aceArtificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care
Critically ill cancer patients have a unique physiological profile marked by severe immunosuppression, frailty, and multimorbidity, making traditional tools like Acute Physiology and Chronic Health Evaluation II or Sequential Organ Failure Assessment often inadequate for accurate risk assessment. This review explores artificial intelligence's potential to transform onco-critical care from reactive to predictive management. We will synthesize literature on two key applications: Early sepsis detection in criticall…
Dynamic machine learning models using vital signs, lab trends and unstructured data can detect sepsis-related deterioration hours before clinical decompensation in critically ill cancer patients, supporting a shift from reactive to predictive onco-critical care.
Findings for general ICU populations may not directly apply to cancer patients and must be extrapolated with caution due to different pathophysiology.
Evidence
- Peer-reviewedWorld Journal of Critical Care Medicine2026-09-09
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Truvace Impact Record TRV-2026-1023, v1: “Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care.” Truvace, 2026-09-09. /record/TRV-2026-1023 (accessed at citation time). sha256 0ddb600b99860304…
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