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  1. 1961

    The role of novel risk-scoring systems in predicting the efficacy of immunotherapy by Yu.V. Moskalenko

    Published 2025-02-01
    “…However, some patients resist ICIs, highlighting the need for novel risk-scoring systems to predict immunotherapy efficacy. Purpose – to evaluate the Lung Immune Prognostic Index (LIPI), Advanced Lung Cancer Inflammation Index (ALI), Patras Immunotherapy Score (PIOS), Prognostic Nutritional Index (PNI), and LEM score (Leukocytes, ECOG, Metastases) as predictors of ICI therapy efficacy in patients with mNSCLC. …”
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    Article
  2. 1962

    Prediction of acute pancreatitis severity based on early CT radiomics by Mingyao Qi, Chao Lu, Rao Dai, Jiulou Zhang, Hui Hu, Xiuhong Shan

    Published 2024-11-01
    “…Abstract Background This study aims to develop and validate an integrated predictive model combining CT radiomics and clinical parameters for early assessment of acute pancreatitis severity. …”
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    Article
  3. 1963

    Risk Prediction Models for Perioperative Hypothermia: A Systematic Review by Liu J, Liu F, Xu W, Du L, Li Y, Liang A, Li B, Zhang M

    Published 2025-07-01
    “…Due to its significant impact, this study systematically reviews and evaluates existing risk prediction models for perioperative hypothermia. …”
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    Article
  4. 1964

    Deformation prediction in innovative implant design with machine learning approaches by Mehmet Onur Yağır, Muhammed Fatih Pekşen, Şaduman Şen, Uğur Şen

    Published 2025-09-01
    “…Unlike classic implants, the sleeved implant is predicted to provide a more homogeneous distribution of chewing forces on the bone by reducing stress concentrations around the implant. …”
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    Article
  5. 1965
  6. 1966

    Bayesian network for predicting mandibular third molar extraction difficulty by Tian Meng, Zhiyong Zhang, Xiao Zhang, Chao Zhang

    Published 2025-01-01
    “…Abstract Background This study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results based on the patient’s personal situation; and (3) identify key surgical points and propose surgical protocols to decrease complications. …”
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    Article
  7. 1967

    Slope deformation prediction based on GA–BP neural networks by Wenhui TAN, Kai LI, Huimin LIU, Meifeng CAI, Qifeng GUO

    Published 2025-04-01
    “…Traditionally, empirical methods and numerical simulations have been employed to predict slope displacement. …”
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    Article
  8. 1968

    Interpretable machine learning for predicting isolated basal septal hypertrophy. by Lei Gao, Boyan Tian, Qiqi Jia, Xingyu He, Guannan Zhao, Yueheng Wang

    Published 2025-01-01
    “…<h4>Objective</h4>To evaluate the effectiveness of five machine learning algorithms in predicting thickening of the basal segment of the interventricular septum and to develop a simple, yet efficient, prediction model for BSH.…”
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    Article
  9. 1969

    Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats by Xiaoting Yao, Jiaxin Li, Jiaqi Fu, Xingquan Wang, Longgang Ma, Hojjat Asadollahpour Nanaei, Ali Mujtaba Shah, Zhuangbiao Zhang, Peipei Bian, Shishuo Zhou, Ao Wang, Xihong Wang, Yu Jiang

    Published 2025-01-01
    “…Genome-wide association studies (GWAS) revealed four candidate genes with selection signatures linked to udder traits. Predictive models, including GBLUP, kernel ridge regression (KRR), and Adaboost.RT, were evaluated for genomic estimated breeding value (GEBV) prediction. …”
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    Article
  10. 1970

    Comparison of Risk Assessment Models for Predicting Postpartum Venous Thromboembolism by Yonghui Xu, Sha Zhu, Ji He, XingSheng Xue, Fei Xiao

    Published 2025-05-01
    “…Methods: This retrospective study was conducted from February 2019 to February 2024. …”
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    Article
  11. 1971

    Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma by Abdul Basit Ahanger, Syed Wajid Aalam, Tariq Ahmad Masoodi, Asma Shah, Meraj Alam Khan, Ajaz A. Bhat, Assif Assad, Muzafar Ahmad Macha, Muzafar Rasool Bhat

    Published 2025-01-01
    “…This study explores the utility of radiogenomics and machine learning (ML) in predicting these oncogenic signaling pathways in GBM patients. …”
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    Article
  12. 1972

    Collagen turnover biomarkers to predict outcome of patients with biliary cancer by Leonard Kaps, Muhammed A. Genc, Markus Moehler, Stephan Grabbe, Jörn M. Schattenberg, Detlef Schuppan, Rasmus Sund Pedersen, Morten A. Karsdal, Philipp Mildenberger, Annett Maderer, Nicholas Willumsen

