A machine learning approach in a monocentric cohort for predicting primary refractory disease in Diffuse Large B-cell lymphoma patients - Detrait, Marie Y.; Warnon, Stéphanie; Lagasse, Raphaël; Dumont, Laurent; De Prophétis, Stéphanie; Hansenne, Amandine; Raedemaeker, Juliette; Robin, Valérie; Verstraete, Géraldine; Gillain, Aline; Depasse, Nicolas; Jacmin, Pierre; et al. (PloS one, 2024)
Keywords: Accuracy; Adult; Aged; Aged, 80 and over; Algorithms; Artificial intelligence; B-cell lymphoma; Care and treatment; Cell culture; Clinical trials; Cohort Studies; Comorbidity; Computed tomography; Computer and Information Sciences; Cytomegalovirus; Cytomegalovirus infections; Data mining; Datasets; Decision making; Decision trees; Demographic variables; Development and progression; Diagnosis; Diagnostic imaging; Evaluation; Female; Health aspects; Health services; Hematology; Humans; Learning algorithms; Lymphocytes B; Lymphocytic leukemia; Lymphoma; Lymphoma, Large B-Cell, Diffuse - diagnosis; Lymphoma, Large B-Cell, Diffuse - mortality; Lymphoma, Large B-Cell, Diffuse - pathology; Lymphomas; Machine Learning; Male; Medical research; Medical treatment; Medicine and Health Sciences; Medicine, Experimental; Middle Aged; Mutation; Patients; Performance evaluation; Physical Sciences; Population studies; Positron emission; Positron Emission Tomography Computed Tomography; Prognosis; Regression analysis; Research and Analysis Methods; Retrospective Studies; ROC Curve; Support Vector Machine; Support vector machines; Survival; Belgium; Taiwan; Michigan; Ann Arbor Michigan; United States--US