AIMC Topic: Risk Factors
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The risk factors for relapse behavior in individuals with substance use disorders: An interpretable machine learning study.
Journal of affective disorders
May 27, 2025
BACKGROUND: Substance abuse has become a serious public health problem worldwide, and finding effective prevention and treatment strategies is undoubtedly an urgent need. This study addresses the risk factors that lead to relapse behaviors among subs...
A hybrid explainable federated-based vision transformer framework for breast cancer prediction via risk factors.
Scientific reports
May 27, 2025
Breast cancer remains a leading cause of mortality in women, underscoring the need for timely and accurate diagnosis. This paper addresses this challenge by introducing a comprehensive explainable federated learning framework for breast cancer predic...
Development and validation of a LASSO logistic regression based nomogram for predicting live births in women with polycystic ovary syndrome: a retrospective cohort study.
Frontiers in endocrinology
May 27, 2025
OBJECTIVE: There is limited study on predictive models for live births in patients with polycystic ovarian syndrome (PCOS). The study aimed to develop and validate a nomogram for predicting live births in Chinese women with PCOS, as well as to identi...
Type 2 Diabetes in Taiwan: Unmasking Influential Factors Through Advanced Predictive Modeling.
Journal of diabetes research
May 27, 2025
Type 2 diabetes (T2D) is influenced by lifestyle, genetics, and environmental conditions. By utilizing machine learning techniques, we can enhance the precision of T2D risk prediction by analyzing the complex interactions among these variables. This...
Heterogeneity in the association between internet use and dementia among older adults: A machine-learning analysis.
Archives of gerontology and geriatrics
May 26, 2025
BACKGROUND & AIMS: Internet use among older adults may reduce the risk of dementia, but it remains unknown how the effects vary across individuals. The aim of this study was to rigorously examine heterogeneity in the association between internet use ...
Study protocol for the Rainbow Austrian Longitudinal Family (RALF) study: a longitudinal, multi-method, multi-rater investigation of risk and resilience factors in Austrian LGBTQ+ parent families.
BMC psychology
May 26, 2025
BACKGROUND: Research on LGBTQ+ parent families is evolving to include a growing range of family systems, identities, methodologies, and topics. However, studies that examine minority-specific risk and resilience factors and their associations with wi...
Development and validation of a predictive model for refracture risk in elderly individuals with osteoporotic vertebral compression fracture: a retrospective study in China.
Aging clinical and experimental research
May 26, 2025
BACKGROUND: The rising incidence of refractures and associated adverse outcomes among individuals with osteoporotic vertebral compression fractures has gained significant attention. Identifying refracture risk is crucial for implementing effective pr...
Development of a non-contrast CT-based radiomics nomogram for early prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage.
BMC medical imaging
May 23, 2025
BACKGROUNDS: Delayed cerebral ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH), leading to poor prognosis and high mortality. This study developed a non-contrast CT (NCCT)-based radiomics nomogram for e...
Development and validation of a hypoxemia prediction model in middle-aged and elderly outpatients undergoing painless gastroscopy.
Scientific reports
May 23, 2025
Hypoxemia is a common complication associated with anesthesia in painless gastroscopy. With the aging of the social population, the number of cases of hypoxemia among middle-aged and elderly patients is increasing. However, tools for predicting hypox...
Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study.
Annals of medicine
May 22, 2025
OBJECTIVES: To develop a machine learning-based model to predict the relapse risk of Primary Autoimmune Haemolytic Anaemia (AIHA) after the last remission.
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