Showing 981 - 1,000 results of 1,420 for search '(((model OR (more OR more)) OR more) OR made) screening algorithm', query time: 0.26s Refine Results
  1. 981

    Research on Feature Extraction of Performance Degradation for Flexible Material R2R Processing Roller Based on PCA by Yaohua Deng, Huiqiao Zhou, Kexing Yao, Zhiqi Huang, Chengwang Guo

    Published 2020-01-01
    “…The Jacobi iteration method was introduced to derive the algorithm for solving eigenvalue and eigenvector of the covariance matrix. …”
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  2. 982

    A machine learning approach to predict positive coronary artery calcium scores in individuals with diabetes: a cross-sectional analysis of ELSA-Brasil baseline data by J.L. Amorim, I.M. Bensenor, A.P. Alencar, A.C. Pereira, A.C. Goulart, P.A. Lotufo, I.S. Santos

    Published 2025-08-01
    “…We analyzed 25 sociodemographic, medical history, symptom-related, and laboratory variables from 585 participants from the São Paulo investigation center with CACS data and no overt cardiovascular disease at baseline. We used six ML algorithms to build models to identify individuals with positive CACS. …”
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  3. 983

    GB-SAR Engineering Interference Suppression Method Integrating Amplitude-Phase Feature Analysis and Robust Regression by Wenting Zhang, Tao Lai, Yuanhui Mo, Haifeng Huang, Qingsong Wang, Zhihua Zhou

    Published 2025-01-01
    “…Subsequently, a two-stage suppression model based on robust estimation theory is developed to effectively suppress interference. …”
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  4. 984

    Time-Distributed Vision Transformer Stacked With Transformer for Heart Failure Detection Based on Echocardiography Video by Mgs M. Luthfi Ramadhan, Adyatma W. A. Nugraha Yudha, Muhammad Febrian Rachmadi, Kevin Moses Hanky Jr Tandayu, Lies Dina Liastuti, Wisnu Jatmiko

    Published 2024-01-01
    “…This study proposed a novel deep learning model consisting of a time-distributed vision transformer stacked with a transformer. …”
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  5. 985

    Cross-validation of the safe supplement screener (S3) predicting consistent third-party-tested nutritional supplement use in NCAA Division I athletes by Kinta D. Schott, Avaani Bhalla, Emma Armstrong, Ryan G. N. Seltzer, Floris C. Wardenaar

    Published 2025-01-01
    “…IntroductionThis cross-sectional study aimed to cross-validate an earlier developed algorithm-based screener and explore additional potential predictors for whether athletes will use third-party-tested (TPT) supplements.MethodsTo justify the initial model behind the supplement safety screener (S3) algorithm which predicts whether athletes will use TPT supplements, a cross-validation was performed using this independent dataset based on responses of a large group of collegiate NCAA DI athletes. …”
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  6. 986
  7. 987

    Transmitted drug resistance in the CFAR network of integrated clinical systems cohort: prevalence and effects on pre-therapy CD4 and viral load. by Art F Y Poon, Jeannette L Aldous, W Christopher Mathews, Mari Kitahata, James S Kahn, Michael S Saag, Benigno Rodríguez, Stephen L Boswell, Simon D W Frost, Richard H Haubrich

    Published 2011-01-01
    “…Aggregate effects of mutations by drug class were estimated by fitting linear models of pVL and CD4 on weighted sums over TDR mutations according to the Stanford HIV Database algorithm. …”
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  8. 988

    Problems and perspectives of family doctors training on the undergraduate stage by Yu. M. Kolesnik, V. D. Syvolap, N. S. Mikhaylovskaya, T.O. Kulinich

    Published 2013-04-01
    “…For working on practical part of family doctors basic skills it is planned to organize educational and training center at the family ambulatory, and its equipment with the necessary visual means, phantoms, models, simulators, diagnostic, medical apparatus and instruments. …”
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  9. 989

    Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images by Mingzhi Zhang, Tsz Kin Ng, Yi Zheng, Guihua Zhang, Jian-Wei Lin, Ji Wang, Jie Ji, Peiwen Xie, Yongqun Xiong, Hanfu Wu, Cui Liu, Huishan Zhu, Jinqu Huang, Leixian Lin

    Published 2025-05-01
    “…The deep learning (DL) performance was compared with the diabetic retinopathy experts.Setting Data were collected from Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Chaozhou People’s Hospital and The Second Affiliated Hospital of Shantou University Medical College from January 2010 to December 2023.Participants 7790 volumes of 7146 eyes from 4254 patients were annotated, of which 6281 images were used as the development set and 1509 images were used as the external validation set, split based on the centres.Main outcomes Accuracy, F1-score, sensitivity, specificity, area under receiver operating characteristic curve (AUROC) and Cohen’s kappa were calculated to evaluate the performance of the DL algorithm.Results In classifying DME with non-DME, our model achieved an AUROCs of 0.990 (95% CI 0.983 to 0.996) and 0.916 (95% CI 0.902 to 0.930) for hold-out testing dataset and external validation dataset, respectively. …”
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  10. 990

    Exploring the role of repetitive negative thinking in the transdiagnostic context of depression and anxiety in children by Kuiliang Li, Lei Ren, Xiao Li, Chang Liu, Xuejiao Tan, Ming Ji, Xi Luo

