AIMC Topic: Predictive Value of Tests
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Design and application of tumor prediction model based on statistical method.
Computer assisted surgery (Abingdon, England)
Oct 16, 2017
Two prediction models for tumor prediction based on logistic regression and BP neural network were proposed in this paper; a sensitivity analysis of risk factors was also conducted. The two protocols will be implemented in the R language and demonstr...
Developing a dengue forecast model using machine learning: A case study in China.
PLoS neglected tropical diseases
Oct 16, 2017
BACKGROUND: In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine lea...
Learning ensemble classifiers for diabetic retinopathy assessment.
Artificial intelligence in medicine
Oct 6, 2017
Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doct...
Decision Trees
Predictive Value of Tests
Humans
Diabetic Retinopathy
Machine Learning
Clinical Decision-Making
Decision Support Techniques
Reproducibility of Results
Prognosis
Diabetes Mellitus, Type 2
Risk Factors
Time Factors
Electronic Health Records
Diabetes Mellitus, Type 1
Risk Assessment
Decision Support Systems, Clinical
Fuzzy Logic
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
Male
Aortography
Middle Aged
Humans
Sinus of Valsalva
Diagnosis, Computer-Assisted
Machine Learning
Aortic Valve Stenosis
Phenotype
Bicuspid Aortic Valve Disease
Reproducibility of Results
Aortic Valve
Female
Heart Valve Diseases
Pattern Recognition, Automated
Aged
Predictive Value of Tests
Aorta, Thoracic
Adult
Radiographic Image Interpretation, Computer-Assisted
Computed Tomography Angiography
Cross-Sectional Studies
Aortic Valve Insufficiency
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...
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