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Journal Abstract Search

423 related items for PubMed ID: 39054495

  • 1. Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).
    Yasin P, Yimit Y, Cai X, Aimaiti A, Sheng W, Mamat M, Nijiati M.
    Eur J Med Res; 2024 Jul 25; 29(1):383. PubMed ID: 39054495
    [Abstract] [Full Text] [Related]

  • 2. An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease.
    Huang D, Gong L, Wei C, Wang X, Liang Z.
    Respir Res; 2024 Jun 18; 25(1):246. PubMed ID: 38890628
    [Abstract] [Full Text] [Related]

  • 3. Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up.
    Dai C, Fan Y, Li Y, Bao X, Li Y, Su M, Yao Y, Deng K, Xing B, Feng F, Feng M, Wang R.
    Front Endocrinol (Lausanne); 2020 Jun 18; 11():643. PubMed ID: 33042013
    [Abstract] [Full Text] [Related]

  • 4. Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation.
    Zhou S, Lu Z, Liu Y, Wang M, Zhou W, Cui X, Zhang J, Xiao W, Hua T, Zhu H, Yang M.
    Eur J Med Res; 2024 Jan 03; 29(1):14. PubMed ID: 38172962
    [Abstract] [Full Text] [Related]

  • 5. Investigation on explainable machine learning models to predict chronic kidney diseases.
    Ghosh SK, Khandoker AH.
    Sci Rep; 2024 Feb 14; 14(1):3687. PubMed ID: 38355876
    [Abstract] [Full Text] [Related]

  • 6. Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion.
    Karabacak M, Bhimani AD, Schupper AJ, Carr MT, Steinberger J, Margetis K.
    BMC Musculoskelet Disord; 2024 May 21; 25(1):401. PubMed ID: 38773464
    [Abstract] [Full Text] [Related]

  • 7. Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods.
    Zhang B, Huang S, Zhou C, Zhu J, Chen T, Feng S, Huang C, Wang Z, Wu S, Liu C, Zhan X.
    Comput Assist Surg (Abingdon); 2024 Dec 21; 29(1):2345066. PubMed ID: 38860617
    [Abstract] [Full Text] [Related]

  • 8. Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis.
    Zeleke AJ, Palumbo P, Tubertini P, Miglio R, Chiari L.
    Front Artif Intell; 2023 Dec 21; 6():1179226. PubMed ID: 37588696
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  • 10. Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis.
    Cui Y, Shi X, Qin Y, Wang Q, Cao X, Che X, Pan Y, Wang B, Lei M, Liu Y.
    Int J Surg; 2024 May 01; 110(5):2738-2756. PubMed ID: 38376838
    [Abstract] [Full Text] [Related]

  • 11. Development and validation of a diagnostic model for differentiating tuberculous spondylitis from brucellar spondylitis using machine learning: A retrospective cohort study.
    Yasin P, Mardan M, Xu T, Cai X, Abulizi Y, Wang T, Sheng W, Mamat M.
    Front Surg; 2022 May 01; 9():955761. PubMed ID: 36684365
    [Abstract] [Full Text] [Related]

  • 12. Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.
    Shi H, Yang D, Tang K, Hu C, Li L, Zhang L, Gong T, Cui Y.
    Clin Nutr; 2022 Jan 01; 41(1):202-210. PubMed ID: 34906845
    [Abstract] [Full Text] [Related]

  • 13. Development and validation of a novel nomogram to predict the risk of the prolonged postoperative length of stay for lumbar spinal stenosis patients.
    Yasin P, Cai X, Mardan M, Xu T, Abulizi Y, Aimaiti A, Yang H, Sheng W, Mamat M.
    BMC Musculoskelet Disord; 2023 Sep 02; 24(1):703. PubMed ID: 37660009
    [Abstract] [Full Text] [Related]

  • 14. Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP.
    Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA.
    Sci Rep; 2023 Jun 02; 13(1):8984. PubMed ID: 37268685
    [Abstract] [Full Text] [Related]

  • 15. Predicting cerebral edema in patients with spontaneous intracerebral hemorrhage using machine learning.
    Xu J, Yuan C, Yu G, Li H, Dong Q, Mao D, Zhan C, Yan X.
    Front Neurol; 2024 Jun 02; 15():1419608. PubMed ID: 39421568
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  • 18. Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery.
    Chen X, Pan J, Li Y, Tang R.
    Aging Clin Exp Res; 2023 Nov 02; 35(11):2643-2656. PubMed ID: 37733228
    [Abstract] [Full Text] [Related]

  • 19. Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases.
    Peng S, Huang J, Liu X, Deng J, Sun C, Tang J, Chen H, Cao W, Wang W, Duan X, Luo X, Peng S.
    Front Cardiovasc Med; 2022 Nov 02; 9():994359. PubMed ID: 36312291
    [Abstract] [Full Text] [Related]

  • 20. Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation.
    Pan P, Li Y, Xiao Y, Han B, Su L, Su M, Li Y, Zhang S, Jiang D, Chen X, Zhou F, Ma L, Bao P, Xie L.
    J Med Internet Res; 2020 Nov 11; 22(11):e23128. PubMed ID: 33035175
    [Abstract] [Full Text] [Related]

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