Results 211 to 220 of about 38,464 (255)

Research progress on the use of the optical coherence tomography system for the diagnosis and treatment of central nervous system tumors

Ibrain, Volume 11, Issue 1, Page 3-18, Spring 2025.
With the advantages of high‐resolution imaging, efficient image acquisition, intraoperative real‐time detection, and radiation‐free and noninvasive characteristics, optical coherence tomography (OCT) provides accurate diagnosis and effective intraoperative guidance for the minimally invasive diagnosis and treatment of central nervous system (CNS ...
Jiuhong Li, Ziba Ayi, Gonggong Lu, Haibo Rao, Feilong Yang, Jing Li, Jiachen Sun, Junlin Lu, Xulin Hu, Si Zhang, Xuhui Hui   +10 more
wiley   +1 more source

Addressing the Abundance Paradox Through Topology‐Guided Graph Learning for Cross‐Cohort Classification of Type 2 Diabetes

iMetaMed, EarlyView.
By shifting from highly variable taxonomic abundance to conserved ecological network topologies, our Dual‐Stream Graph Attention Network (DS‐GAT‐CP) resolves the “abundance paradox,” enabling highly accurate and cross‐regionally robust non‐invasive screening for Type 2 Diabetes. ABSTRACT Gut microbiome‐based machine learning models for Type 2 Diabetes (
Changjian Wu, Ping Wei, Xuhui Wang, Hua Hua, Junli Li   +4 more
wiley   +1 more source

Time‐Frequency Market‐State Learning for Execution‐Aware Directional Prediction: Evidence From the CSI 300 Index

International Studies of Economics, EarlyView.
ABSTRACT Financial markets can be viewed as multiscale information systems in which short‐lived disturbances, recurrent temporal patterns, liquidity conditions, and execution constraints jointly shape index price movements. This study develops an empirical‐wavelet‐and‐Fourier time‐frequency (EFT) feature block integrated with a ConvTransformer ...
Yi Chen, Ranzhe Jing, Qiang Li
wiley   +1 more source

A Comprehensive Study to Compare Different Compound Representations for Predicting Carcinogenicity In Vivo

Journal of Applied Toxicology, EarlyView.
ABSTRACT Carcinogenicity evaluation is a critical component of chemical risk assessment, yet traditional in vivo testing remains time consuming, costly, and ethically challenging. Computational approaches based on machine learning offer promising alternatives, but the relative contributions of different molecular representation strategies for ...
Iuri Barbosa Pereira, Rogerio Salvini, Eloisa Dutra Caldas   +2 more
wiley   +1 more source

Comparison of machine learning methods for prediction of venous thromboembolism among hospitalized adults

Journal of Hospital Medicine, EarlyView.
Abstract Background Hospital‐acquired venous thromboembolism (HA‐VTE) is a significant cause of morbidity and mortality among hospitalized adults. Accurate prediction of HA‐VTE is crucial for timely intervention and prevention. While logistic regression is widely used for the development of clinical prediction models, there is ongoing interest in the ...
Yeji Ko, Benjamin French, Jonathan S. Schildcrout, Merna El‐Rifai, Adam Wright, Benjamin F. Tillman, Amanda S. Mixon, Henry J. Domenico   +7 more
wiley   +1 more source

Early prediction of acute kidney injury in traumatic and non‐traumatic rhabdomyolysis using an interpretable machine learning model: A multicenter study with external validation

Journal of Intelligent Medicine, EarlyView.
Abstract Acute kidney injury (AKI) is a common and severe complication of rhabdomyolysis (RM), and early risk stratification remains challenging because of its multifactorial and heterogeneous nature. We developed and externally validated an interpretable machine learning (ML) model for early prediction of AKI in RM across traumatic and non‐traumatic ...
Chunli Liu, Jie Shi, Fengjuan Wang, Liang Ma, Huangang Hu, Li Zhang, Jie Song, Heng Jin, Dingwei Yang, Xiaoqin Guo, Haojun Fan, Qi Lv   +11 more
wiley   +1 more source

CALORIES BURNT PREDICTION USING XGBOOST ALGORITHM

International Research Journal of Modernization in Engineering Technology and Science
openaire   +1 more source

Intelligent diagnosis‐related group classification for 134,189 patients in eastern China using machine learning

Journal of Intelligent Medicine, EarlyView.
Intelligent DRG Classification with ELGWO‐LightGBM. Abstract Diagnosis‐related group (DRG) classification is crucial for healthcare cost management and resource allocation, but traditional manual classification by physicians is inefficient and error‐prone, especially for large‐scale medical data.
Yanxi Zhang, Yihan Cheng, Yuhui Ruan, Kun Wang   +3 more
wiley   +1 more source

Mycotoxin management in the cereal supply chain: Advanced detection, detoxification, and smart packaging strategies

Journal of the Science of Food and Agriculture, EarlyView.
Abstract Mycotoxins remain a persistent threat to the safety and quality of cereal grains and other agricultural products, and their impact on human health continues to raise global concerns. In many situations, the practices traditionally used to control these toxins are no longer sufficiently effective. They can be costly, difficult to implement on a
Abolfazl Asqardokht‐Aliabadi, Dipendra Kumar Mahato, Jafar Mohammadzadeh Milani   +2 more
wiley   +1 more source

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Improved XGBoost Model Based on Genetic Algorithm

International Journal of Computer Applications in Technology, 2020
An optimised XGBoost model based on genetic algorithm to search for optimal parameter combinations is proposed in this paper.
Jinxiang Chen, Feng Zhao, Yanguang Sun, Yilan Yin   +3 more
exaly   +3 more sources