Ibrahim Karabayir

Wake Forest School of Medicine · Section on Cardiology

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 such networks. To cope with the vanishing gradient problem, in this article, we propose a novel algorithm, evolved gradient direc...

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 presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accur...

Objective: To understand the temporal relationships of postoperative complications in children and determine if they are related to each other in a predictable manner. Summary of background data: Children with multiple postoperative complications have increased suffering and higher risk for mortality. Rigorous analysis of the temporal relations...

Abstract Aims Heart failure (HF) is a leading cause of death. Early intervention is the key to reduce HF-related morbidity and mortality. This study assesses the utility of electrocardiograms (ECGs) in HF risk prediction. Methods and results Data from the baseline visits (1987–89) of the Atherosclerosis Risk in Communities (ARIC) study was used. In...

Background: Parkinson's disease (PD) is a chronic, disabling neurodegenerative disorder. Objective: To predict a future diagnosis of PD using questionnaires and simple non-invasive clinical tests. Methods: Participants in the prospective Kuakini Honolulu-Asia Aging Study (HAAS) were evaluated biannually between 1995-2017 by PD experts using st...

Introduction More than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings...

Background/Introduction Preeclampsia (PE) is a major concern for maternal and fetal health, affecting about 5%-8% of women worldwide. Assessing PE early remains an obstetric challenge. PE increases risk of cardiovascular diseases such as heart failure and ischemic and hypertensive heart disease. This is more important when the disorder persists or...

Background Heart failure (HF) is a progressive condition with high global incidence. HF has two main subtypes: HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF). There is an inherent need for simple yet effective electrocardiogram (ECG)-based artificial intelligence (AI; ECG-AI) models that can predict HF ris...

Background Sudden cardiac death (SCD) affects >4 million people globally, and ~300,000 yearly in the US. Fatal coronary heart disease (FCHD) is used as a proxy to SCD when coronary disease is present and no other causes of death can be identified. Electrocardiographic (ECG) artificial intelligence (AI) models (ECG-AI) show promise in predicting adv...

Little is known about electrocardiogram (ECG) markers of Parkinson’s disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case–control study included samples f...

Rationale: Bronchopulmonary dysplasia (BPD) is the most common morbidity affecting very preterm infants. Gut microbial communities contribute to multiple lung diseases, and alterations of the gut microbiome may be a factor in BPD pathogenesis. Objective: To determine if features of the multikingdom gut microbiome predict the development of BPD in v...

BACKGROUND The use of traditional models to predict heart failure (HF) has limitations in preventing HF hospitalizations. Artificial intelligence (AI) and machine learning (ML) in cardiovascular medicine only have limited data published regarding HF populations, with none assessing the favorability of decongestive therapy aquapheresis (AQ). AI and...

Little is known about Electrocardiogram (ECG) markers of Parkinson’s disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before incidence of the disease. This retrospective case-control stu...

Autonomic nervous system pathology manifests early in Parkinson’s disease (PD) course. Although heart rate variability measured by a 5-minute electrocardiogram (ECG) is reported to be reduced in PD, little is known about ECG markers during prodromal stage, and brief 10-second ECGs have been rarely studied. The aim of the study is to build externall...

Objectives: Applying machine learning to predict the 10-year cardiomyopathy risk among adult survivors of childhood cancer. Methods: The St. Jude Lifetime Cohort Study (SJLIFE) is an ongoing study of adult survivors of childhood cancer with in-person clinical evaluations. ECG and ECHO data were obtained on participants who did not have cardiomyopat...

Elevated levels of dietary fats in westernized diets, associated with increased risk of obesity and other chronic diseases, are increasingly consumed by children in the United States. Cooking practices such as high heat frying and increased use of oxidizable sources of fats have introduced high levels of lipid oxidation products (LOPs) into these d...

Artificial intelligence (AI) has been increasingly used in most scientific disciplines, including medicine. AI use in medicine is frequently criticized as being a ‘black box’, implying lack of interpretability. Especially in epidemiology, parametric models such as logistic regression (LR) are preferred since the estimated regression coefficients pr...

