AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Longitudinal Studies

Showing 251 to 260 of 445 articles

Clear Filters

What are the main patient safety concerns of healthcare stakeholders: a mixed-method study of Web-based text.

International journal of medical informatics
OBJECTIVES: Various healthcare stakeholders define quality of care in different ways. Public policy could advocate all these concerns. This study was conducted to identify the main themes on patient safety of stakeholders expressed before and after t...

Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach.

International journal of medical informatics
OBJECTIVE: Mental or substance use disorders (M/SUD) are major contributors of disease burden with high risk for hospital readmissions. We sought to develop and evaluate a readmission model using a machine learning (ML) approach.

Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model.

NeuroImage
BACKGROUND AND AIMS: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 ...

Brain MRI analysis using a deep learning based evolutionary approach.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural network (CNN) models have recently demonstrated impressive performance in medical image analysis. However, there is no clear understanding of why they perform so well, or what they have learned. In this paper, a three-dimensional...

Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm.

Neuroscience letters
Because depression has high prevalence and cause enduring disability, it is important to predict onset of depression among community dwelling adults. In this study, we aimed to build a machine learning-based predictive model for future onset of depre...

A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the dis...

A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis.

NeuroImage. Clinical
INTRODUCTION: Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w lesions on brain MR scans is considered a predictive biomarker for the diseas...

Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies.

NeuroImage
In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily between scans. For quantitative analysis of intensity data, images are typically normalized to a cons...