Diagnostic and interventional imaging
Nov 11, 2019
OBJECTIVE: To assess the diagnostic value of machine learning-based texture feature analysis of late gadolinium enhancement images on cardiac magnetic resonance imaging (MRI) for assessing the presence of ventricular tachyarrhythmia (VT) in patients ...
Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority of Parkinson's patients lose their ability to speak, write and balance. Many machine learning methods...
BACKGROUND: Postoperative pancreatic fistula remains an unsolved challenge after pancreatoduodenectomy. Important in this regard is the presence of a soft pancreatic texture which is a major risk factor. Advances in machine learning and texture analy...
BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the a...
While machine learning approaches can enhance prediction ability, little is known about their ability to predict 30-day readmission after hospitalization for Chronic Obstructive Pulmonary Disease (COPD). We identified patients aged ≥40 years with unp...
Human voice recognition systems (VRSs) are a prerequisite for voice-controlled human-machine interfaces (HMIs). In order to avoid interference from unexpected background noises, skin-attachable VRSs are proposed to directly detect physiological mecha...
International journal of molecular sciences
Nov 11, 2019
The work aiming to unravel the correlation between protein sequence and function in the absence of structural information can be highly rewarding. We present a new way of considering descriptors from the amino acids index database for modeling and pr...
The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered...
Machine learning methods show promise to translate univariate biomarker findings into clinically useful multivariate decision support systems. At current, works in major depressive disorder have predominantly focused on neuroimaging and clinical pred...
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