BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

343 related articles for article (PubMed ID: 30971285)

  • 1. Comparison and development of machine learning tools in the prediction of chronic kidney disease progression.
    Xiao J; Ding R; Xu X; Guan H; Feng X; Sun T; Zhu S; Ye Z
    J Transl Med; 2019 Apr; 17(1):119. PubMed ID: 30971285
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of response after cardiac resynchronization therapy with machine learning.
    Liang Y; Ding R; Wang J; Gong X; Yu Z; Pan L; Huang J; Li R; Su Y; Zhu S; Ge J
    Int J Cardiol; 2021 Dec; 344():120-126. PubMed ID: 34592246
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods.
    Yahya N; Ebert MA; Bulsara M; House MJ; Kennedy A; Joseph DJ; Denham JW
    Med Phys; 2016 May; 43(5):2040. PubMed ID: 27147316
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model.
    Bozorgmehr A; Thielmann A; Weltermann B
    PLoS One; 2021; 16(5):e0250842. PubMed ID: 33945572
    [TBL] [Abstract][Full Text] [Related]  

  • 5. [Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients].
    Tang CQ; Li JQ; Xu DY; Liu XB; Hou WJ; Lyu KY; Xiao SC; Xia ZF
    Zhonghua Shao Shang Za Zhi; 2018 Jun; 34(6):343-348. PubMed ID: 29961290
    [No Abstract]   [Full Text] [Related]  

  • 6. Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation.
    Afzali MH; Sunderland M; Stewart S; Masse B; Seguin J; Newton N; Teesson M; Conrod P
    Addiction; 2019 Apr; 114(4):662-671. PubMed ID: 30461117
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: A machine learning-based prediction model by using a big database.
    Inaguma D; Kitagawa A; Yanagiya R; Koseki A; Iwamori T; Kudo M; Yuzawa Y
    PLoS One; 2020; 15(9):e0239262. PubMed ID: 32941535
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Emergency department triage prediction of clinical outcomes using machine learning models.
    Raita Y; Goto T; Faridi MK; Brown DFM; Camargo CA; Hasegawa K
    Crit Care; 2019 Feb; 23(1):64. PubMed ID: 30795786
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Machine learning models in breast cancer survival prediction.
    Montazeri M; Montazeri M; Montazeri M; Beigzadeh A
    Technol Health Care; 2016; 24(1):31-42. PubMed ID: 26409558
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Novel Urinary Biomarkers For Improved Prediction Of Progressive Egfr Loss In Early Chronic Kidney Disease Stages And In High Risk Individuals Without Chronic Kidney Disease.
    Rodríguez-Ortiz ME; Pontillo C; Rodríguez M; Zürbig P; Mischak H; Ortiz A
    Sci Rep; 2018 Oct; 8(1):15940. PubMed ID: 30374033
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting hospitalization following psychiatric crisis care using machine learning.
    Blankers M; van der Post LFM; Dekker JJM
    BMC Med Inform Decis Mak; 2020 Dec; 20(1):332. PubMed ID: 33302948
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy.
    Wong NC; Lam C; Patterson L; Shayegan B
    BJU Int; 2019 Jan; 123(1):51-57. PubMed ID: 29969172
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A machine learning approach to predict early outcomes after pituitary adenoma surgery.
    Hollon TC; Parikh A; Pandian B; Tarpeh J; Orringer DA; Barkan AL; McKean EL; Sullivan SE
    Neurosurg Focus; 2018 Nov; 45(5):E8. PubMed ID: 30453460
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Risk factor mining and prediction of urine protein progression in chronic kidney disease: a machine learning- based study.
    Lu Y; Ning Y; Li Y; Zhu B; Zhang J; Yang Y; Chen W; Yan Z; Chen A; Shen B; Fang Y; Wang D; Song N; Ding X
    BMC Med Inform Decis Mak; 2023 Aug; 23(1):173. PubMed ID: 37653403
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Individualized prediction of chronic kidney disease for the elderly in longevity areas in China: Machine learning approaches.
    Su D; Zhang X; He K; Chen Y; Wu N
    Front Public Health; 2022; 10():998549. PubMed ID: 36339144
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population.
    Ma X; Wu Y; Zhang L; Yuan W; Yan L; Fan S; Lian Y; Zhu X; Gao J; Zhao J; Zhang P; Tang H; Jia W
    J Transl Med; 2020 Mar; 18(1):146. PubMed ID: 32234053
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Identification of clinically relevant features in hypertensive patients using penalized regression: a case study of cardiovascular events.
    Garcia-Carretero R; Barquero-Perez O; Mora-Jimenez I; Soguero-Ruiz C; Goya-Esteban R; Ramos-Lopez J
    Med Biol Eng Comput; 2019 Sep; 57(9):2011-2026. PubMed ID: 31346948
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Logistic regression was as good as machine learning for predicting major chronic diseases.
    Nusinovici S; Tham YC; Chak Yan MY; Wei Ting DS; Li J; Sabanayagam C; Wong TY; Cheng CY
    J Clin Epidemiol; 2020 Jun; 122():56-69. PubMed ID: 32169597
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting post-stroke pneumonia using deep neural network approaches.
    Ge Y; Wang Q; Wang L; Wu H; Peng C; Wang J; Xu Y; Xiong G; Zhang Y; Yi Y
    Int J Med Inform; 2019 Dec; 132():103986. PubMed ID: 31629312
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Clinical prediction models for progression of chronic kidney disease to end-stage kidney failure under pre-dialysis nephrology care: results from the Chronic Kidney Disease Japan Cohort Study.
    Hasegawa T; Sakamaki K; Koiwa F; Akizawa T; Hishida A;
    Clin Exp Nephrol; 2019 Feb; 23(2):189-198. PubMed ID: 30069609
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 18.