ArticleFrontiers in immunology2023
A simplified machine learning model utilizing platelet-related genes for predicting poor prognosis in sepsis.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Serum LINC01127 serves as a diagnostic biomarker for sepsis and its predictive value for clinical outcomes.Hereditas · 2026Article
- Development and validation of a nomogram for predicting thrombocytopenia in sepsis patients treated with linezolid.BMC pharmacology & toxicology · 2026Article
- Development and validation of machine learning models for 30-day mortality prediction in septic shock.Frontiers in microbiology · 2026Article
- Development and validation of QuadraSynth ML: a novel classifier for sepsis prediction.Frontiers in cellular and infection microbiology · 2026Article
- Stratified assessment of platelets in sepsis: from dynamic counts to functional phenotypes.Frontiers in immunology · 2026Review
- S100A12 as a key biomarker in a neutrophil-associated gene prediction model for sepsis diagnosis.Medicine · 2025Article
- LncRNA CYP1B1-AS1 as a clinical biomarker exacerbates sepsis inflammatory response via targeting miR- 18a- 5p.BMC immunology · 2025Article
- Harness machine learning for multiple prognoses prediction in sepsis patients: evidence from the MIMIC-IV database.BMC medical informatics and decision making · 2025Article
- Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.Scientific reports · 2025Article
- Article
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- SAFE-MIL: a statistically interpretable framework for screening potential targeted therapy patients based on risk estimation.Frontiers in genetics · 2024Article
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6 authors.
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Abstract
Background: Thrombocytopenia is a known prognostic factor in sepsis, yet the relationship between platelet-related genes and sepsis outcomes remains elusive. We developed a machine learning (ML) model based on platelet-related genes to predict poor prognosis in sepsis. The model underwent rigorous evaluation on six diverse platforms, ensuring reliable and versatile findings. Methods: A retrospective analysis of platelet data from 365 sepsis patients confirmed the predictive role of platelet count in prognosis. We employed COX analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) techniques to identify platelet-related genes from the GSE65682 dataset. Subsequently, these genes were trained and validated on six distinct platforms comprising 719 patients, and compared against the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ-Failure Assessment (SOFA) score. Results: A PLT count <100×10 Conclusion: Our findings highlight the broad applicability of this ML model, based on platelet-related genes, in facilitating early treatment decisions for sepsis patients with poor outcomes. Our study paves the way for advancements in personalized medicine and improved patient care.
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