AIMC Topic: Case-Control Studies

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A novel approach to antimicrobial resistance: Machine learning predictions for carbapenem-resistant Klebsiella in intensive care units.

International journal of medical informatics
This study was conducted at Kocaeli University Hospital in Turkey and aimed to predict carbapenem-resistant Klebsiella pneumoniae infection in intensive care units using the Extreme Gradient Boosting (XGBoost) algorithm, a form of artificial intellig...

Explainable machine learning identifies a polygenic risk score as a key predictor of pancreatic cancer risk in the UK Biobank.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Predicting the risk of developing pancreatic ductal adenocarcinoma (PDAC) is of paramount importance, given its high mortality rate. Current PDAC risk prediction models rely on a limited number of variables, do not include genetics, and h...

Development of machine learning-based models to predict congenital heart disease: A matched case-control study.

International journal of medical informatics
BACKGROUND: The current congenital heart disease (CHD) prediction tools lack adequate interpretability and convenience, hindering the development of personalized CHD management strategies. We developed a machine learning-based risk stratification mod...

Machine learning approaches and genetic determinants that influence the development of type 2 diabetes mellitus: a genetic association study in Brazilian patients.

Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas
This genetic association study including 120 patients with type 2 diabetes mellitus (T2DM) and 166 non-diabetic individuals aimed to investigate the association of polymorphisms in the genes GSTM1 and GSTT1 (gene deletion), GSTP1 (rs1695), ACE (rs464...

Integrating Metabolomics Domain Knowledge with Explainable Machine Learning in Atherosclerotic Cardiovascular Disease Classification.

International journal of molecular sciences
Metabolomic data often present challenges due to high dimensionality, collinearity, and variability in metabolite concentrations. Machine learning (ML) application in metabolomic analyses is enabling the extraction of meaningful information from comp...

Oral Microbe Community and Pyramid Scene Parsing Network-based Periodontitis Risk Prediction.

International dental journal
BACKGROUND: Periodontitis (PD) is a common chronic inflammatory disease affecting the gums and supporting tooth structures. It is often diagnosed only after significant irreversible tissue damage - such as gum recession and bone loss - has occurred, ...

Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning.

Journal of translational medicine
BACKGROUND: The aim of this study was to explore the microbial variations and biomarkers in the oral environment of patients with persistent pulmonary nodules (pPNs) and to reveal the potential biological functions of the salivary microbiota in pPNs.

Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques.

Scientific reports
Hepatocellular carcinoma (HCC) represents a significant health burden in Egypt, largely attributable to the endemic prevalence of hepatitis B and C viruses. Early identification of HCC remains a challenge due to the lack of widespread screening among...

The efficacy of topological properties of functional brain networks in identifying major depressive disorder.

Scientific reports
Major Depressive Disorder (MDD) is a common mental disorder characterized by cognitive impairment, and its pathophysiology remains to be explored. In this study, we aimed to explore the efficacy of brain network topological properties (TPs) in identi...