AIMC Topic: Biomarkers
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Machine learning techniques to predict diabetic ketoacidosis and HbA1c above 7% among individuals with type 1 diabetes - A large multi-centre study in Australia and New Zealand.
Nutrition, metabolism, and cardiovascular diseases : NMCD
Jan 9, 2025
BACKGROUND AND AIMS: Type 1 diabetes and diabetic ketoacidosis (DKA) have a significant impact on individuals and society across a wide spectrum. Our objective was to utilize machine learning techniques to predict DKA and HbA1c>7 %.
New Zealand
Support Vector Machine
Australia
Predictive Value of Tests
Glycated Hemoglobin
Young Adult
Adult
Male
Middle Aged
Adolescent
Diabetes Mellitus, Type 1
Biomarkers
Diabetic Ketoacidosis
Prognosis
Female
Risk Factors
Humans
Risk Assessment
Machine Learning
Deep Learning
Decision Support Techniques
Prospective Studies
Harnessing NLP to investigate biomarker interactions and CVD risks in elderly chronic kidney disease patients.
SLAS technology
Jan 9, 2025
Chronic kidney disease (CKD) significantly increases the risk of CVD diseases, particularly among elderly patients. Understanding the interaction between several biomarkers and cardiovascular (CVD) risks is crucial for improving patient outcomes and ...
Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children.
Brain research bulletin
Jan 9, 2025
BACKGROUND: Due to the importance and the consequences of anxiety, the goals of the current study are brain mapping, biomarker identification and the use of an assessment method for diagnosis of anxiety during emotional face in preschool children.
A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles.
Orphanet journal of rare diseases
Jan 9, 2025
BACKGROUND: Glycogen storage disease (GSD) Ia is an ultra-rare inherited disorder of carbohydrate metabolism. Patients often present in the first months of life with fasting hypoketotic hypoglycemia and hepatomegaly. The diagnosis of GSD Ia relies on...
Repeatability of AI-based, automatic measurement of vertebral and cardiovascular imaging biomarkers in low-dose chest CT: the ImaLife cohort.
European radiology
Jan 8, 2025
OBJECTIVE: To evaluate the repeatability of AI-based automatic measurement of vertebral and cardiovascular markers on low-dose chest CT.
Predicting 28-day all-cause mortality in patients admitted to intensive care units with pre-existing chronic heart failure using the stress hyperglycemia ratio: a machine learning-driven retrospective cohort analysis.
Cardiovascular diabetology
Jan 8, 2025
Chronic heart failure (CHF) poses a significant threat to human health. The stress hyperglycemia ratio (SHR) is a novel metric for accurately assessing stress hyperglycemia, which has been correlated with adverse outcomes in various major diseases. H...
Time Factors
Hyperglycemia
Chronic Disease
Predictive Value of Tests
Patient Admission
Cause of Death
Heart Failure
Male
Middle Aged
Risk Assessment
Blood Glucose
Female
Risk Factors
Aged
Aged, 80 and over
Humans
Machine Learning
Decision Support Techniques
Prognosis
Retrospective Studies
Intensive Care Units
Databases, Factual
Biomarkers
Comprehensive bioinformatics analysis identifies metabolic and immune-related diagnostic biomarkers shared between diabetes and COPD using multi-omics and machine learning.
Frontiers in endocrinology
Jan 8, 2025
BACKGROUND: Diabetes and chronic obstructive pulmonary disease (COPD) are prominent global health challenges, each imposing significant burdens on affected individuals, healthcare systems, and society. However, the specific molecular mechanisms suppo...
Diagnostic and prognostic perspectives of Fabry disease via fiber evanescent wave spectroscopy advanced by machine learning.
Biosensors & bioelectronics
Jan 7, 2025
Fabry disease (FD) is a rare disorder resulting from a genetic mutation characterized by the accumulation of sphingolipids in various cells throughout the human body, leading to progressive and irreversible organ damage, particularly in males. Geneti...
A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.
Journal of proteome research
Jan 7, 2025
Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study analyzed plasma samples from 30 healthy postpartum women and 30 PPD-risk women using mass spectrome...
Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning.
PloS one
Jan 7, 2025
Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen serum biomarkers in PCOS patients using untargeted lipidomics and ensemble machine learning. Serum fr...
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