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Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data.

Medical image analysis
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Currently, many multi-task learning approaches have been proposed to predict the disease progression at the earl...

System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia.

Sensors (Basel, Switzerland)
Face recognition is a natural skill that a child performs from the first days of life; unfortunately, there are people with visual or neurological problems that prevent the individual from performing the process visually. This work describes a system...

Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers.

Disease markers
INTRODUCTION: Hematological indices including red cell distribution width and neutrophil to lymphocyte ratio are proven to be associated with outcomes of acute coronary syndrome. The usefulness of machine learning techniques in predicting mortality a...

Machine Learning to Predict Delays in Adjuvant Radiation following Surgery for Head and Neck Cancer.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: To apply a novel methodology with machine learning (ML) to a large national cancer registry to help identify patients who are high risk for delayed adjuvant radiation.

Robot-Assisted Versus Fluoroscopy-Guided Pedicle Screw Placement in Transforaminal Lumbar Interbody Fusion for Lumbar Degenerative Disease.

World neurosurgery
OBJECTIVE: To compare the clinical accuracy and perioperative outcomes for pedicle screw placement in transforaminal lumbar interbody fusion (TLIF) between the robot-assisted (RA) technique and fluoroscopy-guided (FG) technique.

Prediction of mortality following pediatric heart transplant using machine learning algorithms.

Pediatric transplantation
BACKGROUND: Optimizing transplant candidates' priority for donor organs depends on the accurate assessment of post-transplant outcomes. Due to the complexity of transplantation and the wide range of possible serious complications, recipient outcomes ...

A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This paper presents a fully developed computer aided diagnosis (CAD) system for early knee OsteoArthritis (OA) detection using knee X-ray imaging and machine learning algorithms. The X-ray images are first preprocessed in the Fourier domain using a c...

Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks.

International journal of environmental research and public health
Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection has become important to prevent other health condit...