Qin Zhang

A machine-learning-derived online prediction model based on inflammatory and nutritional composite indicators for acute kidney injury in sepsis patients with multiple myeloma

An interpretable Random Forest model employing eight routinely available indicators accurately predicts AKI in septic MM patients, bridging algorithmic complexity and bedside applicability, and may facilitate early risk stratification and personalized renal protection in this high-risk group.