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Journal Abstract Search

157 related items for PubMed ID: 37897663

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  • 3. MRI-based interpretable radiomics nomogram for discrimination between Brucella spondylitis and Pyogenic spondylitis.
    Yasin P, Yimit Y, Abliz D, Mardan M, Xu T, Yusufu A, Cai X, Sheng W, Mamat M.
    Heliyon; 2024 Jan 15; 10(1):e23584. PubMed ID: 38173524
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  • 4. 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
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  • 5. Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study.
    Feng S, Wang S, Liu C, Wu S, Zhang B, Lu C, Huang C, Chen T, Zhou C, Zhu J, Chen J, Xue J, Wei W, Zhan X.
    Sci Rep; 2024 Apr 02; 14(1):7691. PubMed ID: 38565845
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  • 6. Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis.
    Zhu J, Lu Q, Liang T, JieJiang, Li H, Zhou C, Wu S, Chen T, Chen J, Deng G, Yao Y, Liao S, Yu C, Huang S, Sun X, Chen L, Chen W, Ye Z, Guo H, Chen W, Jiang W, Fan B, Tao X, Zhan X, Liu C.
    Rheumatol Ther; 2022 Oct 02; 9(5):1377-1397. PubMed ID: 35932360
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  • 8. Comparison of Machine Learning Algorithms and Nomogram Construction for Diabetic Retinopathy Prediction in Type 2 Diabetes Mellitus Patients.
    Jiang W, Li Z.
    Ophthalmic Res; 2024 Oct 02; 67(1):537-548. PubMed ID: 39231456
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  • 10. [Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].
    Xie Z, Jin J, Liu D, Lu S, Yu H, Han D, Sun W, Huang M.
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2024 Apr 02; 36(4):345-352. PubMed ID: 38813626
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  • 11. A diagnostic model for differentiating tuberculous spondylitis from pyogenic spondylitis: a retrospective case-control study.
    Liu YX, Lei F, Zheng LP, Yuan H, Zhou QZ, Feng DX.
    Sci Rep; 2023 Jun 26; 13(1):10337. PubMed ID: 37365238
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  • 13. Predicting colorectal cancer risk: a novel approach using anemia and blood test markers.
    Zhang Z, Zhang T, Zhang R, Zhu X, Wu X, Tan S, Jian Z.
    Front Oncol; 2024 Jun 26; 14():1347058. PubMed ID: 38344199
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  • 15. Development of a Diagnostic Model for Differentiating Tuberculous Spondylitis and Pyogenic Spondylitis With MRI: A Multicenter Retrospective Observational Study.
    Wang J, Li Z, Chi X, Chen Y, Wang H, Wang X, Cui K, Wang Q, Lu T, Zheng J, Zhang Q, Hao Y.
    Spine (Phila Pa 1976); 2024 Jan 01; 49(1):34-45. PubMed ID: 37796171
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  • 16. Monocyte-to-Lymphocyte Ratio Was an Independent Factor of the Severity of Spinal Tuberculosis.
    Chen L, Liu C, Liang T, Ye Z, Huang S, Chen J, Sun X, Yi M, Jiang J, Chen T, Li H, Chen W, Guo H, Yao Y, Liao S, Yu C, Wu S, Fan B, Gan Z, Zhan X.
    Oxid Med Cell Longev; 2022 Jan 01; 2022():7340330. PubMed ID: 35633888
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  • 18. Analysis of T-lymphocyte subsets and risk factors in children with tuberculosis.
    Ma WW, Wang LC, Zhao DA, Wei N, Cui JW, Li SJ.
    Tuberculosis (Edinb); 2024 May 01; 146():102496. PubMed ID: 38401266
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  • 19. Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm.
    Sun T, Liu J, Yuan H, Li X, Yan H.
    Front Oncol; 2024 May 01; 14():1403392. PubMed ID: 39184040
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  • 20. Derivation and validation of the first web-based nomogram to predict the spontaneous pregnancy after reproductive surgery using machine learning models.
    Liu Z, Wang M, He S, Wang X, Liu X, Xie X, Bao H.
    Front Endocrinol (Lausanne); 2024 May 01; 15():1378157. PubMed ID: 39015183
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