AIMC Topic: Longitudinal Studies

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Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS).

Drug and chemical toxicology
Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utiliz...

Deep learning(s) in gaming disorder through the user-avatar bond: A longitudinal study using machine learning.

Journal of behavioral addictions
BACKGROUND AND AIMS: Gaming disorder [GD] risk has been associated with the way gamers bond with their visual representation (i.e., avatar) in the game-world. More specifically, a gamer's relationship with their avatar has been shown to provide relia...

A machine learning approach to identify important variables for distinguishing between fallers and non-fallers in older women.

PloS one
Falls are a significant ongoing public health concern for older adults. At present, few studies have concurrently explored the influence of multiple measures when seeking to determine which variables are most predictive of fall risks. As such, this c...

Predicting mortality in brain stroke patients using neural networks: outcomes analysis in a longitudinal study.

Scientific reports
In this study, Neural Networks (NN) modelling has emerged as a promising tool for predicting outcomes in patients with Brain Stroke (BS) by identifying key risk factors. In this longitudinal study, we enrolled 332 patients form Imam hospital in Ardab...

Electroencephalographic abnormalities in children with type 1 diabetes mellitus: a prospective study.

Turkish journal of medical sciences
BACKGROUND/AIM: The aim herein was to investigate epileptiform discharges on electroencephalogram (EEG), their correlation with glutamic acid decarboxylase 65 autoantibody (GAD-ab) in newly diagnosed pediatric type 1 diabetes mellitus (T1DM) patients...

An improved multiply robust estimator for the average treatment effect.

BMC medical research methodology
BACKGROUND: In observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the inverse probability weighting and g-computation for estimating the average treatment effect (ATE). How...

Detecting changes in the performance of a clinical machine learning tool over time.

EBioMedicine
BACKGROUND: Excessive use of blood cultures (BCs) in Emergency Departments (EDs) results in low yields and high contamination rates, associated with increased antibiotic use and unnecessary diagnostics. Our team previously developed and validated a m...

A deep sift convolutional neural networks for total brain volume estimation from 3D ultrasound images.

Computer methods and programs in biomedicine
Preterm infants are a highly vulnerable population. The total brain volume (TBV) of these infants can be accurately estimated by brain ultrasound (US) imaging which enables a longitudinal study of early brain growth during Neonatal Intensive Care (NI...

Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment.

European archives of psychiatry and clinical neuroscience
Ecological momentary assessment (EMA), a structured diary assessment technique, has shown feasibility to capture psychotic(-like) symptoms across different study groups. We investigated whether EMA combined with unsupervised machine learning can dist...

Explainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid condition.

Scientific reports
This study uses artificial intelligence for testing (1) whether the comorbidity of diabetes and its comorbid condition is very strong in the middle-aged or old (hypothesis 1) and (2) whether major determinants of the comorbidity are similar for diffe...