A machine learning algorithm to improve patient-centric pediatric cardiopulmonary resuscitation

A Novel Embedded Feature Selection and Dimensionality Reduction Method for an SVM Type Classifier to Predict Periventricular Leukomalacia (PVL) in Neonates

“…These four signals monitoring the heart's health are believed to be carrying significant indicators for PVL occurrence, particularly since PVL injury in neonates is due to, but not limited to, the effects of various interventions such as cardiac surgery required to treat children with complex congenital heart diseases, such as HLHS and TGA [21]. PVL injury in each neonate was inferred from the MRI comparison by a trained physician (Figure 1), where PVL positive (p = 32) denoted as patients with evidence of intracranial hemorrhage in size larger than 100 mm 3 and PVL negative (n = 24) denoted such patients with intracranial hemorrhage smaller than 10 mm 3 . Figure 1 illustrates the data collection's temporal path and contrasts the shortcomings of the current PVL diagnosis and the developed predictive model's superiority.…”

“…In particular, the introduction of AI and ML into clinical research has allowed the healthcare sector to make advances in the decision-making process through better detection and prediction of diseases in patients at an early stage ([ 2 ] pp. 289–302, [ 3 , 4 ]). However, despite the excellent solutions offered by the automatic ML (aML) [ 5 ] approach, its learning process has become increasingly complex and opaque, limiting its applicability in medical research [ 1 , 2 ].…”