AIMC Topic: Middle Aged

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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
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...

Machine learning models of depression in middle-aged and older adults with cardiovascular metabolic diseases.

Journal of affective disorders
BACKGROUND: The incidence of cardiovascular metabolic diseases (CMD) is increasing, and depression in CMD patients significantly impacts prognosis. Therefore, this study aimed to develop and validate a predictive model for depression in CMD patients ...

Functional connectome-based predictive modeling of suicidal ideation.

Journal of affective disorders
Suicide represents an egregious threat to society despite major advancements in medicine, in part due to limited knowledge of the biological mechanisms of suicidal behavior. We apply a connectome predictive modeling machine learning approach to ident...

Retinal image-based deep learning for mild cognitive impairment detection in coronary artery disease population.

Heart (British Cardiac Society)
BACKGROUND: Coronary artery disease (CAD) is linked to an increased risk of mild cognitive impairment (MCI). Effective and convenient screening methods for identifying MCI from the CAD population are still lacking. This study aims to develop a deep l...

A comparative analysis of automatic and manual scoring methods in polysomnography.

Sleep
The objective of this study was to compare twenty-six polysomnography (PSG) parameters between the groups utilizing automatic scoring (AS) software and manual scoring (MS) technique. Two MS groups, each comprising technicians with sleep-scoring exper...

Identification of key factors and explainability analysis for surgical decision-making in hepatic alveolar echinococcosis assisted by machine learning.

World journal of gastroenterology
BACKGROUND: Echinococcosis, caused by Echinococcus parasites, includes alveolar echinococcosis (AE), the most lethal form, primarily affecting the liver with a 90% mortality rate without prompt treatment. While radical surgery combined with antiparas...

Machine Learning-based Prediction of Active Tuberculosis in People With HIV Using Clinical Data.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
BACKGROUND: Coinfections of Mycobacterium tuberculosis (MTB) and human immunodeficiency virus (HIV) impose a substantial global health burden. Patients with MTB infection face a heightened risk of progression to incident active TB, which preventive t...

History matters: Preventing severe allergic transfusion reactions.

American journal of clinical pathology
OBJECTIVE: Prior studies have shown that pretransfusion medication is not effective in preventing allergic transfusion reactions (ATRs), but these studies did not consider the patient's history of ATR. This study evaluated whether pretransfusion anti...

Machine Learning Algorithms for Predicting Urinary Tract Infections: Integration of Demographic Data and Dipstick Reflectance Results.

Clinical chemistry
BACKGROUND: Urinary tract infections (UTIs) are among the most common infections encountered in healthcare settings. Current diagnostic practices often require 24-48 h due to the time needed for culture results. Given that 70%-80% of cultures return ...