AIMC Topic: Machine Learning

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Developing a machine learning-based predictive model for cesium sorption distribution coefficient on crushed granite.

Journal of environmental radioactivity
The sorption of radionuclides on granite has been extensively studied over the past few decades due to its significance in the safety assessment of geological disposal for high-level radioactive waste (HLW). The sorption properties of granite for rad...

Leaf-Face Classifier Based on an Integrated Electrochemical Tongue and Machine Learning.

ACS sensors
Botanical sourcing seriously impacts the safety and potency of herbal medicines, restricting the development of the traditional Chinese medicinal industry. Rapid and convenient identification of plant resources is important to address this problem. H...

Advancing Source Apportionment of Atmospheric Particles: Integrating Morphology, Size, and Chemistry Using Electron Microscopy Technology and Machine Learning.

Environmental science & technology
To further reduce atmospheric particulate matter concentrations, there is a need for a more precise identification of their sources. The SEM-EDS technology (scanning electron microscopy and energy-dispersive X-ray spectroscopy) can provide high-resol...

Machine learning for predicting severe dengue in Puerto Rico.

Infectious diseases of poverty
BACKGROUND: Distinguishing between non-severe and severe dengue is crucial for timely intervention and reducing morbidity and mortality. World Health Organization (WHO)-recommended warning signs offer a practical approach for clinicians but have limi...

Development of a machine learning model related to explore the association between heavy metal exposure and alveolar bone loss among US adults utilizing SHAP: a study based on NHANES 2015-2018.

BMC public health
BACKGROUND: Alveolar bone loss (ABL) is common in modern society. Heavy metal exposure is usually considered to be a risk factor for ABL. Some studies revealed a positive trend found between urinary heavy metals and periodontitis using multiple logis...

Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper.

BMC medical informatics and decision making
Training machine learning models using data from severe COVID-19 patients admitted to a central hospital, where entire wards are specifically dedicated to COVID-19, may yield predictions that differ significantly from those generated using data colle...

Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer.

Scientific reports
Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process and therefore develop distant metastasis. Cuproptosis is a novel type of cell death and a limited nu...

A novel aggregated coefficient ranking based feature selection strategy for enhancing the diagnosis of breast cancer classification using machine learning.

Scientific reports
Effective Breast cancer (BC) analysis is crucial for early prognosis, controlling cancer recurrence, timely medical intervention, and determining appropriate treatment procedures. Additionally, it plays a significant role in optimizing mortality rate...

Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics.

Scientific reports
Development of antibodies often begins with the assessment and optimization of their physicochemical properties, and their efficient engagement with the target of interest. Decisions at the early optimization stage are critical for the success of the...

Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques.

Scientific reports
In prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early prediction of clinically significant cancer (CsPCa). The prostate imaging-reporting and data system (PI-RA...