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Journal Abstract Search

221 related items for PubMed ID: 35098205

  • 21.
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  • 22. Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods.
    Kivrak M, Guldogan E, Colak C.
    Comput Methods Programs Biomed; 2021 Apr; 201():105951. PubMed ID: 33513487
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  • 24. Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis.
    Pattharanitima P, Thongprayoon C, Kaewput W, Qureshi F, Qureshi F, Petnak T, Srivali N, Gembillo G, O'Corragain OA, Chesdachai S, Vallabhajosyula S, Guru PK, Mao MA, Garovic VD, Dillon JJ, Cheungpasitporn W.
    J Clin Med; 2021 Oct 28; 10(21):. PubMed ID: 34768540
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  • 25. Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization.
    Zhou H, Liu L, Zhao Q, Jin X, Peng Z, Wang W, Huang L, Xie Y, Xu H, Tao L, Xiao X, Nie W, Liu F, Li L, Yuan Q.
    Front Immunol; 2023 Oct 28; 14():1140755. PubMed ID: 37077912
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  • 29. Application of boosted trees to the prognosis prediction of COVID-19.
    Molaei S, Moazen H, Niazkar HR, Sabaei M, Johari MG, Rezaianzadeh A.
    Health Sci Rep; 2024 May 28; 7(5):e2104. PubMed ID: 38784249
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  • 33. Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests.
    Çubukçu HC, Topcu Dİ, Bayraktar N, Gülşen M, Sarı N, Arslan AH.
    Am J Clin Pathol; 2022 May 04; 157(5):758-766. PubMed ID: 34791032
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  • 34. Prediction of low cardiac output syndrome in patients following cardiac surgery using machine learning.
    Hong L, Xu H, Ge C, Tao H, Shen X, Song X, Guan D, Zhang C.
    Front Med (Lausanne); 2022 May 04; 9():973147. PubMed ID: 36091676
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  • 35. A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms.
    Aznar-Gimeno R, Esteban LM, Labata-Lezaun G, Del-Hoyo-Alonso R, Abadia-Gallego D, Paño-Pardo JR, Esquillor-Rodrigo MJ, Lanas Á, Serrano MT.
    Int J Environ Res Public Health; 2021 Aug 17; 18(16):. PubMed ID: 34444425
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  • 36. A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation.
    Chen Y, Ouyang L, Bao FS, Li Q, Han L, Zhang H, Zhu B, Ge Y, Robinson P, Xu M, Liu J, Chen S.
    J Med Internet Res; 2021 Apr 07; 23(4):e23948. PubMed ID: 33714935
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  • 37. [Prediction of intensive care unit readmission for critically ill patients based on ensemble learning].
    Lin Y, Wu JY, Lin K, Hu YH, Kong GL.
    Beijing Da Xue Xue Bao Yi Xue Ban; 2021 Jun 18; 53(3):566-572. PubMed ID: 34145862
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  • 38. Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach.
    Awad A, Bader-El-Den M, McNicholas J, Briggs J.
    Int J Med Inform; 2017 Dec 18; 108():185-195. PubMed ID: 29132626
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  • 39. An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach.
    Liu CF, Hung CM, Ko SC, Cheng KC, Chao CM, Sung MI, Hsing SC, Wang JJ, Chen CJ, Lai CC, Chen CM, Chiu CC.
    Front Med (Lausanne); 2022 Dec 18; 9():935366. PubMed ID: 36465940
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  • 40. Development of a novel machine learning model based on laboratory and imaging indices to predict acute cardiac injury in cancer patients with COVID-19 infection: a retrospective observational study.
    Wan G, Wu X, Zhang X, Sun H, Yu X.
    J Cancer Res Clin Oncol; 2023 Dec 18; 149(19):17039-17050. PubMed ID: 37747525
    [Abstract] [Full Text] [Related]

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