AIMC Topic: Biomarkers

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Improving blood glucose level predictability using machine learning.

Diabetes/metabolism research and reviews
This study was designed to improve blood glucose level predictability and future hypoglycemic and hyperglycemic event alerts through a novel patient-specific supervised-machine-learning (SML) analysis of glucose level based on a continuous-glucose-mo...

Exploration of chemical markers using a metabolomics strategy and machine learning to study the different origins of Ixeris denticulata (Houtt.) Stebb.

Food chemistry
As a generally edible plant, Ixeris denticulata (Houtt.) Stebb is widely distributed in China. Its medicinal value has attracted much attention in recent years. However, the chemical markers that cause quality and taste differences in I. denticulata ...

An Efficient Combination among sMRI, CSF, Cognitive Score, and 4 Biomarkers for Classification of AD and MCI Using Extreme Learning Machine.

Computational intelligence and neuroscience
Alzheimer's disease (AD) is the most common cause of dementia and a progressive neurodegenerative condition, characterized by a decline in cognitive function. Symptoms usually appear gradually and worsen over time, becoming severe enough to interfere...

A Comparative Study of the Diagnostic Potential of Plasma and Erythrocytic α-Synuclein in Parkinson's Disease.

Neuro-degenerative diseases
BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disease characterized by intracellular α-synuclein (α-Syn) deposition. Alternation of the α-Syn expression level in plasma or erythrocytes may be used as a potential PD biomarker. However, n...

Language as a biomarker for psychosis: A natural language processing approach.

Schizophrenia research
Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onset in prospective clinical high risk (CHR) cohort studies. The potential value of linguistic biomarke...

Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers.

Scientific reports
In this paper we develop statistical algorithms to infer possible cardiac pathologies, based on data collected from 24 h Holter recording over a sample of 2829 labelled patients; labels highlight whether a patient is suffering from cardiac pathologie...

S100 proteins, cytokines, and chemokines as tear biomarkers in children with juvenile idiopathic arthritis-associated uveitis.

Ocular immunology and inflammation
PURPOSE: Biomarkers for juvenile idiopathic arthritis-associated uveitis (JIA-U) are needed. We aimed to measure inflammatory biomarkers in tears as a non-invasive method to identify biomarkers of uveitis activity.

Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction.

Biomarkers in medicine
In patients with suspected myocardial infarction (MI), we sought to validate a machine learning-driven, multibiomarker panel for prediction of incident major adverse cardiovascular events (MACE). A previously described prognostic panel for MACE con...