AIMC Topic: Aged, 80 and over

Clear Filters Showing 1811 to 1820 of 3475 articles

Using machine learning to predict early readmission following esophagectomy.

The Journal of thoracic and cardiovascular surgery May 29, 2020
OBJECTIVE: To establish a machine learning (ML)-based prediction model for readmission within 30 days (early readmission or early readmission) of patients based on their profile at index hospitalization for esophagectomy.
Adolescent Young Adult Humans Adult Machine Learning Models, Statistical Male Retrospective Studies Decision Trees Middle Aged Female Esophagectomy Length of Stay Aged Patient Readmission Aged, 80 and over
View on PubMed DOI

Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan.

PloS one May 29, 2020
BACKGROUND: Although dialysis patients are at a high risk of death, it is difficult for medical practitioners to simultaneously evaluate many inter-related risk factors. In this study, we evaluated the characteristics of hemodialysis patients using m...
Adult Prognosis Male Female Middle Aged Cohort Studies Humans Decision Making, Computer-Assisted Risk Factors Deep Learning Artificial Intelligence Aged Logistic Models Renal Dialysis Machine Learning Aged, 80 and over Support Vector Machine Databases, Factual Cluster Analysis Prospective Studies Kidney Failure, Chronic Japan
View on PubMed DOI

Emerging Pharmacotherapy and Health Care Needs of Patients in the Age of Artificial Intelligence and Digitalization.

The Annals of pharmacotherapy May 28, 2020
Advances in the application of artificial intelligence, digitization, technology, iCloud computing, and wearable devices in health care predict an exciting future for health care professionals and our patients. Projections suggest an older, generally...
Humans Artificial Intelligence Delivery of Health Care Female Aged, 80 and over Aged Male Decision Support Systems, Clinical Telemedicine Digital Technology Drug Therapy
View on PubMed DOI

Deep learning COVID-19 detection bias: accuracy through artificial intelligence.

International orthopaedics May 27, 2020
BACKGROUND: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported re...
Middle Aged Deep Learning Adolescent Child Bias Coronavirus Infections Humans SARS-CoV-2 Betacoronavirus Neural Networks, Computer Aged Pneumonia, Viral Pandemics Aged, 80 and over COVID-19 Young Adult Female Adult Male
View on PubMed DOI

Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT.

Radiology May 26, 2020
Background Deep learning may help to improve computer-aided detection of volume (CADv) measurement of pulmonary nodules at chest CT. Purpose To determine the efficacy of a deep learning method for improving CADv for measuring the solid and ground-gla...
Multiple Pulmonary Nodules Sensitivity and Specificity Male Tomography, X-Ray Computed Middle Aged Female Lung Humans Aged Lung Neoplasms Neural Networks, Computer Aged, 80 and over Retrospective Studies Adult
View on PubMed DOI

Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2.

Ophthalmology May 21, 2020
PURPOSE: To develop deep learning models for detecting reticular pseudodrusen (RPD) using fundus autofluorescence (FAF) images or, alternatively, color fundus photographs (CFP) in the context of age-related macular degeneration (AMD).
Ophthalmologists Retinal Drusen Area Under Curve Macular Degeneration Datasets as Topic Optical Imaging Fluorescein Angiography Humans Female Reproducibility of Results Aged Aged, 80 and over Sensitivity and Specificity Male Middle Aged Deep Learning ROC Curve
View on PubMed DOI

Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies.

Human brain mapping May 21, 2020
PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized f...
Humans Tomography, X-Ray Computed Female Middle Aged Deep Learning Cognitive Dysfunction Radiopharmaceuticals Aniline Compounds Positron-Emission Tomography Aged Aged, 80 and over Young Adult Adult Male Fluorodeoxyglucose F18 Neuroimaging Benzothiazoles Carbolines Dihydroxyphenylalanine
View on PubMed DOI

Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.

PloS one May 19, 2020
OBJECTIVE: Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to pre...
Female Risk Factors Aged Aged, 80 and over Male Humans Machine Learning Algorithms Retrospective Studies Logistic Models Nonlinear Dynamics Hip Fractures Germany Osteoporotic Fractures
View on PubMed DOI

Predicting conversion to wet age-related macular degeneration using deep learning.

Nature medicine May 18, 2020
Progression to exudative 'wet' age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the ...
Male Humans Macular Degeneration Disease Progression Prognosis Early Diagnosis Deep Learning Female Wet Macular Degeneration Imaging, Three-Dimensional Aged Early Medical Intervention Tomography, Optical Coherence Aged, 80 and over Geographic Atrophy
View on PubMed DOI

Unsupervised Cluster Analysis of Patients With Aortic Stenosis Reveals Distinct Population With Different Phenotypes and Outcomes.

Circulation. Cardiovascular imaging May 18, 2020
BACKGROUND: There is a lack of studies investigating the heterogeneity of patients with aortic stenosis (AS). We explored whether cluster analysis identifies distinct subgroups with different prognostic significances in AS.
Reproducibility of Results Risk Factors Aged Aged, 80 and over Male Echocardiography Decision Support Techniques Phenotype Prospective Studies Prognosis Female Middle Aged Comorbidity Severity of Illness Index Hemodynamics Age Factors Cluster Analysis Aortic Valve Stenosis Aortic Valve Unsupervised Machine Learning Predictive Value of Tests Cause of Death Humans
View on PubMed DOI
Popular Topics
Recent Journals