AIMC Topic: Disease Progression
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Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning.
BMC cardiovascular disorders
Aug 14, 2020
BACKGROUND: Subclinical diastolic dysfunction is a precursor for developing heart failure with preserved ejection fraction (HFpEF); yet not all patients progress to HFpEF. Our objective was to evaluate clinical and echocardiographic variables to iden...
Female
Risk Factors
Aged
Aged, 80 and over
Male
Humans
Biomarkers
Retrospective Studies
Disease Progression
Diagnosis, Computer-Assisted
Deep Learning
Cluster Analysis
Unsupervised Machine Learning
Ventricular Function, Left
Stroke Volume
Echocardiography
Middle Aged
Risk Assessment
Natriuretic Peptide, Brain
Asymptomatic Diseases
Diastole
Predictive Value of Tests
Peptide Fragments
Heart Failure
Ventricular Dysfunction, Left
Predicting Alzheimer's disease progression using deep recurrent neural networks.
NeuroImage
Aug 4, 2020
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
Aug 1, 2020
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
Jul 31, 2020
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
Jul 30, 2020
INTRODUCTION: Identifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention.
Application of a Robotic Tele-Echography System for COVID-19 Pneumonia.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Jul 29, 2020
To date, coronavirus disease 2019 (COVID-19) has infected millions of people worldwide. Ultrasound plays an indispensable role in the diagnosis, monitoring, and follow-up of patients with COVID-19. In this study, we used a robotic tele-echography sys...
Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer's Disease Neuroimaging (ADNI) database.
PloS one
Jul 27, 2020
The Alzheimer's Disease Neuroimaging (ADNI) database is an expansive undertaking by government, academia, and industry to pool resources and data on subjects at various stage of symptomatic severity due to Alzheimer's disease. As expected, magnetic r...
Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning.
Gut
Jul 20, 2020
OBJECTIVE: Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), hi...
From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans.
European radiology
Jul 18, 2020
OBJECTIVE: To develop a fully automated AI system to quantitatively assess the disease severity and disease progression of COVID-19 using thick-section chest CT images.
Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression.
Aging
Jul 9, 2020
Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer's disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathoge...
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