AIMC Topic: Aged, 80 and over

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Correlation-Aware Sparse and Low-Rank Constrained Multi-Task Learning for Longitudinal Analysis of Alzheimer's Disease.

IEEE journal of biomedical and health informatics
Alzheimer's disease (AD), as a severe neurodegenerative disease, is now attracting more and more researchers' attention in the healthcare. With the development of magnetic resonance imaging (MRI), the neuroimaging-based longitudinal analysis is gradu...

Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning.

European radiology
PURPOSE: To evaluate the diagnostic performance of bone texture analysis (TA) combined with machine learning (ML) algorithms in standard CT scans to identify patients with vertebrae at risk for insufficiency fractures.

Disease Trajectories and End-of-Life Care for Dementias: Latent Topic Modeling and Trend Analysis Using Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Despite the increasing prevalence, growing costs, and high mortality of dementia in older adults in the U.S., little is known about the course of these diseases and what care dementia patients receive in their final years of life. Using a large volum...

In vitro activity of tedizolid and other comparator drugs in methicillin-resistant Staphylococcus aureus isolates in skin and soft tissue infections in seven Colombian hospitals.

Biomedica : revista del Instituto Nacional de Salud
Introduction: Methicillin-resistant Staphylococcus aureus (MRSA) causes severe skin and soft tissue infections in hospitals and, more recently, in the community. Tedizolid is a new second-generation oxazolidinone derivative having greater in vitro po...

Can low-frequency guided waves at the tibia paired with machine learning differentiate between healthy and osteopenic/osteoporotic subjects? A pilot study.

Ultrasonics
PURPOSE: Axial transmission quantitative acoustics (ax-QA) has shown to be a promising tool for assessing bone health and properties in a safe, inexpensive, and portable manner. This study investigated the efficacy of low-frequency ax-QA measured at ...

QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy.

NeuroImage
Whole brain segmentation from structural magnetic resonance imaging (MRI) is a prerequisite for most morphological analyses, but is computationally intense and can therefore delay the availability of image markers after scan acquisition. We introduce...

Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Alberta Stroke Program Early CT Score (ASPECTS) was devised as a systematic method to assess the extent of early ischemic change on noncontrast CT (NCCT) in patients with acute ischemic stroke (AIS). Our aim was to automate AS...

Refining Convolutional Neural Network Detection of Small-Bowel Obstruction in Conventional Radiography.

AJR. American journal of roentgenology
OBJECTIVE: The purpose of this study was to evaluate improvement of convolutional neural network detection of high-grade small-bowel obstruction on conventional radiographs with increased training set size.

Efficient sleep classification based on entropy features and a support vector machine classifier.

Physiological measurement
OBJECTIVE: Sleep quality helps to reflect on the physical and mental condition, and efficient sleep stage scoring promises considerable advantages to health care. The aim of this study is to propose a simple and efficient sleep classification method ...