AIMC Topic: Longitudinal Studies

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Predicting depression and unravelling its heterogeneous influences in middle-aged and older people populations: a machine learning approach.

BMC psychology
BACKGROUND: Aging has become a global trend, and depression, as an accompanying issue, poses a significant threat to the health of middle-aged and older adults. Existing studies primarily rely on statistical methods such as logistic regression for sm...

Interplay of Spontaneous Reporting and Longitudinal Healthcare Databases for Signal Management: Position Statement from the Real-World Evidence and Big Data Special Interest Group of the International Society of Pharmacovigilance.

Drug safety
Signal management, defined as the set of activities from signal detection to recommendations for action, is conducted using different data sources and leveraging data from spontaneous reporting databases (SRDs), which represent the cornerstone of pha...

Longitudinal brain age in first-episode mania youth treated with lithium or quetiapine.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
It is unclear if lithium and quetiapine have neuroprotective effects, especially in early stages of bipolar and schizoaffective disorders. Here, an age-related multivariate brain structural measure (i.e., brain-PAD) at baseline and changes in respons...

Longitudinal study on the impact of short-term radiological interpretation training on resting-state brain network hubs.

Trends in neuroscience and education
Radiological expertise develops through extensive experience in specific imaging modalities. While previous research has focused on long-term learning and neural mechanisms of expertise, the effects of short-term radiological training on resting-stat...

Cardiometabolic index predicts cardiovascular events in aging population: a machine learning-based risk prediction framework from a large-scale longitudinal study.

Frontiers in endocrinology
BACKGROUND: While the Cardiometabolic Index (CMI) serves as a novel marker for assessing adipose tissue distribution and metabolic function, its prognostic utility for cardiovascular disease (CVD) events remains incompletely understood. This investig...

LA-ResUNet: Attention-based network for longitudinal liver tumor segmentation from CT images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Longitudinal liver tumor segmentation plays a fundamental role in studying and monitoring the progression of associated diseases. The correlation and differences between longitudinal data can further improve segmentation performance, which are inevit...

A longitudinal observational study with ecological momentary assessment and deep learning to predict non-prescribed opioid use, treatment retention, and medication nonadherence among persons receiving medication treatment for opioid use disorder.

Journal of substance use and addiction treatment
BACKGROUND: Despite effective treatments for opioid use disorder (OUD), relapse and treatment drop-out diminish their efficacy, increasing the risks of adverse outcomes, including death. Predicting important outcomes, including non-prescribed opioid ...

Early social interactions and young school-aged children's behavioral problems: Converging evidence from theory- and data-driven approaches.

Journal of child psychology and psychiatry, and allied disciplines
BACKGROUND: Although prior studies have established the relation between social interactions and behavioral adjustment, it remains unclear whether aspects of early social interactions are uniquely related to behavioral problems and the relative impor...

Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study.

BMC pregnancy and childbirth
BACKGROUND: Low birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algo...