World Journal of Critical Care Medicine
TL;DR
The analysis will show how dynamic machine learning models, unlike static scores, use vital signs, lab trends, and unstru-ctured machine learning data to detect deterioration hours before clinical decompensation, suggesting that analytics can transform oncology intensive care unit care, balancing aggressive treatments and palliative care.
Abstract
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 critically ill cancer patients and refining mortality prediction models to guide ethical care. The analysis will show how dynamic machine learning models, unlike static scores, use vital signs, lab trends, and unstructured machine learning data to detect deterioration hours before clinical decompensation. The review will also examine barriers to adoption, highlighting the need for explainable artificial intelligence to build clinician trust and address data heterogeneity across cancer populations. Ultimately, it will suggest that analytics can transform oncology intensive care unit care, balancing aggressive treatments and palliative care. While focusing on cancer patients, some evidence from general intensive care unit populations will be identified as extrapolated with caution, given different pathophysiology.