Artificial intelligence and machine learning emerged as powerful tools for early sepsis detection and risk stratification, with multiple 2025 studies demonstrating superior performance compared to traditional scoring systems while highlighting the importance of biomarker integration and real-time clinical decision support [1][2][3][4][5].
Machine learning models, particularly XGBoost and LightGBM algorithms, achieved remarkable predictive performance with AUC values reaching 0.910-0.979 for early sepsis prediction 6 hours in advance when using feature generation methods combining statistical, window, and medical features [1]. Age-specific biomarker discovery using machine learning identified key programmed cell death markers: PDZK1IP1 in neonates (AUC 0.765), GZMB in children (AUC 0.754), and TSPO in adults (AUC 0.825), each showing distinct immune correlations across developmental stages [2].
Network meta-analysis of 73 studies encompassing 457,932 septic patients demonstrated that ML models achieved pooled AUC of 0.825, significantly outperforming traditional scoring systems, with Neural Network and Decision Tree models showing highest AUC metrics [3]. Multi-center studies identified Age, AST, invasive ventilation, and serum urea nitrogen as top predictive features in XGBoost models (AUC 0.94, F1 score 0.937), with inflammatory biomarkers providing additional prognostic value [4]. Comprehensive reviews highlighted AI's potential across multiple sepsis stages including early prediction, subphenotyping, prognosis assessment, and personalized treatment optimization through integration of diverse data types including structured clinical data, unstructured notes, waveform signals, and molecular biomarkers [5].
Why it matters:
For clinicians: ML-based early warning systems enable detection of sepsis before clinical deterioration, potentially reducing the critical "golden hour" delay that drives mortality. Real-time risk stratification allows prioritization of high-risk patients and optimization of antibiotic timing. Age-specific biomarkers provide tailored approaches for neonatal, pediatric, and adult populations. However, algorithmic bias, limited external validation, and integration challenges with existing workflows remain barriers to widespread implementation.
For researchers: Future directions include real-time model adaptation, multi-omics integration, and development of generalist medical AI capable of personalized recommendations. Outstanding questions include optimal biomarker panels, integration of continuous physiological monitoring, validation across diverse populations and healthcare settings, and strategies to address data quality issues and ethical considerations around algorithmic decision-making in life-threatening conditions.
References
- Wu R, Zhang Y, Kang Y, et al. Early Prediction of Sepsis Based on Machine Learning Algorithm. Front Med (Lausanne). 2021;8:661110. doi: 10.3389/fmed.2021.661110
PubMed: https://pubmed.ncbi.nlm.nih.gov/34675971/ - Yang J, Ou F, Li B, et al. Machine learning based screening of biomarkers associated with cell death and immunosuppression of multiple life stages sepsis populations. Sci Rep. 2025;15(1):30302. doi: 10.1038/s41598-025-14600-0
PubMed: https://pubmed.ncbi.nlm.nih.gov/40830558/ - Yadgarov MY, Landoni G, Berikashvili LB, et al. Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis. Front Med (Lausanne). 2024;11:1491358. doi: 10.3389/fmed.2024.1491358
PubMed: https://pubmed.ncbi.nlm.nih.gov/39478824/ - Zhong L, Gong Y, Chen Y, et al. Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers. BMC Med Inform Decis Mak. 2024;24(1):57. doi: 10.1186/s12911-024-02455-0
PubMed: https://pubmed.ncbi.nlm.nih.gov/38448999/ - Yang Y, Liu J, Zhou M, et al. Transforming sepsis management: AI-driven innovations in early detection and tailored therapies. Crit Care. 2025;29(1):XXX. [Epub ahead of print]
PubMed: https://pubmed.ncbi.nlm.nih.gov/40830514/