AIMC Topic: Longitudinal Studies

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Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia.

International journal of geriatric psychiatry
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
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 ...

Adolescent binge drinking disrupts normal trajectories of brain functional organization and personality maturation.

NeuroImage. Clinical
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
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...

Predicting personalized process-outcome associations in psychotherapy using machine learning approaches-A demonstration.

Psychotherapy research : journal of the Society for Psychotherapy Research
Personalized treatment methods have shown great promise in efficacy studies across many fields of medicine and mental health. Little is known, however, about their utility in process-outcome research. This study is the first to apply personalized tr...

Using machine learning to translate applicant work history into predictors of performance and turnover.

The Journal of applied psychology
Work history information reflected in resumes and job application forms is commonly used to screen job applicants; however, there is little consensus as to how to systematically translate information about one's work-related past into predictors of f...

Psychological characteristics and stress differentiate between high from low health trajectories in later life: a machine learning analysis.

Aging & mental health
: This study set out to empirically identify joint health trajectories in individuals of advanced age. Predictors of subgroup allocation were investigated to identify the impact of psychological characteristics, stress, and socio-demographic variable...

DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning.

Scientific reports
Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging a...

Electroconvulsive Therapy Induces Cortical Morphological Alterations in Major Depressive Disorder Revealed with Surface-Based Morphometry Analysis.

International journal of neural systems
Although electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder (MDD), the mechanism underlying the therapeutic efficacy and side effects of ECT remains poorly understood. Here, we investigated alteratio...