AIMC Topic: Disease Progression

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The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging d...

Predicting Progression to Advanced Age-Related Macular Degeneration from Clinical, Genetic, and Lifestyle Factors Using Machine Learning.

Ophthalmology
PURPOSE: Current prediction models for advanced age-related macular degeneration (AMD) are based on a restrictive set of risk factors. The objective of this study was to develop a comprehensive prediction model applying a machine learning algorithm a...

Does including machine learning predictions in ALS clinical trial analysis improve statistical power?

Annals of clinical and translational neurology
OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease which leads to progressive muscle weakness and eventually death. The increasing availability of large ALS clinical trial datasets have generated much interest in developing...

Detecting asthma exacerbations using daily home monitoring and machine learning.

The Journal of asthma : official journal of the Association for the Care of Asthma
OBJECTIVE: Acute exacerbations contribute significantly to the morbidity of asthma. Recent studies have shown that early detection and treatment of asthma exacerbations leads to improved outcomes. We aimed to develop a machine learning algorithm to d...

Predicting the progression of mild cognitive impairment to Alzheimer's disease by longitudinal magnetic resonance imaging-based dictionary learning.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic res...

Predicting Alzheimer's disease progression using deep recurrent neural networks.

NeuroImage
Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more t...

Development of prognostic model for patients at CKD stage 3a and 3b in South Central China using computational intelligence.

Clinical and experimental nephrology
BACKGROUND: Chronic kidney disease (CKD) stage 3 was divided into two subgroups by eGFR (45 mL/ min 1.73 m). There is difference in prevalence of CKD, racial differences, economic development, genetic, and environmental backgrounds between China and ...

Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Asthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients' quality of life. However, efficient methods that can help identify personalized risk factors an...

AD risk score for the early phases of disease based on unsupervised machine learning.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Identifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention.