Bölüm "Fakülteler, İktisadi ve İdari Bilimler Fakültesi, Ekonometri Bölümü" PubMed İndeksli Yayın Koleksiyonu için listeleme
Toplam kayıt 6, listelenen: 1-6
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Application of machine learning to the prediction of postoperative sepsis after appendectomy
(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 ... -
Artificial Intelligence-Assisted Prediction of Late-Onset Cardiomyopathy Among Childhood Cancer Survivors
(NLM (Medline), 2021)PURPOSE: Early identification of childhood cancer survivors at high risk for treatment-related cardiomyopathy may improve outcomes by enabling intervention before development of heart failure. We implemented artificial ... -
Fungi form interkingdom microbial communities in the primordial human gut that develop with gestational age
(Federation Amer Soc Exp Biol, 2019)Fungal and bacterial commensal organisms play a complex role in the health of the human host. Expansion of commensal ecology after birth is a critical period in human immune development. However, the initial fungal ... -
Gradient boosting for Parkinson's disease diagnosis from voice recordings
(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 to Identify Dialysis Patients at High Death Risk
(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)
(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 ...