Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care

Table 1 Comparison of traditional scoring systems vs machine learning models in sepsis prognostication

Feature	Traditional scores (SOFA, APACHE II, SAPS II)	Machine learning models (CatBoost, random forest, XGBoost, DeepAISE)
Data utilization	Uses static data, typically the worst values recorded within the first 24 hours of ICU admission[6]	Utilizes continuous, dynamic, real-time data streams including high-frequency vital signs and lab trends[18,20]
Data types	Limited to predefined structured physiological variables and lab values	Capable of processing structured data, unstructured clinical notes (via NLP), and multi-omics data[12,17]
Handling of complexity	Assumes linear relationships between physiological variables and patient outcomes[39]	Capable of modeling highly complex, non-linear interactions among high-dimensional data points[24]
Performance in oncology	Often inadequate due to lack of cancer-specific variables; altered baselines in cytopenic patients skew results[4,5]	Highly adaptable; incorporates cancer-specific comorbidities, treatment history, and specific biomarkers (e.g., blood urea nitrogen, red cell distribution width)[3,4]
Interpretability	Transparent, simple point-based systems; easily calculated manually at the bedside	Inherently “black box”; requires explainable AI techniques like SHAP or LIME to translate logic to clinicians[35]
Application	Retrospective severity assessment and general benchmarking	Real-time early warning alerts, dynamic mortality probability, and personalized treatment recommendations[39]

SOFA: Sequential Organ Failure Assessment; APACHE: Acute Physiology and Chronic Health Evaluation; SAPS: Simplified Acute Physiology Score; ICU: Intensive care unit; NLP: Natural language processing; AI: Artificial intelligence; SHAP: SHapley Additive exPlanations; LIME: Local interpretable model-agnostic explanations.