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    A predictive machine-learning model for clinical decision-making in washed microbiota transplantation on ulcerative colitis by Sheng Zhang, Gaochen Lu, Weihong Wang, Qianqian Li, Rui Wang, Zulun Zhang, Xia Wu, Chenchen Liang, Yujie Liu, Pan Li, Quan Wen, Bota Cui, Faming Zhang

    Published 2024-12-01
    “…Besides, the voting ensembles exhibited an area under curve (AUC) of 0.769 ± 0.019 [accuracy, 0.754; F1-score, 0.845] in the internal validation; the AUC of the external validation was 0.614 ± 0.017 [accuracy, 0.801; F1-score, 0.887]. Additionally, the model was available at https://wmtpredict.streamlit.app.ConclusionsThis study pioneered the development of a machine learning model to predict the one-month clinical response of WMT on UC. …”
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    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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    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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    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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    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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