AIMC Topic: Longitudinal Studies

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Using machine learning to predict poor adherence to antiretroviral therapy among adolescents with HIV in low resource settings.

AIDS (London, England)
OBJECTIVES: Achieving optimal adherence to antiretroviral therapy (ART) and viral suppression is still insufficient for attaining the UNAIDS 95-95-95 target of 2030, especially among adolescents with HIV (AWHIV). This study sought to develop a model ...

Deep learning to quantify the pace of brain aging in relation to neurocognitive changes.

Proceedings of the National Academy of Sciences of the United States of America
Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since bi...

Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain.

Proceedings of the National Academy of Sciences of the United States of America
Longitudinal imaging data are routinely acquired for health studies and patient monitoring. A central goal in longitudinal studies is tracking relevant change over time. Traditional methods remove nuisance variation with custom pipelines to focus on ...

Prediction of early-onset bipolar using electronic health records.

Journal of child psychology and psychiatry, and allied disciplines
BACKGROUND: Early identification of bipolar disorder (BD) provides an important opportunity for timely intervention. In this study, we aimed to develop machine learning models using large-scale electronic health record (EHR) data including clinical n...

Comparison of logistic regression and machine learning methods for predicting depression risks among disabled elderly individuals: results from the China Health and Retirement Longitudinal Study.

BMC psychiatry
BACKGROUND: Given the accelerated aging population in China, the number of disabled elderly individuals is increasing, and depression is a common mental disorder among older adults. This study aims to establish an effective model for predicting depre...

Prediction of cognitive conversion within the Alzheimer's disease continuum using deep learning.

Alzheimer's research & therapy
BACKGROUND: Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) is important to timely assignment to optimal treatment modes. We aimed to develop a deep learning model to predict cognitive conversion to guide re-as...

Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement longitudinal study (CHARLS).

BMC public health
BACKGROUND: Due to the ageing population and evolving lifestyles occurring in China, middle-aged and elderly populations have become high-risk groups for cardiovascular disease (CVD). The aim of this study was to analyse the incidence characteristics...

Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.

The journal of prevention of Alzheimer's disease
BACKGROUND: Mild cognitive impairment (MCI) and preclinical MCI (e.g., subjective cognitive decline, SCD) are considered risk states of dementia, such as Alzheimer's Disease (AD). However, it is challenging to accurately predict conversion from norma...

The relationship between activities of daily living and speech impediments based on evidence from statistical and machine learning analyses.

Frontiers in public health
INTRODUCTION: Speech impediments (SIs) are increasingly prevalent among middle-aged and older adults, raising concerns within public health. Early detection of potential SI in this demographic is critical. This study investigates the potential of Act...