AIMC Topic: Longitudinal Studies
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Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach.
International journal of medical informatics
Apr 18, 2020
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
Apr 9, 2020
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
Mar 28, 2020
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...
Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard.
Ophthalmology
Mar 10, 2020
PURPOSE: To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss.
Adult
Humans
Sensitivity and Specificity
Glaucoma
Cross-Sectional Studies
Middle Aged
Vision Disorders
Artificial Intelligence
Male
Visual Fields
Longitudinal Studies
Retrospective Studies
Predictive Value of Tests
False Negative Reactions
Monitoring, Physiologic
Female
Optic Nerve Diseases
Visual Acuity
Aged
Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm.
Neuroscience letters
Jan 31, 2020
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
Jan 20, 2020
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
Dec 28, 2019
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
Dec 9, 2019
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...
Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence.
NeuroImage. Clinical
Dec 9, 2019
The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-...
Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach.
Journal of psychiatric research
Dec 6, 2019
A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive intervention. However, efforts to develop algorithms for internalizing disorder onset have been limited. The g...
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