OBJECTIVE: Predict the number of hospitalizations for asthma and pneumonia associated with exposure to air pollutants in the city of São José dos Campos, São Paulo State.
We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were ex...
IEEE journal of biomedical and health informatics
Jun 8, 2017
Forced spirometry testing is gradually becoming available across different healthcare tiers including primary care. It has been demonstrated in earlier work that commercially available spirometers are not fully able to assure the quality of individua...
Alimentary pharmacology & therapeutics
Jun 6, 2017
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) affects 20%-40% of the general population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Electronic medical records facilitate large-scale epidemiologic...
OBJECTIVE: Follow-up of right ventricular performance is important for patients with congenital heart disease. Cardiac magnetic resonance imaging is optimal for this purpose. However, observer-dependency of manual analysis of right ventricular volume...
OBJECTIVE: Structural and functional abnormalities have been reported in the brain of patients with adolescent-onset schizophrenia (AOS). The brain regional functional synchronization in patients with AOS remains unclear.
With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the sema...
Paediatric inflammatory bowel disease (PIBD), comprising Crohn's disease (CD), ulcerative colitis (UC) and inflammatory bowel disease unclassified (IBDU) is a complex and multifactorial condition with increasing incidence. An accurate diagnosis of PI...
The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF). We collected various candidate features fro...
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