AIMC Topic: Longitudinal Studies
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Using Machine Learning to Identify Suicide Risk: A Classification Tree Approach to Prospectively Identify Adolescent Suicide Attempters.
Archives of suicide research : official journal of the International Academy for Suicide Research
Jun 10, 2019
This study applies classification tree analysis to prospectively identify suicide attempters among a large adolescent community sample, to demonstrate the strengths and limitations of this approach for risk identification. Data were drawn from the Na...
Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning.
Human brain mapping
Jun 5, 2019
Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional biomarkers. To properly realize their potential, b...
Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury.
Scientific reports
May 28, 2019
Muscle fat infiltration (MFI) of the deep cervical spine extensors has been observed in cervical spine conditions using time-consuming and rater-dependent manual techniques. Deep learning convolutional neural network (CNN) models have demonstrated st...
Identifying predictors of within-person variance in MRI-based brain volume estimates.
NeuroImage
May 18, 2019
Adequate reliability of measurement is a precondition for investigating individual differences and age-related changes in brain structure. One approach to improve reliability is to identify and control for variables that are predictive of within-pers...
Predicting childhood obesity using electronic health records and publicly available data.
PloS one
Apr 22, 2019
BACKGROUND: Because of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective strategies are needed to address this condition in early childhood. ...
Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia.
International journal of geriatric psychiatry
Apr 15, 2019
OBJECTIVES: The overall aim of the present study was to explore the role of cognitive reserve (CR) in the conversion from mild cognitive impairment (MCI) to dementia. We used traditional and machine learning (ML) techniques to compare converter and n...
Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data.
IEEE transactions on bio-medical engineering
Apr 3, 2019
Currently available risk prediction methods are limited in their ability to deal with complex, heterogeneous, and longitudinal data such as that available in primary care records, or in their ability to deal with multiple competing risks. This paper ...
Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning.
Pediatric research
Mar 31, 2019
BACKGROUND: Machine learning models may enhance the early detection of clinically relevant hyperbilirubinemia based on patient information available in every hospital.
Adolescent binge drinking disrupts normal trajectories of brain functional organization and personality maturation.
NeuroImage. Clinical
Mar 31, 2019
Adolescent binge drinking has been associated with higher risks for the development of many health problems throughout the lifespan. Adolescents undergo multiple changes that involve the co-development processes of brain, personality and behavior; th...
Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach.
BMC medical research methodology
Mar 29, 2019
BACKGROUND: Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are underappreciated but pose a significant public health burden. Efforts for prevention and management could be supported by an understanding of the relative...
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