AIMC Topic: Aged, 80 and over

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Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification.

Human brain mapping Feb 25, 2019
Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diag...
Middle Aged Cognitive Dysfunction Models, Theoretical Alzheimer Disease Humans Machine Learning Aged Aged, 80 and over Male Diffusion Tensor Imaging Retrospective Studies Multiparametric Magnetic Resonance Imaging Independent Living Female
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A novel machine-learning-based infection screening system via 2013-2017 seasonal influenza patients' vital signs as training datasets.

The Journal of infection Feb 22, 2019
Adolescent Child Mass Screening Predictive Value of Tests Clinical Decision Rules Influenza, Human Vital Signs Adult Sensitivity and Specificity Humans Male Machine Learning Middle Aged Female Aged Aged, 80 and over Young Adult
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Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer's dementia diagnosis using multi-measure rs-fMRI spatial patterns.

PloS one Feb 22, 2019
BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) is essential for timely treatment. Machine learning and multivariate pattern analysis (MVPA) for the diagnosis of brain disorders are explicitly attracting at...
Aged, 80 and over Magnetic Resonance Imaging Humans Male Republic of Korea Brain Mapping Cognitive Dysfunction Female Alzheimer Disease Support Vector Machine Aged
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Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data.

Sensors (Basel, Switzerland) Feb 20, 2019
The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data...
Aged, 80 and over Male Quality of Life Exercise Machine Learning Algorithms Female Aged Wearable Electronic Devices Monitoring, Physiologic Humans Neural Networks, Computer
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Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients.

PloS one Feb 20, 2019
Currently in patients with bladder cancer, various clinical evaluations (imaging, operative findings at transurethral resection and radical cystectomy, pathology) are collectively used to determine disease status and prognosis, and recommend neoadjuv...
Models, Biological Disease-Free Survival Humans Cystectomy Female Machine Learning Survival Rate Aged Databases, Factual Male Aged, 80 and over Middle Aged Urinary Bladder Neoplasms
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Potential of a machine-learning model for dose optimization in CT quality assurance.

European radiology Feb 19, 2019
OBJECTIVES: To evaluate machine learning (ML) to detect chest CT examinations with dose optimization potential for quality assurance in a retrospective, cross-sectional study.
Retrospective Studies Female Aged Aged, 80 and over Young Adult Humans Machine Learning Multidetector Computed Tomography Adolescent Radiation Injuries Quality Assurance, Health Care Thoracic Diseases Adult Radiation Dosage Cross-Sectional Studies Radiography, Thoracic Male Middle Aged
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Qualitative versus quantitative lumbar spinal stenosis grading by machine learning supported texture analysis-Experience from the LSOS study cohort.

European journal of radiology Feb 19, 2019
PURPOSE: To investigate and compare the reproducibility and accuracy of qualitative ratings and quantitative texture analysis (TA) in detection and grading of lumbar spinal stenosis (LSS) in magnetic resonance imaging (MR) scans of the lumbar spine.
Aged Aged, 80 and over Lumbar Vertebrae Observer Variation Machine Learning Spinal Stenosis Male Humans Reference Standards Algorithms Cohort Studies Outcome Assessment, Health Care Middle Aged Female Magnetic Resonance Imaging Reproducibility of Results
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Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome.

BMC medical informatics and decision making Feb 18, 2019
BACKGROUND: Increasing life expectancy results in more elderly people struggling with age related diseases and functional conditions. This poses huge challenges towards establishing new approaches for maintaining health at a higher age. An important ...
Models, Theoretical Frailty Frail Elderly Data Mining Medical Informatics Applications Aged, 80 and over Male Aging Humans Health Surveys Female Risk Factors Aged Machine Learning Prognosis
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Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy.

Laboratory investigation; a journal of technical methods and pathology Feb 15, 2019
Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessm...
Aged, 80 and over Male Deep Learning Neuropathology Tauopathies Humans Brain Female Aged
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Random forest prediction of Alzheimer's disease using pairwise selection from time series data.

PloS one Feb 14, 2019
Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing...
Humans Image Interpretation, Computer-Assisted Pattern Recognition, Automated Neuroimaging Male Machine Learning Disease Progression Middle Aged Female Magnetic Resonance Imaging Aged Alzheimer Disease Aged, 80 and over
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