    Published 2025-08-01
    “…Network analysis revealed that RNT’s core features exhibited the highest bridge betweenness and bridge expected influence, indicating a critical mediating role in the co-occurrence of symptoms. The random forest model showed optimal predictive performance (AUC = 0.90, recall = 0.95), supporting its applicability for early screening. …”
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  11. 991

    MYOPIA PREVALENCE AMONG STUDENTS DURING COVID-19 PANDEMIC. A SYSTEMATIC REVIEW AND META-ANALYSIS by Natasha Hana Savitri, Adinda Sandya Poernomo, Muhammad Bagus Fidiandra1, Eka Candra Setyawan1, Arinda Putri Auna Vanadia1, Bulqis Inas Sakinah1, Lilik Djuari

    Published 2022-12-01
    “…Data retrieval used the PICO method and journal adjustments were selected using the PRISMA algorithm. Data analysis was performed using a random-effects model. …”
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  12. 992
  13. 993

    Proteomic analysis of blood plasma as a tool for personalized diagnosis of lung adenocarcinoma by D. N. Korobkov, A. S. Kononikhin, S. D. Semenov, H. L. Kordzaya, A. G. Brzhozovskiy, A. E. Bugrova, E. Yu. Vasilieva, D. Yu. Kanner, E. N. Nikolaev, A. A. Komissarov

    Published 2025-04-01
    “…Classifiers developed based on these protein panels make it possible to distinguish between patients with LAC and healthy controls, as well as to detect the presence of metastases among patients with LAC, with sensitivity and specificity of more than 90 %.Conclusion. The data obtained can be used to develop new tests for LAC screening and predicting disease outcomes based on the blood plasma proteome. …”
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  14. 994

    Deaths ascribed to non-communicable diseases among rural Kenyan adults are proportionately increasing: evidence from a health and demographic surveillance system, 2003-2010. by Penelope A Phillips-Howard, Kayla F Laserson, Nyaguara Amek, Caryl M Beynon, Sonia Y Angell, Sammy Khagayi, Peter Byass, Mary J Hamel, Anne M van Eijk, Emily Zielinski-Gutierrez, Laurence Slutsker, Kevin M De Cock, John Vulule, Frank O Odhiambo

    Published 2014-01-01
    “…<h4>Background</h4>Non-communicable diseases (NCDs) result in more deaths globally than other causes. Monitoring systems require strengthening to attribute the NCD burden and deaths in low and middle-income countries (LMICs). …”
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  15. 995

    Ethical and social issues in prediction of risk of severe mental illness: a scoping review and thematic analysis by Ivars Neiders, Signe Mežinska, Neeltje E. M. van Haren

    Published 2025-05-01
    “…First, there are issues that should deserve more attention than they have received thus far (clinical utility, extensive or mandatory use). …”
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  16. 996

    Amyloid Cardiomyopathy: Review of A Fatal Case Report by O. V. Soldatova, I. Ya. Goryanskaya

    Published 2025-05-01
    “…To date, it has been proven that amyloid cardiomyopathy is an important and often undiagnosed cause of chronic heart failure and cardiac arrhythmias, especially in the elderly. There are more than 15 types of precursor proteins capable of causing systemic amyloidosis, but only 2 of them accumulate in the interstitium of the heart: light chains of clonal immunoglobulin (AL) and tetrameric protein transthyretin (TTR). …”
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  17. 997

    3D Film Animation Image Acquisition and Feature Processing Based on the Latest Virtual Reconstruction Technology by Siwei Wu, Shan Xiao, Yihua Di, Cheng Di

    Published 2021-01-01
    “…Finally, the target 3D face is reconstructed using the feature points of the target face for model matching. The experimental results show that the algorithm reconstructs faces with high realism and accuracy, and the algorithm can reconstruct expression faces.…”
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  18. 998

    Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update by Aniruddha Sen, Palani Selvam Mohanraj, Vijaya Laxmi, Sumel Ashique, Rajalakshimi Vasudevan, Afaf Aldahish, Anupriya Velu, Arani Das, Iman Ehsan, Anas Islam, Sabina Yasmin, Mohammad Yousuf Ansari

    Published 2025-06-01
    “…At the same time, the rapidly increasing role of AI in diabetes care is woven into the story, mainly targeting how insulin therapy can be modified and personalized through algorithms and predictive modelling. It leaves a deep review of their pre-existing synergies, which helps understand how collaborative opportunities will unlock the future of T2DM care. …”
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  19. 999

    Project quality, regulation quality by Elena Mussinelli

    Published 2024-06-01
    “…These tools legitimise choices where conformity to the standard acts as a screen for the assumption of precise responsibilities. …”
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  20. 1000

    Harnessing AI and Quantum Computing for Revolutionizing Drug Discovery and Approval Processes: Case Example for Collagen Toxicity by David Melvin Braga, Bharat Rawal

    Published 2025-07-01
    “…In this context, “in silico” describes scientific studies performed using computer algorithms, simulations, or digital models to analyze biological, chemical, or physical processes without the need for laboratory (in vitro) or live (in vivo) experiments. …”
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