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Pubmed for Handhelds

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Journal Abstract Search

414 related items for PubMed ID: 35934714

  • 41. Risk assessment of imported malaria in China: a machine learning perspective.
    Yang S, Li RY, Yan SN, Yang HY, Cao ZY, Zhang L, Xue JB, Xia ZG, Xia S, Zheng B.
    BMC Public Health; 2024 Mar 20; 24(1):865. PubMed ID: 38509529
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  • 43. Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke.
    Kim SH, Jeon ET, Yu S, Oh K, Kim CK, Song TJ, Kim YJ, Heo SH, Park KY, Kim JM, Park JH, Choi JC, Park MS, Kim JT, Choi KH, Hwang YH, Kim BJ, Chung JW, Bang OY, Kim G, Seo WK, Jung JM.
    Sci Rep; 2021 Oct 18; 11(1):20610. PubMed ID: 34663874
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  • 44. Machine Learning-Based Prediction of Subsequent Vascular Events After 6 Months in Chinese Patients with Minor Ischemic Stroke.
    Zhang R, Wang J.
    Int J Gen Med; 2022 Oct 18; 15():3797-3808. PubMed ID: 35418774
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  • 47. Machine learning random forest for predicting oncosomatic variant NGS analysis.
    Pellegrino E, Jacques C, Beaufils N, Nanni I, Carlioz A, Metellus P, Ouafik L.
    Sci Rep; 2021 Nov 08; 11(1):21820. PubMed ID: 34750410
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  • 51. A weighted genetic risk score using all known susceptibility variants to estimate rheumatoid arthritis risk.
    Yarwood A, Han B, Raychaudhuri S, Bowes J, Lunt M, Pappas DA, Kremer J, Greenberg JD, Plenge R, Rheumatoid Arthritis Consortium International (RACI), Worthington J, Barton A, Eyre S.
    Ann Rheum Dis; 2015 Jan 08; 74(1):170-6. PubMed ID: 24092415
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  • 56. Classification and Identification of Contaminants in Recyclable Containers Based on a Recursive Feature Elimination-Light Gradient Boosting Machine Algorithm Using an Electronic Nose.
    Ba F, Peng P, Zhang Y, Zhao Y.
    Micromachines (Basel); 2023 Oct 31; 14(11):. PubMed ID: 38004904
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  • 57. A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.
    Lee M, Park T, Shin JY, Park M.
    Sci Rep; 2024 Aug 01; 14(1):17851. PubMed ID: 39090161
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  • 58. Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy.
    Yang SW, Hyon YK, Na HS, Jin L, Lee JG, Park JM, Lee JY, Shin JH, Lim JS, Na YG, Jeon K, Ha T, Kim J, Song KH.
    BMC Urol; 2020 Jul 03; 20(1):88. PubMed ID: 32620102
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