We present a novel approach utilizing non-invasive near-infrared spectroscopy (NIRS) to assess disease severity in Extracorporeal Membrane Oxygenation (ECMO) patients. By monitoring lower limb microcirculation, our real-time assessment enables informed adjustments to ECMO settings and cardiovascular drug dosages, potentially mitigating complications and improving patient outcomes. Through machine learning, we classified VV-ECMO and VA-ECMO patient populations into high and low disease severity groups with an accuracy of 80%. The NIRS and support vector machine(SVM) combination demonstrate promising potential for clinically distinguishing disease severity in ECMO patients, providing valuable treatment insights and predictive tools for patient conditions and prognoses.
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