Novel hybridized computational paradigms integrated with five stand-alone algorithms for clinical prediction of HCV status among patients: A data-driven technique

The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the clinical prediction of hepatitis C status among patients, using both sociodemographic and clinical input variables. Both the visualized and quantitative performances of the stand-alone algorithms present the capability of the Gaussian process regression (GPR), Generalized neural network (GRNN), and Interactive linear regression (ILR) over the Support Vector Regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS) models. Hence, due to the lower performance of the stand-alone algorithms at a certain point, four different novel hybrid data intelligent algorithms were proposed, including: interactive linear regression-Gaussian process regression (ILR-GPR), interactive linear regression-generalized neural network (ILR-GRNN), interactive linear regression-Support Vector Regression (ILR-SVR), and interactive linear regression-adaptive neuro-fuzzy inference system (ILR-ANFIS), to boost the prediction accuracy of the stand-alone techniques in the clinical prediction of hepatitis C among patients. Based on the quantitative prediction skills presented by the novel hybridized paradigms, the proposed techniques were able to enhance the performance efficiency of the single paradigms up to 44% and 45% in the calibration and validation phases, respectively.Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin-10, Turkiy

Advances in Deep Learning through Gradient Amplification ?and Applications?

Deep neural networks currently play a prominent role in solving problems across a wide variety of disciplines. Improving performance of deep learning models and reducing their training times are some of the ongoing challenges. Increasing the depth of the networks improves performance but suffers from the problem of vanishing gradients and increased training times. In this research, we design methods to address these challenges in deep neural networks and demonstrate deep learning applications in several domains. We propose a gradient amplification based approach to train deep neural networks, which improves their training and testing accuraries, addresses vanishing gradients, as well as reduces the training time by reaching higher accuracies even at higher learning rates. We also develop an integrated training strategy to enable/disable amplification at certain epochs. Detailed analysis is performed on different neural networks using random amplification, where the layers to be amplified are selected randomly. The implications of gradient amplification on the number of layers, types of layers, amplification factors, training strategies and learning rates are studied in detail. With this knowledge, effective ways to update gradients are designed to perform amplification at layer-level and also at neuron-level. Lastly, we provide applications of deep learning methods to some of the challenging problems in the areas of smartgrids and bioinformatics. Deep neural networks with feed forward architectures are used to solve data integrity attacks in smart grids. We propose an image based preprocessing method to convert heterogenous genomic sequences into images which are then classified to detect Hepatitis C virus(HCV) infection stages. In summary, this research advances deep learning techniques and their applications to real world problems

EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis – 2021 update

Non-invasive tests are increasingly being used to improve the diagnosis and prognostication of chronic liver diseases across aetiologies. Herein, we provide the latest update to the EASL Clinical Practice Guidelines on the use of non-invasive tests for the evaluation of liver disease severity and prognosis, focusing on the topics for which relevant evidence has been published in the last 5 years

Ultrasound Elastography

Elastography, the science of creating noninvasive images of mechanical characteristics of tissues, has been rapidly evolving in recent years. The advantage of this technique resides in the ability to rapidly detect and quantify the changes in the stiffness of soft tissues resulting from specific pathological or physiological processes. Ultrasound elastography is nowadays applied especially on the liver and breast, but the technique has been increasingly used for other tissues including the thyroid, lymph nodes, spleen, pancreas, gastrointestinal tract, kidney, prostate, and the musculoskeletal and vascular systems. This book presents some of the applications of strain and shear-wave ultrasound elastography in hepatic, pancreatic, breast, and musculoskeletal conditions

Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation

In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid–base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes

Blood

This book examines both the fluid and cellular components of blood. After the introductory section, the second section presents updates on various topics in hemodynamics. Chapters in this section discuss anemia, 4D flow MRI in cardiology, cardiovascular complications of robot-assisted laparoscopic pelvic surgery, altered perfusion in multiple sclerosis, and hemodynamic laminar shear stress in oxidative homeostasis. The third section focuses on thalassemia with chapters on diagnosis and screening for thalassemia, high blood pressure in beta-thalassemia, and hepatitis C infection in thalassemia patients

Liver Biopsy

Liver biopsy is recommended as the gold standard method to determine diagnosis, fibrosis staging, prognosis and therapeutic indications in patients with chronic liver disease. However, liver biopsy is an invasive procedure with a risk of complications which can be serious. This book provides the management of the complications in liver biopsy. Additionally, this book provides also the references for the new technology of liver biopsy including the non-invasive elastography, imaging methods and blood panels which could be the alternatives to liver biopsy. The non-invasive methods, especially the elastography, which is the new procedure in hot topics, which were frequently reported in these years. In this book, the professionals of elastography show the mechanism, availability and how to use this technology in a clinical field of elastography. The comprehension of elastography could be a great help for better dealing and for understanding of liver biopsy