ArticleFrontiers in immunology2021
Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission.
Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.
- Sepsis mortality prediction using machine learning and deep learning - a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Trial
- Article
- HCAR3 and Kynurenic Acid in Cancer: A Promising Axis of Immunometabolic Regulation or a Scientific Mirage?International journal of molecular sciences · 2025Review
- Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap.NPJ digital medicine · 2025Review
- Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock.Scientific reports · 2025Article
- A 5-transcript signature for discriminating viral and bacterial etiology in pediatric pneumonia.iScience · 2025Article
- FUNCTIONAL IMMUNOPHENOTYPING FOR PRECISION THERAPIES IN SEPSIS.Shock (Augusta, Ga.) · 2025Review
- Deep learning and machine learning integration of radiomics and transcriptomics predicts response-adapted radiotherapy outcome and radiosensitivity in resectable locally advanced laryngeal carcinoma.Frontiers in artificial intelligence · 2025Article
- Machine learning-based time-to-event survival analysis in pediatric patients with severe sepsis.Frontiers in pediatrics · 2025Article
- Navigating the Modern Landscape of Sepsis: Advances in Diagnosis and Treatment.International journal of molecular sciences · 2024Review
- Article
- The immune landscape of sepsis and using immune clusters for identifying sepsis endotypes.Frontiers in immunology · 2024Article
- Utilize proteomic analysis to identify potential therapeutic targets for combating sepsis and sepsis-related death.Frontiers in endocrinology · 2024Article
- SAFE-MIL: a statistically interpretable framework for screening potential targeted therapy patients based on risk estimation.Frontiers in genetics · 2024Article
- Integrated multi-omics and artificial intelligence to explore new neutrophils clusters and potential biomarkers in sepsis with experimental validation.Frontiers in immunology · 2024Article
- Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model.Frontiers in genetics · 2024Article
- The use of probabilistic graphical models in pediatric sepsis: a feasibility and scoping review.Translational pediatrics · 2023Article
- Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images.Pharmaceutics · 2023Article
- Six potential biomarkers in septic shock: a deep bioinformatics and prospective observational study.Frontiers in immunology · 2023Observational
Corrections and comments
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Authors and funding
5 authors at 5 institutions in 3 countries.
Funding
Abstract
A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to perform a meta-analysis of previously published gene expression datasets to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 h of pediatric ICU (PICU) admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with 10 iterations, the overall mean area under the curve reached 0.82. Using a subset of the same set of genes, we further achieved an overall area under the curve of 0.72, 0.96, 0.83, and 0.82, respectively, on four independent external validation sets. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers (
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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.