AIMC Topic: Aged, 80 and over

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An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging.

Nature aging Jul 12, 2021
While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8-96 years, we developed a deep-learning meth...
Animals Aging Deep Learning Mice Inflammation Humans Aged, 80 and over Frailty Endothelial Cells Multimorbidity Immunosenescence
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The cardiovascular phenotype of Chronic Obstructive Pulmonary Disease (COPD): Applying machine learning to the prediction of cardiovascular comorbidities.

Respiratory medicine Jul 7, 2021
BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous group of lung conditions that are challenging to diagnose and treat. As the presence of comorbidities often exacerbates this scenario, the characterization of patients with C...
Disease Progression Prevalence Comorbidity Cluster Analysis Machine Learning Phenotype Aged, 80 and over Retrospective Studies Pulmonary Disease, Chronic Obstructive Female Male Aged Cardiovascular Diseases Humans
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Prediction Model Using Machine Learning for Mortality in Patients with Heart Failure.

The American journal of cardiology Jul 7, 2021
Heart Failure (HF) is a major cause of morbidity and mortality in the US. With aging of the US population, the public health burden of HF is enormous. We aimed to develop an ensemble prediction model for 30-day mortality after discharge using machine...
Machine Learning Female Hospitalization Aged Aged, 80 and over Male Middle Aged Cause of Death Heart Failure Mortality Humans Clinical Decision Rules
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A comparative study of perioperative and survival outcomes of robot-assisted radical cystectomy in patients over 80 and under 80 years old.

World journal of surgical oncology Jul 6, 2021
BACKGROUND: Radical cystectomy (RC) is the standard treatment for bladder cancer, but the safety and efficacy of this treatment for elderly people need to be considered. We compare perioperative data and survival outcomes between elderly (≥80 years) ...
Robotic Surgical Procedures Carcinoma, Transitional Cell Humans Cystectomy Prognosis Retrospective Studies Aged Aged, 80 and over Treatment Outcome Urinary Bladder Neoplasms Robotics
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Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort.

PLoS computational biology Jul 6, 2021
Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse e...
Anti-Inflammatory Agents, Non-Steroidal Drug Interactions Chemical and Drug Induced Liver Injury Aged Aged, 80 and over Young Adult Adult Male Middle Aged Adolescent Computational Biology Liver Models, Statistical Machine Learning Algorithms Retrospective Studies Electronic Health Records Female Humans
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Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial.

Journal of gastroenterology Jul 3, 2021
BACKGROUND: We have developed the computer-aided detection (CADe) system using an original deep learning algorithm based on a convolutional neural network for assisting endoscopists in detecting colorectal lesions during colonoscopy. The aim of this ...
Female Japan Aged Humans Aged, 80 and over Artificial Intelligence Middle Aged Adult Male Colonoscopy Early Detection of Cancer Adenoma Robotic Surgical Procedures
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Self-Supervised Feature Learning and Phenotyping for Assessing Age-Related Macular Degeneration Using Retinal Fundus Images.

Ophthalmology. Retina Jul 2, 2021
OBJECTIVE: Diseases such as age-related macular degeneration (AMD) are classified based on human rubrics that are prone to bias. Supervised neural networks trained using human-generated labels require labor-intensive annotations and are restricted to...
Prospective Studies Female Reproducibility of Results Aged Aged, 80 and over Humans Neural Networks, Computer Algorithms Tomography, Optical Coherence Fundus Oculi Retina Macular Degeneration Fluorescein Angiography Male Middle Aged Severity of Illness Index Deep Learning Follow-Up Studies
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A machine learning based exploration of COVID-19 mortality risk.

PloS one Jul 2, 2021
Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invas...
Risk Symptom Assessment Young Adult Electronic Health Records Adult Female Comorbidity Male Humans SARS-CoV-2 Severity of Illness Index Middle Aged Aged Pandemics Models, Theoretical Aged, 80 and over COVID-19 Support Vector Machine Forecasting Triage
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Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia.

Clinical imaging Jul 1, 2021
PURPOSE: Comparison of deep learning algorithm, radiomics and subjective assessment of chest CT for predicting outcome (death or recovery) and intensive care unit (ICU) admission in patients with severe acute respiratory syndrome coronavirus 2 (SARS-...
Lung Deep Learning Tomography, X-Ray Computed SARS-CoV-2 Humans Aged COVID-19 Aged, 80 and over Retrospective Studies Middle Aged
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Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study.

The Lancet. Digital health Jul 1, 2021
BACKGROUND: Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray interpretati...
Artificial Intelligence Thoracic Injuries Retrospective Studies Female Aged Humans Deep Learning Adolescent ROC Curve Thoracic Neoplasms Mass Screening Aged, 80 and over Young Adult Adult Male Middle Aged Area Under Curve Radiologists Radiography, Thoracic Radiographic Image Interpretation, Computer-Assisted Infections Models, Biological X-Rays
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