AIMC Topic:
Predictive Value of Tests
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Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals.
Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999)
Oct 2, 2017
OBJECTIVE: To conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI), F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and regional cerebral blood flo...
Special section on artificial intelligence for diabetes.
Artificial intelligence in medicine
Sep 22, 2017
Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory.
Nanotoxicology
Sep 22, 2017
Nanoparticles (NPs) are part of our daily life, having a wide range of applications in engineering, physics, chemistry, and biomedicine. However, there are serious concerns regarding the harmful effects that NPs can cause to the different biological ...
Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review.
Tuberculosis (Edinburgh, Scotland)
Sep 20, 2017
Tuberculosis [TB] has afflicted numerous nations in the world. As per a report by the World Health Organization [WHO], an estimated 1.4 million TB deaths in 2015 and an additional 0.4 million deaths resulting from TB disease among people living with ...
A hierarchical classifier based on human blood plasma fluorescence for non-invasive colorectal cancer screening.
Artificial intelligence in medicine
Sep 20, 2017
Colorectal cancer (CRC) a leading cause of death by cancer, and screening programs for its early identification are at the heart of the increasing survival rates. To motivate population participation, non-invasive, accurate, scalable and cost-effecti...
Machine-learning phenotypic classification of bicuspid aortopathy.
The Journal of thoracic and cardiovascular surgery
Sep 14, 2017
BACKGROUND: Bicuspid aortic valves (BAV) are associated with incompletely characterized aortopathy. Our objectives were to identify distinct patterns of aortopathy using machine-learning methods and characterize their association with valve morpholog...
Aortic Aneurysm
Aortography
Sinus of Valsalva
Reproducibility of Results
Heart Valve Diseases
Aged
Pattern Recognition, Automated
Predictive Value of Tests
Adult
Humans
Machine Learning
Aorta, Thoracic
Cross-Sectional Studies
Radiographic Image Interpretation, Computer-Assisted
Diagnosis, Computer-Assisted
Phenotype
Male
Computed Tomography Angiography
Aortic Valve Insufficiency
Aortic Valve Stenosis
Middle Aged
Bicuspid Aortic Valve Disease
Female
Aortic Valve
Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis.
Scientific reports
Sep 7, 2017
Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel b...
Accurate Identification of Fatty Liver Disease in Data Warehouse Utilizing Natural Language Processing.
Digestive diseases and sciences
Aug 31, 2017
INTRODUCTION: Natural language processing is a powerful technique of machine learning capable of maximizing data extraction from complex electronic medical records.
Using Machine Learning to Define the Association between Cardiorespiratory Fitness and All-Cause Mortality (from the Henry Ford Exercise Testing Project).
The American journal of cardiology
Aug 30, 2017
Previous studies have demonstrated that cardiorespiratory fitness is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined ca...
Predicting symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network in a pediatric population.
Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Aug 29, 2017
PURPOSE: Artificial neural networks (ANN) are increasingly applied to complex medical problem solving algorithms because their outcome prediction performance is superior to existing multiple regression models. ANN can successfully identify symptomati...
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