Showing 601 - 620 results of 1,420 for search '(((made OR model) OR model) OR more) screening algorithm', query time: 0.23s Refine Results
  1. 601

    A large-scale prospective nested case-control study: developing a comprehensive risk prediction model for early detection of pancreatic cancer in the community-based ESPRIT-AI coho... by Chaoliang Zhong, Penghao Li, Jia Zhao, Xue Han, Beilei Wang, Gang Jin

    Published 2025-02-01
    “…Multiple machine learning algorithms were compared, with the best performing algorithm selected for the final predictive model, subsequently validated using a real-world external test cohort. …”
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    Article
  2. 602

    Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion by Ma Jingyi, Tiejun Zhang, Jing Guodong, Yan Wenjun, Yang Bin

    Published 2020-01-01
    “…In addition, ablation experiments show that the multi-scale feature extraction, screening and local feature coding in this paper have a significant effect on improving the algorithm’s ability to distinguish different cloud images.…”
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  3. 603

    Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only by Yonina Ron, Tchelet Ron, Naomi Fridman, Anat Goldstein

    Published 2025-08-01
    “…This study develops machine learning (ML) models to predict future myopia development. These models utilize easily accessible, non-invasive data gathered during standard eye clinic visits, deliberately excluding more complex measurements such as axial length or corneal curvature. …”
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    Article
  4. 604

    Multimodal Approach (MRI and Ultrasonography) to the Diagnosis of Fetal Congenital Heart Diseases by A. V. Pomortsev, M. N. Karakhalis, N. V. Krivonosova, K. F. Goloseev

    Published 2024-11-01
    “…In 85.7% of the fetuses, the findings of different imaging techniques fully coincided with the autopsy findings.Conclusions: In this cohort of pregnant women, the second screening should include more examinations to verify the diagnosis of CHD. …”
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    Article
  5. 605

    A Bioinert Hydrogel Framework for Precision 3D Cell Cultures: Advancing Automated High‐Content and High‐Throughput Drug Screening by Hyunsu Jeon, Tiago Thomaz Migliati Zanon, James Carpenter, Aliciana Ilias, Yamil Colón, Yichun Wang

    Published 2025-04-01
    “…The unique hexagonal‐close‐packed geometry of iCC structure enables HCHTS through conventional plate reader analysis and fluorescent microscopy assisted by house‐developed automated data processing algorithm. This advancement offers promising applications in tissue engineering, disease modeling, and drug development in biomedical research.…”
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    Article
  6. 606

    Development and validation of a screening tool for sepsis without laboratory results in the emergency department: a machine learning studyResearch in context by Shan Jiang, Shuai Dai, Yulin Li, Xianlong Zhou, Cheng Jiang, Cong Tian, Yana Yuan, Chengwei Li, Yan Zhao

    Published 2025-02-01
    “…The qSepsis was derived by three ML algorithms, including logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB). …”
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    Article
  7. 607
  8. 608

    A New Fast Sparse Unmixing Algorithm Based on Adaptive Spectral Library Pruning and Nesterov Optimization by Kewen Qu, Fangzhou Luo, Huiyang Wang, Wenxing Bao

    Published 2025-01-01
    “…To address these shortcomings, this article proposes a new fast two-step sparse unmixing algorithm, called NeSU-LP, which is based on adaptive spectral library pruning technology and the Nesterov fast optimization strategy. …”
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    Article
  9. 609

    HPRNA: Predicting synergistic drug combinations for angina pectoris based on human pathway relationship network algorithm. by Mengyao Zhou, Mengfan Xu, Xiangling Zhang, Xiaochun Xing, Yang Li, Guanghui Wang, Guiying Yan

    Published 2025-01-01
    “…Compared to lengthy medical drugs experimental screening, mathematical models and algorithms show great potential in synergistic drug combinations prediction. …”
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    Article
  10. 610

    Identification of Plasma Proteins Associated with Alzheimer's Disease Using Feature Selection Techniques and Machine Learning Algorithms by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

    Published 2025-02-01
    “…This study aims to use computational algorithms to explore the relationship between plasma proteins and AD progression by identifying a panel of plasma proteins that can serve as biomarkers for tracking and diagnosing AD. …”
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  11. 611
  12. 612

    Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning by Zuo B, Jin L, Sun Z, Hu H, Yin Y, Yang S, Liu Z

    Published 2025-05-01
    “…Ten machine learning algorithms were used to develop and validate a model for predicting superaverage length of stay, and the best model was evaluated and selected.Results: We screened 83 candidate variables using the Boruta algorithm and identified 9 potentially important variables, including: cerebrovascular disease, white blood cell count, hematocrit, D-dimer, activated partial thromboplastin time, fibrin degradation products, partial pressure of carbon dioxide, reduced hemoglobin, and oxyhemoglobin. …”
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  13. 613
  14. 614

    Improving routine mental health screening for depression and anxiety in a paediatric lupus clinic: a quality improvement initiative for enhanced mental healthcare by Deborah M Levy, Evelyn Smith, Lawrence Ng, Andrea M Knight, Linda Hiraki, Tala El Tal, Avery Longmore, Audrea Chen, Holly Convery, Dinah Finkelstein, Chetana Kulkarni, Neely Lerman, Karen Leslie, Sharon Lorber, Oscar Mwizerwa, Vandana Rawal, Stephanie Wong, Asha Jeyanathan

    Published 2024-12-01
    “…Statistical process control charts were used to analyse the outcome measure for percentage of screened patients with cSLE. Patient and caregiver satisfaction surveys were conducted at baseline and after screening as a balancing measure.Interventions MH screening workflow with a referral algorithm was developed with stakeholders. …”
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  15. 615
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  17. 617

    Integrated Machine Learning Algorithms-Enhanced Predication for Cervical Cancer from Mass Spectrometry-Based Proteomics Data by Da Zhang, Lihong Zhao, Bo Guo, Aihong Guo, Jiangbo Ding, Dongdong Tong, Bingju Wang, Zhangjian Zhou

    Published 2025-03-01
    “…Furthermore, by integrating feature importance values, Shapley values, and local interpretable model-agnostic explanation (LIME) values, we demonstrated that the diagnostic area under the curve (AUC) achieved by our multi-dimensional learning models approached 1, significantly outperforming the diagnostic AUC of single markers derived from the PRIDE database. …”
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    Article
  18. 618

    Feature Selection based on Genetic Algorithm for Classification of Mammogram Using K-means, k-NN and Euclidean Distance by Kameran Adil Ibrahim

    Published 2023-02-01
    “…., the classifications was done on the bases of the features selected using genetic algorithm. Attempts have also been made to study the performance of each feature selected by Genetic Algorithm (GA) in classification. …”
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  19. 619

    Genetics, sex and the use of platelet‐rich plasma influence the development of arthrofibrosis after anterior cruciate ligament reconstruction by Mikel Sánchez, Izarbe Yarza, Cristina Jorquera, Jose María Aznar, Leonor López deDicastillo, Cristina Valente, Renato Andrade, João Espregueira‐Mendes, David Celorrio, Beatriz Aizpurua, Juan Azofra, Diego Delgado

    Published 2025-01-01
    “…Abstract Purpose To identify genes and patient factors that are related to the development of arthrofibrosis in patients after anterior cruciate ligament (ACL) reconstruction and to develop a prognostic model. Methods The study included patients diagnosed with ACL injury who underwent ACL reconstruction. …”
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  20. 620

    Chemoreactome screening of aquacobalamin and heptamethyl ester of cyanoaquacobyrinic acid cytotoxic effects on tumor cells with experimental confirmation on BT-474 and A549 cell by I. Yu. Torshin, M. V. Filimonova, O. A. Gromova, L. A. Maiorova, M. A. Sorokina, D. E. Frolova, A. N. Gromov, I. A. Reyer

    Published 2024-05-01
    “…Experimental studies of tumor cell cultures were carried out using the MTT testwith aquacobalamin and HECСA on cell lines of immortalized (telomerized) fibroblasts (Fb-hTERT), lung carcinoma (A549), and breast duct cancer (BT-474).Results. Chemoreactome screening of the effects of molecules on tumor cells made it possible to obtain estimates of cell growth IC50 for 470 tumor cell lines. …”
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