AIMC Topic:
Predictive Value of Tests
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Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity.
Drug and alcohol dependence
Oct 1, 2019
BACKGROUND: This longitudinal study explored the utility of machine learning (ML) methodology in predicting the trajectory of severity of substance use from childhood to thirty years of age using a set of psychological and health characteristics.
Predicting post-stroke pneumonia using deep neural network approaches.
International journal of medical informatics
Oct 1, 2019
BACKGROUND AND PURPOSE: Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely and accurate prediction of post-stroke pneumonia would be highly valuable in clinical practice. Pre...
Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.
Ophthalmology
Sep 30, 2019
PURPOSE: To develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SDÂ OCT) optic nerve head images.
A new approach for brain tumor diagnosis system: Single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network.
Medical hypotheses
Sep 30, 2019
Magnetic resonance imaging (MRI) images can be used to diagnose brain tumors. Thanks to these images, some methods have so far been proposed in order to distinguish between benign and malignant brain tumors. Many systems attempting to define these tu...
Endocan serum concentration in uninfected newborn infants.
Journal of infection in developing countries
Sep 30, 2019
INTRODUCTION: Endocan is a specific endothelial mediator involved in the inflammatory response. Its role in the diagnosis of sepsis has been studied in adult patients and late onset neonatal sepsis. The clinical signs of early onset sepsis (EOS) are ...
Comparison of CT and MRI images for the prediction of soft-tissue sarcoma grading and lung metastasis via a convolutional neural networks model.
Clinical radiology
Sep 28, 2019
AIM: To realise the automated prediction of soft-tissue sarcoma (STS) grading and lung metastasis based on computed tomography (CT), T1-weighted (T1W) magnetic resonance imaging (MRI), and fat-suppressed T2-weighted MRI (FST2W) via the convolutional ...
Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.
Journal of veterinary internal medicine
Sep 26, 2019
BACKGROUND: Advanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human and veterinary medicine.
Deep Learning in Personalization of Cardiovascular Stents.
Journal of cardiovascular pharmacology and therapeutics
Sep 25, 2019
Deep learning (DL) application has demonstrated its enormous potential in accomplishing biomedical tasks, such as vessel segmentation, brain visualization, and speech recognition. This review article has mainly covered recent advances in the principl...
Seizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approach.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 25, 2019
Epileptic seizures occur as a result of a process that develops over time and space in epileptic networks. In this study, we aim at developing a generalizable method for patient-specific seizure prediction by evaluating the spatio-temporal correlatio...
Artificial Intelligence for Aortic Pressure Waveform Analysis During Coronary Angiography: Machine Learning for Patient Safety.
JACC. Cardiovascular interventions
Sep 25, 2019
OBJECTIVES: This study developed a neural network to perform automated pressure waveform analysis and allow real-time accurate identification of damping.
Cardiac Catheterization
Humans
Reproducibility of Results
Europe
Observer Variation
Heart Rate
Machine Learning
Risk Factors
Predictive Value of Tests
Neural Networks, Computer
Arterial Pressure
Artifacts
Patient Safety
Coronary Angiography
Aorta
Aged
Databases, Factual
Coronary Artery Disease
Male
Female
Middle Aged
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