AI Medical Compendium Topic

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Case-Control Studies

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Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study.

Frontiers in public health
OBJECTIVE: Due to the high global prevalence of silicosis and the ongoing challenges in its diagnosis, this pilot study aims to screen biomarkers from routine blood parameters and develop a multi-biomarker model for its early detection.

Circulating endothelial progenitor cells and inflammatory markers in type 1 diabetes after an acute session of aerobic exercise.

Archives of endocrinology and metabolism
OBJECTIVE: To determine circulating endothelial progenitor cells (EPC) counts and levels of inflammatory markers in individuals with and without type 1 diabetes mellitus (T1DM) in response to an intense aerobic exercise session.

Untargeted metabolomics and machine learning unveil the exposome and metabolism linked with the risk of early pregnancy loss.

Journal of hazardous materials
Early pregnancy loss (EPL) may result from exposure to emerging contaminants (ECs), although the underlying mechanisms remain poorly understood. This case-control study measured over 2000 serum features, including 37 ECs, 6 biochemicals, and 2057 end...

Deep learning based prediction of depression and anxiety in patients with type 2 diabetes mellitus using regional electronic health records.

International journal of medical informatics
BACKGROUND: Depression and anxiety are prevalent mental health conditions among individuals with type 2 diabetes mellitus (T2DM), who exhibit unique vulnerabilities and etiologies. However, existing approaches fail to fully utilize regional heterogen...

Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy.

BMC pregnancy and childbirth
BACKGROUND: Intrahepatic cholestasis of pregnancy (ICP) is a liver disorder that occurs in the second and third trimesters of pregnancy and is associated with a significant risk of fetal complications, including premature birth and fetal death. In cl...

Interpretable machine learning-derived nomogram model for early detection of persistent diarrhea in Salmonella typhimurium enteritis: a propensity score matching based case-control study.

BMC infectious diseases
BACKGROUND: Salmonella typhimurium infection is a considerable global health concern, particularly in children, where it often leads to persistent diarrhea. This condition can result in severe health complications including malnutrition and cognitive...

Contrast-enhanced magnetic resonance imaging based calf muscle perfusion and machine learning in peripheral artery disease.

Scientific reports
Peripheral artery disease (PAD) remains underdiagnosed and undertreated and is associated with an increased risk for adverse cardiovascular outcomes. Imaging provides an approach to identifying patients with PAD. However, the role of integrating imag...

Unveiling NLR pathway signatures: EP300 and CPN60 markers integrated with clinical data and machine learning for precision NASH diagnosis.

Cytokine
BACKGROUND: Given the increasing prevalence of metabolic dysfunction-associated fatty liver disease (MAFLD) and non-alcoholic steatohepatitis (NASH), there is a critical need for accurate non-invasive early diagnostic markers.

You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction.

Computers in biology and medicine
Breast Cancer is the most commonly diagnosed cancer worldwide. While screening mammography diminishes the burden of this disease, it has some flaws related to the presence of false negatives. Adapting screening to each woman's needs could help overco...

Machine learning and metabolomics identify biomarkers associated with the disease extent of ulcerative colitis.

Journal of Crohn's & colitis
BACKGROUND AND AIMS: Ulcerative colitis (UC) is a metabolism-related chronic intestinal inflammatory disease. Disease extent is a key parameter of UC. Using serum metabolic profiling to identify noninvasive biomarkers of disease extent may inform the...