AIMC Topic: Disease Progression

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A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine Learning Methods.

IEEE transactions on nanobioscience May 24, 2018
This paper explored the hidden biomedical information from knee magnetic resonance (MR) images for osteoarthritis (OA) prediction. We have computed the cartilage damage index (CDI) information from 36 informative locations on tibiofemoral cartilage c...
Humans Disease Progression Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Osteoarthritis, Knee Cartilage, Articular Machine Learning Female Aged Male Middle Aged
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Phenotypic Clustering of Left Ventricular Diastolic Function Parameters: Patterns and Prognostic Relevance.

JACC. Cardiovascular imaging Apr 18, 2018
OBJECTIVES: This study sought to explore the natural clustering of echocardiographic variables used for assessing left ventricular (LV) diastolic dysfunction (DD) in order to isolate high-risk phenotypic patterns and assess their prognostic significa...
Pattern Recognition, Automated Diastole Echocardiography, Doppler, Color Echocardiography, Doppler, Pulsed Heart Ventricles Progression-Free Survival Ventricular Dysfunction, Left Image Interpretation, Computer-Assisted Aged Aged, 80 and over Male Ventricular Function, Left Phenotype Middle Aged Retrospective Studies Risk Assessment Female Disease Progression Risk Factors Humans Cluster Analysis Hospitalization Predictive Value of Tests Cause of Death Machine Learning
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A Multi-Task Framework for Monitoring Health Conditions via Attention-based Recurrent Neural Networks.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
Monitoring the future health status of patients from the historical Electronic Health Record (EHR) is a core research topic in predictive healthcare. The most important challenges are to model the temporality of sequential EHR data and to interpret t...
Humans Neural Networks, Computer Electronic Health Records Disease Progression Deep Learning Patient Care Management
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Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] Mar 20, 2018
BACKGROUND AND PURPOSE: Current prognostic models for soft tissue sarcoma (STS) patients are solely based on staging information. Treatment-related data have not been included to date. Including such information, however, could help to improve these ...
Neoadjuvant Therapy Neoplasm Staging Sarcoma Proportional Hazards Models Middle Aged Risk Assessment Disease Progression Cohort Studies Machine Learning Prognosis Retrospective Studies Female Aged Aged, 80 and over Adult Survival Rate Male Humans
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Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative.

Osteoarthritis and cartilage Mar 9, 2018
OBJECTIVE: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof.
Humans Time Factors Follow-Up Studies Magnetic Resonance Imaging Predictive Value of Tests Osteoarthritis, Knee Neural Networks, Computer Female Male Middle Aged Disease Progression Cartilage, Articular Menisci, Tibial
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Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

Journal of neuroscience methods Feb 24, 2018
BACKGROUND: There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different d...
Magnetic Resonance Imaging Decision Trees Principal Component Analysis Alzheimer Disease Image Interpretation, Computer-Assisted Brain Humans Female Pattern Recognition, Automated Machine Learning Male Disease Progression Cognitive Dysfunction
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Predicting Visual Acuity by Using Machine Learning in Patients Treated for Neovascular Age-Related Macular Degeneration.

Ophthalmology Feb 14, 2018
PURPOSE: To predict, by using machine learning, visual acuity (VA) at 3 and 12 months in patients with neovascular age-related macular degeneration (AMD) after initial upload of 3 anti-vascular endothelial growth factor (VEGF) injections.
Humans Machine Learning Algorithms Databases, Factual Male Female Visual Acuity Middle Aged Reproducibility of Results Vascular Endothelial Growth Factor A Disease Progression Aged Angiogenesis Inhibitors Intravitreal Injections Aged, 80 and over Follow-Up Studies Wet Macular Degeneration Choroidal Neovascularization
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A Systematic Machine Learning Based Approach for the Diagnosis of Non-Alcoholic Fatty Liver Disease Risk and Progression.

Scientific reports Feb 1, 2018
Prevention and diagnosis of NAFLD is an ongoing area of interest in the healthcare community. Screening is complicated by the fact that the accuracy of noninvasive testing lacks specificity and sensitivity to make and stage the diagnosis. Currently n...
Humans Canada Machine Learning Body Mass Index Female Metabolic Syndrome Male Risk Factors Non-alcoholic Fatty Liver Disease Middle Aged Adult Disease Progression Prevalence
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Machine-learning neuroimaging challenge for automated diagnosis of mild cognitive impairment: Lessons learnt.

Journal of neuroscience methods Jan 2, 2018
Brain Disease Progression Cognitive Dysfunction Alzheimer Disease Image Interpretation, Computer-Assisted Pattern Recognition, Automated Humans Neuroimaging Machine Learning Databases, Factual
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Changes in glomerular filtration rate and outcomes in patients with atrial fibrillation.

American heart journal Dec 30, 2017
BACKGROUND: Patients with kidney disease are more likely to develop atrial fibrillation (AF) than individuals with normal renal function, and more likely to suffer ischemic stroke (IS)/thromboembolism (TE). We investigated the relationship of kidney ...
Comorbidity Proportional Hazards Models Severity of Illness Index Cohort Studies Survival Analysis Aged, 80 and over Glomerular Filtration Rate Treatment Outcome Drug Therapy, Combination Male Follow-Up Studies Confidence Intervals Middle Aged Fibrinolytic Agents Humans Renal Insufficiency Risk Assessment Anti-Arrhythmia Agents Retrospective Studies Disease Progression Atrial Fibrillation Female Aged
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