AIMC Topic: Aged, 80 and over

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Computer-assisted assessment of colonic polyp histopathology using probe-based confocal laser endomicroscopy.

International journal of colorectal disease Nov 6, 2019
INTRODUCTION: Probe-based confocal laser endomicroscopy (pCLE) is a promising modality for classifying polyp histology in vivo, but decision making in real-time is hampered by high-magnification targeting and by the learning curve for image interpret...
Aged Aged, 80 and over Male Middle Aged Colonoscopy Humans Colonic Neoplasms Machine Learning Female Tumor Burden Reproducibility of Results Colonic Polyps Image Interpretation, Computer-Assisted Observer Variation Diagnosis, Computer-Assisted Clinical Competence Microscopy, Confocal Predictive Value of Tests Feasibility Studies
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Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification.

European radiology Nov 5, 2019
OBJECTIVE: To evaluate the impact of utilizing digital breast tomosynthesis (DBT) or/and full-field digital mammography (FFDM), and different transfer learning strategies on deep convolutional neural network (DCNN)-based mass classification for breas...
Deep Learning Imaging, Three-Dimensional Mammography Diagnosis, Differential Radiographic Image Interpretation, Computer-Assisted Breast Radiographic Image Enhancement Aged, 80 and over Young Adult Adult Breast Neoplasms Humans Neural Networks, Computer Middle Aged Machine Learning Retrospective Studies Female Aged
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Statistical and machine learning methodology for abdominal aortic aneurysm prediction from ultrasound screenings.

Echocardiography (Mount Kisco, N.Y.) Nov 4, 2019
A method of analysis of a database of patients (n = 10 329) screened for an abdominal aortic aneurysm (AAA) is presented. Self-reported height, weight, age, gender, ethnicity, and parameters "Heart Problems," "Hypertension," "High Cholesterol," "Diab...
Aortic Aneurysm, Abdominal Age Distribution Sex Distribution Aged Aged, 80 and over Male Middle Aged Prevalence Incidence Ultrasonography United States Mass Screening Humans Follow-Up Studies Machine Learning Predictive Value of Tests Retrospective Studies Female Risk Factors
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Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine.

International journal of environmental research and public health Nov 3, 2019
This study developed a support vector machine (SVM) algorithm-based prediction model with considering influence factors associated with the swallowing quality-of-life as the predictor variables and provided baseline information for enhancing the swa...
Male Quality of Life Support Vector Machine Deglutition Disorders Deglutition Humans Republic of Korea Female Aged Aged, 80 and over Cross-Sectional Studies
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Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction.

Journal of healthcare engineering Nov 3, 2019
OBJECTIVE: Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU). A good clinical outcome is more probable when onset is suspected and treated on time, thus early insight of sepsis ons...
Vital Signs Male Middle Aged Case-Control Studies Diagnosis, Computer-Assisted Adolescent Female Humans Sepsis Support Vector Machine Aged Machine Learning Area Under Curve Software Aged, 80 and over Models, Biological Algorithms Early Diagnosis Young Adult Analysis of Variance Intensive Care Units Adult
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Machine-learned analysis of side-differences in odor identification performance.

Neuroscience Nov 2, 2019
A right-left dichotomy of olfactory processes has been recognized on several levels of the perception or processing of olfactory input. On a clinical level, the lateralization of components of human olfaction is contrasted by the predominantly birhin...
Humans Machine Learning Female Aged Adolescent Aged, 80 and over Functional Laterality Child, Preschool Young Adult Olfactory Perception Child Adult Olfactory Pathways Odorants Male Discrimination, Psychological Smell Middle Aged Olfaction Disorders
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A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study.

NeuroImage Nov 1, 2019
Predicting the progression of Alzheimer's Disease (AD) has been held back for decades due to the lack of sufficient longitudinal data required for the development of novel machine learning algorithms. This study proposes a novel machine learning algo...
Female Aged Aged, 80 and over Male Disease Progression Humans Machine Learning Positron-Emission Tomography Neuropsychological Tests Mental Status and Dementia Tests Cognitive Dysfunction Longitudinal Studies Magnetic Resonance Imaging Regression Analysis Alzheimer Disease
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Machine-learning-derived rules set excludes risk of Parkinson's disease in patients with olfactory or gustatory symptoms with high accuracy.

Journal of neurology Nov 1, 2019
BACKGROUND: Chemosensory loss is a symptom of Parkinson's disease starting already at preclinical stages. Their appearance without an identifiable etiology therefore indicates a possible early symptom of Parkinson's disease. Supervised machine-learni...
Olfaction Disorders Taste Disorders Case-Control Studies Adolescent Parkinson Disease Age of Onset Risk Humans Machine Learning Prospective Studies Female Reproducibility of Results Aged Aged, 80 and over Young Adult Adult Male Middle Aged
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A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score.

Journal of general internal medicine Nov 1, 2019
BACKGROUND: Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for cre...
Humans Artificial Intelligence Machine Learning Middle Aged Retrospective Studies Adolescent Aged Hospital Mortality Aged, 80 and over Triage Young Adult Emergency Service, Hospital Adult
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Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study.

European radiology experimental Nov 1, 2019
BACKGROUND: Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise A...
Humans Machine Learning Retrospective Studies Middle Aged Pilot Projects Female Diagnosis, Differential Aged Ultrasonography, Mammary Aged, 80 and over Image Interpretation, Computer-Assisted Adult Breast Diseases Breast Neoplasms
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