AIMC Topic: Body Mass Index

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Profiling movement behaviours in pre-school children: A self-organised map approach.

Journal of sports sciences Nov 7, 2019
Application of machine learning techniques has the potential to yield unseen insights into movement and permits visualisation of complex behaviours and tangible profiles. The aim of this study was to identify profiles of relative motor competence (MC...
Child, Preschool Body Mass Index Accelerometry Fitness Trackers Movement Motor Activity Humans Machine Learning Female Cross-Sectional Studies Male Exercise
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Predicting atrial fibrillation in primary care using machine learning.

PloS one Nov 1, 2019
BACKGROUND: Atrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of t...
Risk Assessment Primary Health Care Body Mass Index Risk Factors Humans Age Factors Antihypertensive Agents Aged Blood Pressure Neural Networks, Computer Atrial Fibrillation Adult Machine Learning Male Retrospective Studies Cardiovascular Diseases Female Middle Aged
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Machine learning of human plasma lipidomes for obesity estimation in a large population cohort.

PLoS biology Oct 18, 2019
Obesity is associated with changes in the plasma lipids. Although simple lipid quantification is routinely used, plasma lipids are rarely investigated at the level of individual molecules. We aimed at predicting different measures of obesity based on...
Humans Machine Learning Biomarkers Waist-Hip Ratio Finland Models, Statistical Female Body Fat Distribution Adipose Tissue Male Sphingomyelins Lipid Metabolism Cohort Studies Sex Factors Body Mass Index Waist Circumference Obesity Lipidomics
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Predictive models for diabetes mellitus using machine learning techniques.

BMC endocrine disorders Oct 15, 2019
BACKGROUND: Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body's inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to be...
ROC Curve Canada Diabetes Mellitus Body Mass Index Follow-Up Studies Models, Statistical Aged, 80 and over Young Adult Adult Male Middle Aged Humans Machine Learning Biomarkers Prognosis Female Risk Factors Aged
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The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes.

Artificial intelligence in medicine Aug 21, 2019
Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. ...
Decision Support Systems, Clinical Diabetes Mellitus, Type 2 Body Mass Index Body Weight Probability Waist Circumference Body Height Speech Recognition Software Female Male Middle Aged Deep Learning Surveys and Questionnaires User-Computer Interface Humans Neural Networks, Computer Artificial Intelligence
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Construct validation of machine learning in the prediction of short-term postoperative complications following total shoulder arthroplasty.

Journal of shoulder and elbow surgery Aug 3, 2019
BACKGROUND: We aimed to demonstrate that supervised machine learning (ML) models can better predict postoperative complications after total shoulder arthroplasty (TSA) than comorbidity indices.
Comorbidity Length of Stay ROC Curve Body Mass Index Age Factors Blood Transfusion Female Aged Male Middle Aged Postoperative Complications Reoperation Serum Albumin Arthroplasty, Replacement, Shoulder Operative Time Predictive Value of Tests Supervised Machine Learning Hematocrit Patient Readmission Smoking Humans Forecasting Databases, Factual
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Variation in the Thickness of Knee Cartilage. The Use of a Novel Machine Learning Algorithm for Cartilage Segmentation of Magnetic Resonance Images.

The Journal of arthroplasty Jul 24, 2019
BACKGROUND: The variation in articular cartilage thickness (ACT) in healthy knees is difficult to quantify and therefore poorly documented. Our aims are to (1) define how machine learning (ML) algorithms can automate the segmentation and measurement ...
Knee Joint Multivariate Analysis Femur Osteoarthritis, Knee Magnetic Resonance Spectroscopy Cartilage, Articular Middle Aged Disease Progression Image Processing, Computer-Assisted Body Mass Index Humans Magnetic Resonance Imaging Machine Learning Algorithms Female Aged Male
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Disability in multiple sclerosis is associated with age and inflammatory, metabolic and oxidative/nitrosative stress biomarkers: results of multivariate and machine learning procedures.

Metabolic brain disease Jul 13, 2019
The aim of this study was to evaluate the immune-inflammatory, metabolic, and nitro-oxidative stress (IM&NO) biomarkers as predictors of disability in multiple sclerosis (MS) patients. A total of 122 patients with MS were included; their disability w...
Humans Machine Learning Biomarkers Female Adult Male Middle Aged Oxidative Stress Inflammation Body Mass Index Age Factors Nitrosative Stress Disability Evaluation Uric Acid Homocysteine Multiple Sclerosis Cholesterol, HDL
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Demographic, psychological, behavioral, and cognitive correlates of BMI in youth: Findings from the Adolescent Brain Cognitive Development (ABCD) study.

Psychological medicine Jul 10, 2019
BACKGROUND: Previous research has implicated demographic, psychological, behavioral, and cognitive variables in the onset and maintenance of pediatric overweight/obesity. No adequately-powered study has simultaneously modeled these variables to asses...
Health Status Disparities Demography Adolescent Child United States Humans Cognition Adolescent Behavior Machine Learning Overweight Obesity Female Body Mass Index Body Weight Risk Factors Sex Factors Male Linear Models Multivariate Analysis
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SmartPulse, a machine learning approach for calibration-free dynamic RF shimming: Preliminary study in a clinical environment.

Magnetic resonance in medicine Jun 30, 2019
PURPOSE: A calibration-free pulse design method is introduced to alleviate artifacts in clinical routine with parallel transmission at high field, dealing with significant inter-subject variability, found for instance in the abdomen.
Male Middle Aged Image Processing, Computer-Assisted Humans Abdomen Machine Learning Body Mass Index Algorithms Cluster Analysis Magnetic Resonance Imaging Female Image Interpretation, Computer-Assisted Reproducibility of Results Artifacts Aged Calibration Aged, 80 and over Radio Waves Young Adult Adult
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