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  1. 2001
  2. 2002

    Development and validation of a predictive model for critical illness in adult patients requiring hospitalization for COVID-19. by Neha Paranjape, Lauren L Staples, Christina Y Stradwick, Herman Gene Ray, Ian J Saldanha

    Published 2021-01-01
    “…We developed a free, web-based calculator to facilitate use of the prediction model (https://icucovid19.shinyapps.io/ICUCOVID19/).…”
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    Article
  3. 2003

    Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors by Qianjin Lu, Ying Zhang, Cheng Zhao, Yu Lei, Hui Jin, Qilin Li

    Published 2024-11-01
    “…Leave-one-out cross-validation confirmed that the ForestMDG model had the best accuracy (0.8338). Finally, we developed a dynamic nomogram for practical use, which is accessible via the following link: https://yingzhang99321.shinyapps.io/dynnomapp/.Conclusion We created a user-friendly dynamic nomogram for predicting the relative risk of SLE onset based on occupational and living environmental exposures.Trial registration number ChiCTR2000038187.…”
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    Article
  4. 2004
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  6. 2006
  7. 2007

    Clinical characteristics, outcomes, and predictive modeling of patients diagnosed with immune checkpoint inhibitor therapy-related pneumonitis by Antonious Hazim, Irene Riestra Guiance, Jacob Shreve, Gordon Ruan, Damian McGlothlin, Allison LeMahieu, Robert Haemmerle, Keith Mcconn, Richard C. Godby, Lisa Kottschade, Anna Schwecke, Casey Fazer-Posorske, Tobias Peikert, Eric Edell, Konstantinos Leventakos, Ashley Egan

    Published 2025-05-01
    “…The grading of pneumonitis was defined in accordance with ASCO guidelines (Schneider et al. in J Clin Oncol 39(36):4073–4126, 2021. https://doi.org/10.1200/JCO.21.01440 ). Predictive modeling was performed using gradient boosting machine learning technology, XGBoost (Chen in 1(4):1, 2015), to conduct binary classification and model reverse engineering using Shapley statistics (Lundberg and Lee in Adv Neural Inf Process Syst 30, 2017). …”
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    Article
  8. 2008

    Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis by Jinxi Yang, Siyao Zeng, Shanpeng Cui, Junbo Zheng, Hongliang Wang

    Published 2025-05-01
    “…While machine learning (ML) models are increasingly being used for ARDS prediction, there is a lack of consensus on the most effective model or methodology. …”
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    Article
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  20. 2020