AIMC Topic: Disease Progression
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Does including machine learning predictions in ALS clinical trial analysis improve statistical power?
Annals of clinical and translational neurology
Aug 30, 2020
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
Aug 14, 2020
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
Aug 14, 2020
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...
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...
Natriuretic Peptide, Brain
Asymptomatic Diseases
Diastole
Predictive Value of Tests
Peptide Fragments
Heart Failure
Ventricular Dysfunction, Left
Stroke Volume
Echocardiography
Middle Aged
Risk Assessment
Disease Progression
Diagnosis, Computer-Assisted
Deep Learning
Cluster Analysis
Unsupervised Machine Learning
Risk Factors
Aged
Aged, 80 and over
Male
Ventricular Function, Left
Humans
Biomarkers
Retrospective Studies
Female
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...
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