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Natural lithium isotope variations in serum after lithium administration as a novel biomarker for differentiating schizophrenia and bipolar disorder.

Translational psychiatry
Accurate differentiation of schizophrenia (SZ) and bipolar disorder (BD) is crucial for effective clinical management. However, current diagnostic methods, which rely heavily on subjective assessments, are prone to high rates of misdiagnosis. This st...

Cardiovascular risk prediction and influencing predictors identification among Bangladeshi individuals using machine learning algorithms and association rule mining.

PloS one
BACKGROUND: Cardiovascular disease (CVD) encompasses a group of disorders that affect the heart and blood vessels, making it one of the leading causes of death globally, including in Bangladesh. Applying predictive modeling for the early identificati...

Estimated glucose disposal rate predicts frailty through diabetes: Evidence from machine learning and mediation models in NHANES.

PloS one
OBJECTIVE: As an emerging insulin resistance marker, the relationship between estimated glucose disposal rate (eGDR) and frailty needs further exploration. This study examines the eGDR-frailty link, develops a machine learning predictive model to add...

Machine learning-based identification of small RNA signatures in aqueous humor as a step toward precision diagnosis of glaucoma.

Annals of medicine
BACKGROUND: Glaucoma is a progressive neurodegenerative disease of the optic nerve and one of the leading causes of irreversible blindness worldwide. Small RNAs (including miRNAs) play an important role in the pathogenesis of the disease. Despite ext...

Investigating the relationship between blood factors and HDL-C levels in the bloodstream using machine learning methods.

Journal of health, population, and nutrition
INTRODUCTION: The study investigates the relationship between blood lipid components and metabolic disorders, specifically high-density lipoprotein cholesterol (HDL-C), which is crucial for cardiovascular health. It uses logistic regression (LR), dec...

"I can no longer give take-home exams": Health professionals educators' experiences and perceptions regarding the use of artificial intelligence in health professions education in Uganda.

BMC medical education
INTRODUCTION: Artificial intelligence (AI) tools offer immense opportunities and challenges for medical education. However, there is limited information about the use of AI among health professional educators, particularly in low- and middle-income c...

Machine learning-based prediction of N2 lymph node metastasis in non-small cell lung cancer.

BMC pulmonary medicine
BACKGROUND: Lung cancer is a leading cause of cancer-related mortality worldwide. Accurate staging of mediastinal lymph nodes is a crucial step in determining appropriate treatment approaches. Current noninvasive diagnostic methods do not provide suf...

AI-driven chemotoxicity prediction in colorectal cancer: impact of race, SDOH, and biological aging.

BMC cancer
BACKGROUND: Patients with colorectal cancer (CRC) often experience chemotoxicity that impacts treatment adherence, survival, and quality of life. Early screening for chemotoxicity risk is vital, yet comprehensive predictive models are lacking. The ob...

The diagnostic value of serum cysteine protease inhibitor (CST4) in colorectal cancer: a preliminary study.

BMC gastroenterology
BACKGROUND: CST4 is associated with various cancers but its diagnostic value in colorectal cancer (CRC) has not been clearly established. This study aims to further validate the diagnostic value of CST4 in colorectal cancer using random forest models...

Support vector machine-based preoperative identification of IDH-Mutant low-grade gliomas in adult gliomas using clinical features.

BMC neurology
BACKGROUND: The preoperative identification of (isocitrate dehydrogenase) IDH-mutant low-grade gliomas (LGGs) is critical for personalized treatment planning. We aimed to develop a streamlined machine-learning model using key clinical features for ra...