AIMC Topic: Longitudinal Studies

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[Cluster predictors of trajectories of leisure-time physical activity intensity in men and women from ELSA-Brasil].

Cadernos de saude publica
The maintenance of physical activity over time is a challenge for public health. Predictors of different physical activity intensities have not been sufficiently analyzed. This study aimed to identify clusters of trajectories of physical activity int...

Predicting children's emotional and behavioral difficulties at age five using pregnancy and newborn risk factors: Evidence from the UK Household Longitudinal Study.

Journal of affective disorders
Childhood emotional and behavioral difficulties have a profound impact on later life outcomes, making it crucial to identify early-life risk factors that predict emotional and behavioral difficulties. However, much of the existing research has concen...

Using Machine Learning to Predict Cognitive Decline in Older Adults From the Chinese Longitudinal Healthy Longevity Survey: Model Development and Validation Study.

JMIR aging
BACKGROUND: Cognitive impairment, indicative of Alzheimer disease and other forms of dementia, significantly deteriorates the quality of life of older adult populations and imposes considerable burdens on families and health care systems worldwide. T...

Using longitudinal data and deep learning models to enhance resource allocation in home-based medical care.

International journal of medical informatics
BACKGROUND: The aging population is driving increased healthcare demands and costs, prompting the need for effective home healthcare programs. Accurate patient assessment is essential for optimizing resource allocation and tailoring services.

Exploring the potential and limitations of deep learning and explainable AI for longitudinal life course analysis.

BMC public health
BACKGROUND: Understanding the complex interplay between life course exposures, such as adverse childhood experiences and environmental factors, and disease risk is essential for developing effective public health interventions. Traditional epidemiolo...

Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.

NPJ primary care respiratory medicine
Primary care consultations provide an opportunity for patients and clinicians to assess asthma attack risk. Using a data-driven risk prediction tool with routinely collected health records may be an efficient way to aid promotion of effective self-ma...

Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction.

Scientific reports
Biological age (BA) and frailty represent two distinct health measures that offer valuable insights into the aging process. Comparing and analyzing blood-based biomarkers from the machine learning (ML) predictors of BA and frailty helps deepen our un...

Predictors of Sleep Latency From the Multiple Sleep Latency Test: A Random Forest Investigation in a Community Sample.

Journal of sleep research
This study aimed to advance the understanding of factors that predict mean sleep latency (MSL) on the multiple sleep latency test (MSLT) by applying machine learning methodology on a high-dimensional dataset from a large community sample. A cross-sec...

Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study.

International journal of molecular sciences
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to...