AIMC Topic: Biomarkers

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A method of inferring the relationship between Biomedical entities through correlation analysis on text.

Biomedical engineering online
BACKGROUND: One of the most important processes in a machine learning-based natural language processing is to represent words. The one-hot representation that has been commonly used has a large size of vector and assumes that the features that make u...

Serum adipocytokines are associated with microalbuminuria in patients with type 1 diabetes and incipient chronic complications.

Diabetes & metabolic syndrome
AIMS: Recent studies have implicated possible contribution of adipocytokines in development and progression of microvascular complications in patients with type 1 diabetes (T1DM). The aim of our study was to investigate relationship between adipocyto...

Circulating betatrophin in relation to metabolic, inflammatory parameters, and oxidative stress in patients with type 2 diabetes mellitus.

Diabetes & metabolic syndrome
AIMS: Recently, it was suggested that betatrophin has a role in controlling pancreatic β cell proliferation and lipid metabolism, however, its role in human subjects has not been established yet. The predicting role of betatrophin and MDA along with ...

EEG may serve as a biomarker in Huntington's disease using machine learning automatic classification.

Scientific reports
Reliable markers measuring disease progression in Huntington's disease (HD), before and after disease manifestation, may guide a therapy aimed at slowing or halting disease progression. Quantitative electroencephalography (qEEG) may provide a quantif...

Effect of combined vitamin D administration plus dietary intervention on oxidative stress markers in patients with metabolic syndrome: A pilot randomized study.

Clinical nutrition ESPEN
BACKGROUND: Metabolic syndrome (MetS) patients can have low 25-hydroxyvitamin D 25(OH)VitD levels, which may be associated with increased oxidative stress. There is little data on the effect of 25(OH)VitD administration plus dietary intervention on o...

JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information.

AJR. American journal of roentgenology
OBJECTIVE: When treatment decisions are being made for patients with acute ischemic stroke, timely and accurate outcome prediction plays an important role. The optimal rehabilitation strategy also relies on long-term outcome predictions. The decision...

Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data.

IEEE transactions on medical imaging
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation o...

High triglycerides to HDL-cholesterol ratio is associated with insulin resistance in normal-weight healthy adults.

Diabetes & metabolic syndrome
AIM: To evaluate the association between high triglyceride/HDL-cholesterol (TG/HDL-C) ratio and insulin resistance (IR) or hyperinsulinemia after oral glucose tolerance test (OGTT) in normal-weight healthy adults.

Artificial intelligence in healthcare.

Nature biomedical engineering
Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only t...

Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy.

Scientific reports
Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent s...