A supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data.
Huo S, Nelde A, Meisel C, Scheibe F, Meisel A, Endres M, Vajkoczy P, Wolf S, Willms J, Boss J, Keller E. A supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data. Journal Of Neurosurgery 2024, 141: 509-517. PMID: 38489814, DOI: 10.3171/2023.12.jns231670.Peer-Reviewed Original ResearchMachine learningReal-time artifact removalML-based solutionsSequential feature selectionML-based algorithmsHistogram-based gradientMachine learning modelsCross-validationFeature selectionHyperparameter optimizationArtifact removalML applicationsMultiple biosensorsRandom treeLearning modelsSupervised modelsGrid searchNormal signalMulticlass areaMonitoring signalsValidation cycleDatasetNested 5-fold cross-validationMonitoring dataMachine