groups Using Machine and Deep Learning on MIMIC-IV.Journal of clinical medicine · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Integrating host mRNA signatures and clinical trajectories with machine learning for risk stratification and survival prediction in sepsis: an ICU-based prospective cohort study.BMC infectious diseases · 2026Article
- Early Sepsis Detection Using Heterogeneous Structured ICU Data with Explainable Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- SOFA-2 score predicts mortality in pneumonia-associated sepsis: a retrospective cohort study.Critical care (London, England) · 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
- Predictors of adult ICU mortality: a retrospective study at two government hospitals in Ethiopia.Scientific reports · 2026Article
- Explainable machine learning with routine biomarkers identifies culture-defined bacteremic urosepsis.Scientific reports · 2026Article
- Optimizing sepsis mortality prediction using hybrid federated learning and explainable AI framework.Scientific reports · 2026Article
- Dynamic prognostic prediction in sepsis using longitudinal blood gas trajectories: development and external validation.Frontiers in medicine · 2026Article
- Artificial Intelligence in Intensive Care: An Overview of Systematic Reviews with Clinical Maturity and Readiness Mapping.Journal of clinical medicine · 2025Review
- Review
- Sepsis Burden in a Major Romanian Emergency Center-An 18-Year Retrospective Analysis of Mortality and Risk Factors.Medicina (Kaunas, Lithuania) · 2025Article
- A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study.BMC infectious diseases · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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.