These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

177 related articles for article (PubMed ID: 34570785)

  • 1. Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.
    Hara K; Kobayashi Y; Tomio J; Ito Y; Svensson T; Ikesu R; Chung UI; Svensson AK
    PLoS One; 2021; 16(9):e0254394. PubMed ID: 34570785
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Association measures of claims-based algorithms for common chronic conditions were assessed using regularly collected data in Japan.
    Hara K; Tomio J; Svensson T; Ohkuma R; Svensson AK; Yamazaki T
    J Clin Epidemiol; 2018 Jul; 99():84-95. PubMed ID: 29548842
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Claims-Based Algorithms for Identifying Patients With Pulmonary Hypertension: A Comparison of Decision Rules and Machine-Learning Approaches.
    Ong MS; Klann JG; Lin KJ; Maron BA; Murphy SN; Natter MD; Mandl KD
    J Am Heart Assoc; 2020 Oct; 9(19):e016648. PubMed ID: 32990147
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Level of Agreement and Factors Associated With Discrepancies Between Nationwide Medical History Questionnaires and Hospital Claims Data.
    Kim YY; Park JH; Kang HJ; Lee EJ; Ha S; Shin SA
    J Prev Med Public Health; 2017 Sep; 50(5):294-302. PubMed ID: 29020761
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Using machine-learning approaches to predict non-participation in a nationwide general health check-up scheme.
    Shimoda A; Ichikawa D; Oyama H
    Comput Methods Programs Biomed; 2018 Sep; 163():39-46. PubMed ID: 30119856
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Performance of different machine learning algorithms in identifying undiagnosed diabetes based on nonlaboratory parameters and the influence of muscle strength: A cross-sectional study.
    Xu Y; Qiu S; Ye J; Chen D; Wang D; Zhou X; Sun Z
    J Diabetes Investig; 2024 Jun; 15(6):743-750. PubMed ID: 38439210
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database.
    Chen-Ying Hung ; Wei-Chen Chen ; Po-Tsun Lai ; Ching-Heng Lin ; Chi-Chun Lee
    Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():3110-3113. PubMed ID: 29060556
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021.
    Ebrahim OA; Derbew G
    Sci Rep; 2023 May; 13(1):7779. PubMed ID: 37179444
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A systematic comparison of machine learning algorithms to develop and validate prediction model to predict heart failure risk in middle-aged and elderly patients with periodontitis (NHANES 2009 to 2014).
    Wang Y; Xiao Y; Zhang Y
    Medicine (Baltimore); 2023 Aug; 102(34):e34878. PubMed ID: 37653785
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Risk Stratification for Early Detection of Diabetes and Hypertension in Resource-Limited Settings: Machine Learning Analysis.
    Boutilier JJ; Chan TCY; Ranjan M; Deo S
    J Med Internet Res; 2021 Jan; 23(1):e20123. PubMed ID: 33475518
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Machine learning algorithm for characterizing risks of hypertension, at an early stage in Bangladesh.
    Islam MM; Rahman MJ; Chandra Roy D; Tawabunnahar M; Jahan R; Ahmed NAMF; Maniruzzaman M
    Diabetes Metab Syndr; 2021; 15(3):877-884. PubMed ID: 33892404
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis.
    Li W; Huang Y; Zhuang BW; Liu GJ; Hu HT; Li X; Liang JY; Wang Z; Huang XW; Zhang CQ; Ruan SM; Xie XY; Kuang M; Lu MD; Chen LD; Wang W
    Eur Radiol; 2019 Mar; 29(3):1496-1506. PubMed ID: 30178143
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning to identify chronic cough from administrative claims data.
    Bali V; Turzhitsky V; Schelfhout J; Paudel M; Hulbert E; Peterson-Brandt J; Hertzberg J; Kelly NR; Patel RH
    Sci Rep; 2024 Jan; 14(1):2449. PubMed ID: 38291064
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Logistic LASSO and Elastic Net to Characterize Vitamin D Deficiency in a Hypertensive Obese Population.
    Garcia-Carretero R; Vigil-Medina L; Barquero-Perez O; Mora-Jimenez I; Soguero-Ruiz C; Goya-Esteban R; Ramos-Lopez J
    Metab Syndr Relat Disord; 2020 Mar; 18(2):79-85. PubMed ID: 31928513
    [No Abstract]   [Full Text] [Related]  

  • 16. Longitudinal study on pediatric dyslipidemia in population-based claims database.
    Li J; Motsko SP; Goehring EL; Vendiola R; Maneno M; Jones JK
    Pharmacoepidemiol Drug Saf; 2010 Jan; 19(1):90-8. PubMed ID: 20035528
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Classification of patients with chronic disease by activation level using machine learning methods.
    Demiray O; Gunes ED; Kulak E; Dogan E; Karaketir SG; Cifcili S; Akman M; Sakarya S
    Health Care Manag Sci; 2023 Dec; 26(4):626-650. PubMed ID: 37824033
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Asymptomatic Hyperuricemia Without Comorbidities Predicts Cardiometabolic Diseases: Five-Year Japanese Cohort Study.
    Kuwabara M; Niwa K; Hisatome I; Nakagawa T; Roncal-Jimenez CA; Andres-Hernando A; Bjornstad P; Jensen T; Sato Y; Milagres T; Garcia G; Ohno M; Lanaspa MA; Johnson RJ
    Hypertension; 2017 Jun; 69(6):1036-1044. PubMed ID: 28396536
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain.
    Tighe PJ; Harle CA; Hurley RW; Aytug H; Boezaart AP; Fillingim RB
    Pain Med; 2015 Jul; 16(7):1386-401. PubMed ID: 26031220
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Early Diabetes Prediction: A Comparative Study Using Machine Learning Techniques.
    Poly TN; Islam MM; Li YJ
    Stud Health Technol Inform; 2022 Jun; 295():409-413. PubMed ID: 35773898
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.