AIMC Topic: Machine Learning

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Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue.

Journal of biomedical optics
SIGNIFICANCE: Diffuse optical modalities such as broadband near-infrared spectroscopy (bNIRS) and hyperspectral imaging (HSI) represent a promising alternative for low-cost, non-invasive, and fast monitoring of living tissue. Particularly, the possib...

Robust identification key predictors of short- and long-term weight status in children and adolescents by machine learning.

Frontiers in public health
BACKGROUND: Early identification of high-risk individuals for weight problems in children and adolescents is crucial for implementing timely preventive measures. While machine learning (ML) techniques have shown promise in addressing this complex cha...

Machine learning-based discovery of UPP1 as a key oncogene in tumorigenesis and immune escape in gliomas.

Frontiers in immunology
INTRODUCTION: Gliomas are the most common and aggressive type of primary brain tumor, with a poor prognosis despite current treatment approaches. Understanding the molecular mechanisms underlying glioma development and progression is critical for imp...

Novel prognostic signature for hepatocellular carcinoma using a comprehensive machine learning framework to predict prognosis and guide treatment.

Frontiers in immunology
BACKGROUND: Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prognostic models to assist clinicians. This study aimed to construct an HCC prognosis-related gene sign...

Predicting malnutrition-based anemia in geriatric patients using machine learning methods.

Journal of evaluation in clinical practice
BACKGROUND: Anemia due to malnutrition may develop as a result of iron, folate and vitamin B12 deficiencies. This situation poses a higher risk of morbidity and mortality in the geriatric population than in other age groups. Therefore, early diagnosi...

Raman spectroscopy combined with machine learning and chemometrics analyses as a tool for identification atherosclerotic carotid stenosis from serum.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Atherosclerosis carotid stenosis (ACS) is one of the main causes of stroke. Unfortunately, the highest number of people go to the doctor with an advanced disease or as a result of a stroke, because carotid atherosclerosis does not cause obvious sympt...

Enhancing pharmacist intervention targeting based on patient clustering with unsupervised machine learning.

Expert review of pharmacoeconomics & outcomes research
OBJECTIVES: Adherence to the American Diabetes Association (ADA) Standards of Medical Care is low. This study aimed to assist pharmacists in identifying patients for diabetes control interventions using unsupervised machine learning.

Application of machine vision in food computing: A review.

Food chemistry
With global intelligence advancing and the awareness of sustainable development growing, artificial intelligence technology is increasingly being applied to the food industry. This paper, grounded in practical application cases, reviews the current r...

Skin Cancer Detection in Diverse Skin Tones by Machine Learning Combining Audio and Visual Convolutional Neural Networks.

Oncology
INTRODUCTION: Skin cancer (SC) is common in fair skin (FS) at a 1:5 lifetime incidence for nonmelanoma skin cancer. In order to assist clinicians' decisions, a risk intervention technology was developed, which combines a dual-mode machine learning of...