AI Medical Compendium Topic:
Risk Factors

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Cardiac patients' surgery outcome and associated factors in Ethiopia: application of machine learning.

BMC pediatrics
INTRODUCTION: Cardiovascular diseases are a class of heart and blood vessel-related illnesses. In Sub-Saharan Africa, including Ethiopia, preventable heart disease continues to be a significant factor, contrasting with its presence in developed natio...

Machine learning analysis with population data for prepregnancy and perinatal risk factors for the neurodevelopmental delay of offspring.

Scientific reports
Neurodevelopmental disorders (NDD) in offspring are associated with a complex combination of pre-and postnatal factors. This study uses machine learning and population data to evaluate the association between prepregnancy or perinatal risk factors an...

Low muscle quality on a procedural computed tomography scan assessed with deep learning as a practical useful predictor of mortality in patients with severe aortic valve stenosis.

Clinical nutrition ESPEN
BACKGROUND & AIMS: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in tr...

Uncovering the most robust predictors of problematic pornography use: A large-scale machine learning study across 16 countries.

Journal of psychopathology and clinical science
Problematic pornography use (PPU) is the most common manifestation of the newly introduced compulsive sexual behavior disorder diagnosis in the 11th revision of the International Classification of Diseases. Research related to PPU has proliferated in...

Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction.

International journal of cardiology
BACKGROUND: Machine learning clustering of patients with ST-elevation acute myocardial infarction (STEMI) may provide important insights into their risk profile, management and prognosis.

CT-based radiomics of machine-learning to screen high-risk individuals with kidney stones.

Urolithiasis
Screening high-risk populations is crucial for the prevention and treatment of kidney stones. Here, we employed radiomics to screen high-risk patients for kidney stones. A total of 513 independent kidneys from our hospital between 2020 and 2022 were ...

Risk prediction of cholangitis after stent implantation based on machine learning.

Scientific reports
The risk of cholangitis after ERCP implantation in malignant obstructive jaundice patients remains unknown. To develop models based on artificial intelligence methods to predict cholangitis risk more accurately, according to patients after stent impl...

A Predictive Model for Intraoperative Cerebrospinal Fluid Leak During Endonasal Pituitary Adenoma Resection Using a Convolutional Neural Network.

World neurosurgery
BACKGROUND: Cerebrospinal fluid (CSF) leak during endoscopic endonasal transsphenoidal surgery can lead to postoperative complications. The clinical and anatomic risk factors of intraoperative CSF leak are not well defined. We applied a two-dimension...

Use of machine learning to identify protective factors for death from COVID-19 in the ICU: a retrospective study.

PeerJ
BACKGROUND: Patients in serious condition due to COVID-19 often require special care in intensive care units (ICUs). This disease has affected over 758 million people and resulted in 6.8 million deaths worldwide. Additionally, the progression of the ...