Background Patients admitted to the emergency department (ED) with COVID-19 symptoms are routinely required to have chest radiographs and computed tomography (CT) scans. COVID-19 infection has been directly related to the development of acute respiratory distress syndrome (ARDS) and severe infections could lead to admission to intensive care and in...

Background: Patients admitted to the emergency department (ED) with COVID-19 symptoms are routinely required to have chest radiographs and computed tomography (CT) scans. COVID-19 infection has been directly related to development of acute respiratory distress syndrome (ARDS) and severe infections lead to admission to intensive care and can also le...

Early identification of individuals at risk for heart failure (HF) can assist with devising and targeting preventive strategies. We assessed the utility of ECGs analyzed by artificial intelligence (AI) in HF prediction. Abstract available at: https://www.jacc.org/doi/pdf/10.1016/S0735-1097%2821%2904400-4

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 intelligence (AI) methods using the Children's Oncology Group guideline-recommended baseline ECG to predict cardiomyopathy. Mater...

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 postoperative sepsis in these patients. Methods: The National Surgery Quality Improvement Program database was used to identif...

Objective: Development of sepsis following appendectomy is uncommon but is associated with significant morbidity and increased costs of care. Factors associated with the development of sepsis post-appendectomy are not well defined. We use a national dataset to assess the utility of various machine learning algorithms in modeling the development of...

10545 Background: Early identification of survivors at high risk for treatment-induced cardiomyopathy may allow for prevention and/or early intervention. We utilized deep learning methods using COG guideline-recommended baseline electrocardiography (ECG) to improve prediction of future cardiomyopathy. Methods: SJLIFE is a cohort of 5-year clinicall...

e14069 Background: There is growing interest in the links between cancer and the gut microbiome. However, the effect of chemotherapy upon the gut microbiome remains unknown. We studied whether machine learning can: 1) accurately classify subjects with cancer vs healthy controls and 2) whether this classification model is affected by chemotherapy ex...

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 presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accur...

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 presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accura...

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 presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accura...

Objective #1: Determine if any of the following variables are predictive of receiving a MT attempt at LUMC: 1) time since last known well, 2) type of stroke presentation (witness vs unwitnessed vs wake-up), 3) gender, 4) age, 5) medical co-morbidities (e.g. atrial fibrillation, hypertension, diabetes mellitus, tobacco use), 6) use of antiplatelet t...

Our aim was to obtain a robust model to accurately detect Parkinson’s disease from speech using machine learning. For this purpose, we used extreme gradient boosting machine (XGBoost). For comparison, we also implemented other classification methods including logistic regression (LR), support vector machine (SVM), random forest (RF) and k-nearest n...

In this work, we present an automated method to detect AF and other abnormal cardiac rhythms using short single lead ECG recordings. Our proposed method consists of two main blocks; feature extraction and classification. We have utilized CNN, Extreme Gradient Boosting (XGBoost) and various signal-processing methods on raw ECG to extract features, t...

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 colonization of the primordial gut remains undescribed. To investigate primordial fungal ecology, we performed amplicon sequencing and...

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. We aimed to use machine learning (ML) by incorporating complex information from electronic health records to predict patients at...

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 colonization of the primordial gut remains undescribed. To investigate primordial fungal ecology, we performed amplicon sequencing and...

In this paper, we establish some new Hadamard-type inequalities using elementary well-known inequalities for functions whose absolute values of the second derivatives are α-

In this paper, the authors achieve some new Hadamard type inequalities using elementary well known inequalities for functions whose second derivatives absolute values are s-geometrically and geometrically convex. And also they get some applications for special means for positive numbers.

In this paper we achieve some new Hadamard type inequalities using elementary well known inequalities for functions whose first derivatives absolute values are s-geometrically and geometrically convex. And also we get some applications for special means for positive numbers.