AIMC Topic: Young Adult

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Impact of age at onset on the phenomenology of depression in treatment-seeking adults in the STAR*D trial.

Journal of affective disorders Oct 26, 2019
BACKGROUND: - Adolescence is characterized by biological, emotional, and behavioral changes. The onset of depression during this vulnerable time may confer specific risks. This study examined whether symptoms of depression were associated with age at...
Humans Neural Networks, Computer Prospective Studies Retrospective Studies Female Young Adult Adult Child Male Middle Aged Regression Analysis Severity of Illness Index Randomized Controlled Trials as Topic Depression Age of Onset Adolescent Patient Acceptance of Health Care
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Predictors of adherence to nicotine replacement therapy: Machine learning evidence that perceived need predicts medication use.

Drug and alcohol dependence Oct 25, 2019
BACKGROUND: Nonadherence to smoking cessation medication is a frequent problem. Identifying pre-quit predictors of nonadherence may help explain nonadherence and suggest tailored interventions to address it.
Tobacco Smoking Tobacco Use Cessation Devices Tobacco Use Disorder Smoking Cessation Counseling Needs Assessment Behavior Therapy Humans Machine Learning Female Young Adult Adult Male Middle Aged Medication Adherence Nicotine Motivation Smokers
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Development and validation of deep learning algorithms for scoliosis screening using back images.

Communications biology Oct 25, 2019
Adolescent idiopathic scoliosis is the most common spinal disorder in adolescents with a prevalence of 0.5-5.2% worldwide. The traditional methods for scoliosis screening are easily accessible but require unnecessary referrals and radiography exposur...
Scoliosis Male Deep Learning Adolescent Child Mass Screening Humans Algorithms Female Young Adult
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Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Oct 24, 2019
Detection and quantification of functional deficits due to moderate traumatic brain injury (mTBI) is crucial for clinical decision-making and timely commencement of functional therapy. In this work, we explore magnetoencephalography (MEG) based funct...
Sensitivity and Specificity Male Adolescent Support Vector Machine Magnetic Resonance Imaging Brain Injuries, Traumatic Automation Neural Pathways Magnetoencephalography Beta Rhythm Glasgow Coma Scale Humans Algorithms Databases, Factual Brain Female Young Adult Adult
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Machine Learning Identifies Large-Scale Reward-Related Activity Modulated by Dopaminergic Enhancement in Major Depression.

Biological psychiatry. Cognitive neuroscience and neuroimaging Oct 22, 2019
BACKGROUND: Theoretical models have emphasized systems-level abnormalities in major depressive disorder (MDD). For unbiased yet rigorous evaluations of pathophysiological mechanisms underlying MDD, it is critically important to develop data-driven ap...
Reward Dopamine Antidepressive Agents, Second-Generation Amisulpride Adult Humans Male Machine Learning Depressive Disorder, Major Female Depression Young Adult Double-Blind Method
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Machine learning for prediction of septic shock at initial triage in emergency department.

Journal of critical care Oct 22, 2019
BACKGROUND: We hypothesized utilizing machine learning (ML) algorithms for screening septic shock in ED would provide better accuracy than qSOFA or MEWS.
Triage Area Under Curve Emergency Service, Hospital Multivariate Analysis Vital Signs Middle Aged Sepsis Hospital Mortality Shock, Septic Support Vector Machine Machine Learning Software Algorithms Prognosis Electronic Health Records Humans Republic of Korea Female Aged Young Adult Adult Male
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Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder.

Drug and alcohol dependence Oct 22, 2019
BACKGROUND: Substance use disorder (SUD) exacts enormous societal costs in the United States, and it is important to detect high-risk youths for prevention. Machine learning (ML) is the method to find patterns and make prediction from data. We hypoth...
Psychological Techniques Humans Machine Learning Female Reproducibility of Results Young Adult Child Adult ROC Curve Male Longitudinal Studies Severity of Illness Index Predictive Value of Tests Adolescent Substance-Related Disorders Outcome Assessment, Health Care
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Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 22, 2019
INTRODUCTION: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms th...
Humans Neural Networks, Computer Algorithms Middle Aged Deep Learning Adolescent Head and Neck Neoplasms Neck Neoplasm Staging Female Aged Young Adult Adult Male Organs at Risk Radiotherapy Planning, Computer-Assisted Observer Variation
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SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis.

NeuroImage. Clinical Oct 22, 2019
Machine learning classification is an attractive approach to automatically differentiate patients from healthy subjects, and to predict future disease outcomes. A clinically isolated syndrome (CIS) is often the first presentation of multiple sclerosi...
Support Vector Machine Gray Matter Male Humans Retrospective Studies Follow-Up Studies White Matter Middle Aged Magnetic Resonance Imaging Female Multiple Sclerosis Young Adult Neuroimaging Adult Disease Progression Adolescent Image Processing, Computer-Assisted
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Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death.

Clinical cancer research : an official journal of the American Association for Cancer Research Oct 21, 2019
PURPOSE: Biomarkers for disease-specific survival (DSS) in early-stage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural netwo...
Melanoma Risk Factors Aged Aged, 80 and over Young Adult Adult Image Processing, Computer-Assisted Survival Rate Staining and Labeling Humans Male Follow-Up Studies Neural Networks, Computer Middle Aged Area Under Curve Algorithms Disease Progression Biopsy Retrospective Studies Deep Learning Neoplasm Recurrence, Local Female
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