April Garlejo, Jacob Bonner, Ashley Paddock, John Park, Nolan Lyda, Ahmed Zaky, Susan McMullan
Assessing and Improving Provider Knowledge for a Cardiothoracic Intensive Care Unit Electronic Dashboard Initiative
Healthcare (Basel, Switzerland)
study reports application extreme learning machine elm nearrealtime kidney monitoring via urine neutrophil gelatinaseassociated lipocalin ngal detection 3d graphene electrode integration marks first instance combining graphenebased electrode machine learning enhance ngal detection accuracy building groups 2020 research methodology involves two key components graphene electrode functionalized lipocalin2 antibody ngal detection elm application improved prediction accuracy using urine analysis data results show significant 15 increase area curve auc ngal determination error reduction 6 054 ngml within linear range 27140 ngml elm also lowered detection limit 148 089 ngml increased accuracy precision sensitivity specificity f1 score aki prediction 889 3069 678 994 1907 respectively findings underscore efficacy simple neural networks enhancing graphenebased electrochemical sensors aki biomarkers elm chosen optimal performanceresource balance comparative analysis elm support vector machines multilayer perceptron random forest algorithms also included research suggests potential miniaturizing aienhanced sensors practical applications