Yazar "Akbilgiç, Oğuz" için listeleme
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Application of machine learning to the prediction of postoperative sepsis after appendectomy
Bunn, Corinne; Kulshrestha, Sujay; Boyda, Jason; Balasubramanian, Neelam; Birch, Steven; Karabayır, İbrahim; Akbilgiç, Oğuz (Mosby-Elsevier, 2021)Background: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with ... -
Gradient boosting for Parkinson's disease diagnosis from voice recordings
Karabayır, İbrahim; Goldman, Samuel M.; Pappu, Suguna; Akbilgiç, Oğuz (Bmc, 2020)Background Parkinson's Disease (PD) is a clinically diagnosed neurodegenerative disorder that affects both motor and non-motor neural circuits. Speech deterioration (hypokinetic dysarthria) is a common symptom, which often ... -
Machine learning analysis on American Gut Project microbiome data to identify subjects with cancer both with and without chemotherapy exposure.
Akbilgiç, Oğuz; Karabayır, İbrahim; Güntürkün, Hakan; Pierre, Joseph F.; Rashe, Aslıley C.; Thomas, Alexandra (Lippincott Williams & Wilkins, 2020)[Abstract Not Available] -
Machine Learning to Identify Dialysis Patients at High Death Risk
Akbilgiç, Oğuz; Obi, Yoshitsugu; Potukuchi, Praveen K.; Karabayır, İbrahim; Nguyen, Danh, V; Soohoo, Melissa; Kovesdy, Csaba P. (Elsevier Science Inc, 2019)Introduction: Given the high mortality rate within the first year of dialysis initiation, an accurate estimation of postdialysis mortality could help patients and clinicians in decision making about initiation of dialysis. ... -
A Novel Learning Algorithm to Optimize Deep Neural Networks: Evolved Gradient Direction Optimizer (EVGO)
Karabayır, İbrahim; Akbilgiç, Oğuz; Taş, Nihat (Ieee-Inst Electrical Electronics Engineers Inc, 2021)Gradient-based algorithms have been widely used in optimizing parameters of deep neural networks' (DNNs) architectures. However, the vanishing gradient remains as one of the common issues in the parameter optimization of ...