    Published 2025-02-01
    “…The diagnostic and prognostic value of the markers was evaluated for overall survival (OS) and progression-free survival (PFS). …”
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    Article
  13. 1973

    Change in elevation predicts 100 km ultra marathon performance by Beat Knechtle, Katja Weiss, David Valero, Volker Scheer, Elias Villiger, Pantelis T. Nikolaidis, Marilia Andrade, Ivan Cuk, Robert Gajda, Thomas Rosemann, Mabliny Thuany

    Published 2025-07-01
    “…A total of 858,544 race records (732,748 from men and 125,796 from women) from 317,312 unique runners originating from 103 different countries and participating in 2,648 100-km races held in 80 different countries worldwide between 1892 and 2022 were analyzed using several descriptive, inferential and predictive methods, including a machine learning XG Boost Regression model. …”
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    Article
  14. 1974

    Machine Learning for Non-Destructive Prediction of Sunflower Leaf Area by Joao Everthon da Silva Ribeiro, Antonio Gideilson Correia da Silva, Pablo Henrique de Almeida Oliveira, Josiana Micarla da Silva Oliveira, Alessandra Nunes da Silva, John Victor Lucas Lima, Ivan Euzebio da Silva, Ester Dos Santos Coelho, Isaque de Oliveira Leite, Elania Freire da Silva, Toshik Iarley da Silva, Lindomar Maria da Silveira, Aurelio Paes Barros Junior

    Published 2025-01-01
    “…However, its measurement using traditional methods can be limited. The search for non-destructive approaches based on leaf dimensions is essential, and machine learning offers promising alternatives for accurate estimation. …”
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    Article
  15. 1975

    Assessing sepsis-induced immunosuppression to predict positive blood cultures by Enrique Hernández-Jiménez, Enrique Hernández-Jiménez, Erika P. Plata-Menchaca, Erika P. Plata-Menchaca, Damaris Berbel, Damaris Berbel, Guillem López de Egea, Guillem López de Egea, Macarena Dastis-Arias, Laura García-Tejada, Fabrizio Sbraga, Pierre Malchair, Nadia García Muñoz, Alejandra Larrad Blasco, Eva Molina Ramírez, Xose Pérez Fernández, Joan Sabater Riera, Arnau Ulsamer

    Published 2024-11-01
    “…This study proposes a novel strategy to predict positive blood cultures by assessing sepsis-induced immunosuppression status through endotoxin tolerance assessment.MethodsOptimal assay conditions have been explored and tested in sepsis-suspected patients meeting the Sepsis-3 criteria. …”
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    Article
  16. 1976

    Plasma proteomic signature for preoperative prediction of microvascular invasion in HCC by Xinrui Shi, Yunzheng Zhao, Ke Li, Qingyu Li, Yifeng Cui, Yuhang Sui, Liang Zhao, Haonan Zhou, Yongsheng Yang, Jiajun Li, Meng Zhou, Zhaoyang Lu

    Published 2025-09-01
    “…However, reliable non-invasive biomarkers for the preoperative evaluation and diagnosis of MVI are urgently needed in clinical practice. …”
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    Article
  17. 1977

    The Predictive Role of StrainParameters in Predicting All-Cause Mortality in Diabetic Hypertensive Patients with Normal Left Ventricular Systolic Function in Long-Term Follow-up by Mustafa Çetin, Turhan Turan, Müjdat Aktaş, Tayyar Gökdeniz, Murat Gökhan Yerlikaya, Ezgi Kalaycıoğlu, Ender Emre, Ahmet Özderya

    Published 2025-03-01
    “…In right ventricular strain evaluation, four-chamber right ventricular strain (RV4CSL%) (26.1±5.4 vs. 20.8±6.2, p-value: 0.005) was also worse in the mortality group. …”
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    Article
  18. 1978

    Comparison of Classical Arima Forecasting Methods to the Machine Learning LSTM Method: a Case Study on DAX® 50 ESG Index by Rosinus, Manuel

    Published 2025-06-01
    “…Predictive accuracy is measured by standard error metrics (MAE, RMSE, MAPE) and the Diebold-Mariano test. …”
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    Article
  19. 1979

    Novel metabolic prognostic score for predicting survival in patients with cancer by Jinyu Shi, Chenan Liu, Xin Zheng, Yue Chen, Heyang Zhang, Tong Liu, Qi Zhang, Li Deng, Hanping Shi

    Published 2025-01-01
    “…The Kaplan–Meier method was used to evaluate the survival of patients with varying burdens of metabolic disorders. …”
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    Article
  20. 1980

    Can Phonemic Verbal Fluency Be Used to Predict Alzheimer’s Disease? by Sara García-González

    Published 2024-11-01
    “…Method: A verbal fluency task was administered to 25 patients with MCI and their respective control subjects. …”
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    Article