BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

221 related articles for article (PubMed ID: 30189255)

  • 1. Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c.
    Ngufor C; Van Houten H; Caffo BS; Shah ND; McCoy RG
    J Biomed Inform; 2019 Jan; 89():56-67. PubMed ID: 30189255
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data.
    Del Parigi A; Tang W; Liu D; Lee C; Pratley R
    Pharmaceut Med; 2019 Jun; 33(3):209-217. PubMed ID: 31933292
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms.
    Nagaraj SB; Sidorenkov G; van Boven JFM; Denig P
    Diabetes Obes Metab; 2019 Dec; 21(12):2704-2711. PubMed ID: 31453664
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Trajectories of Glycemic Change in a National Cohort of Adults With Previously Controlled Type 2 Diabetes.
    McCoy RG; Ngufor C; Van Houten HK; Caffo B; Shah ND
    Med Care; 2017 Nov; 55(11):956-964. PubMed ID: 28922296
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Longitudinal trajectories of glycemic control among U.S. Adults with newly diagnosed diabetes.
    McCoy RG; Faust L; Heien HC; Patel S; Caffo B; Ngufor C
    Diabetes Res Clin Pract; 2023 Nov; 205():110989. PubMed ID: 37918637
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Proof-of-Concept Study of Using Supervised Machine Learning Algorithms to Predict Self-Care and Glycemic Control in Type 1 Diabetes Patients on Insulin Pump Therapy.
    Kurdi S; Alamer A; Wali H; Badr AF; Pendergrass ML; Ahmed N; Abraham I; Fazel MT
    Endocr Pract; 2023 Jun; 29(6):448-455. PubMed ID: 36898528
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records.
    Alhassan Z; Watson M; Budgen D; Alshammari R; Alessa A; Al Moubayed N
    JMIR Med Inform; 2021 May; 9(5):e25237. PubMed ID: 34028357
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes.
    Fu X; Wang Y; Cates RS; Li N; Liu J; Ke D; Liu J; Liu H; Yan S
    Front Endocrinol (Lausanne); 2022; 13():1061507. PubMed ID: 36743935
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms.
    Tao X; Jiang M; Liu Y; Hu Q; Zhu B; Hu J; Guo W; Wu X; Xiong Y; Shi X; Zhang X; Han X; Li W; Tong R; Long E
    Sci Rep; 2023 Sep; 13(1):16437. PubMed ID: 37777593
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.
    Hathaway QA; Roth SM; Pinti MV; Sprando DC; Kunovac A; Durr AJ; Cook CC; Fink GK; Cheuvront TB; Grossman JH; Aljahli GA; Taylor AD; Giromini AP; Allen JL; Hollander JM
    Cardiovasc Diabetol; 2019 Jun; 18(1):78. PubMed ID: 31185988
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Longitudinal effects of medication nonadherence on glycemic control.
    Egede LE; Gebregziabher M; Echols C; Lynch CP
    Ann Pharmacother; 2014 May; 48(5):562-70. PubMed ID: 24586059
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models.
    Motaib I; Aitlahbib F; Fadil A; Z Rhmari Tlemcani F; Elamari S; Laidi S; Chadli A
    Diabetes Res Clin Pract; 2022 Aug; 190():109982. PubMed ID: 35803316
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Survival prediction models: an introduction to discrete-time modeling.
    Suresh K; Severn C; Ghosh D
    BMC Med Res Methodol; 2022 Jul; 22(1):207. PubMed ID: 35883032
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning for modeling animal movement.
    Wijeyakulasuriya DA; Eisenhauer EW; Shaby BA; Hanks EM
    PLoS One; 2020; 15(7):e0235750. PubMed ID: 32716917
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Accuracy and robustness of dynamical tracking of average glycemia (A1c) to provide real-time estimation of hemoglobin A1c using routine self-monitored blood glucose data.
    Kovatchev BP; Flacke F; Sieber J; Breton MD
    Diabetes Technol Ther; 2014 May; 16(5):303-9. PubMed ID: 24299302
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease.
    Zou Y; Zhao L; Zhang J; Wang Y; Wu Y; Ren H; Wang T; Zhang R; Wang J; Zhao Y; Qin C; Xu H; Li L; Chai Z; Cooper ME; Tong N; Liu F
    Ren Fail; 2022 Dec; 44(1):562-570. PubMed ID: 35373711
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study.
    Ye Y; Xiong Y; Zhou Q; Wu J; Li X; Xiao X
    J Diabetes Res; 2020; 2020():4168340. PubMed ID: 32626780
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Ten-year hemoglobin A1c trajectories and outcomes in type 2 diabetes mellitus: The Diabetes & Aging Study.
    Laiteerapong N; Karter AJ; Moffet HH; Cooper JM; Gibbons RD; Liu JY; Gao Y; Huang ES
    J Diabetes Complications; 2017 Jan; 31(1):94-100. PubMed ID: 27503405
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Marginal Structural Models Using Calibrated Weights With SuperLearner: Application to Type II Diabetes Cohort.
    Kalia S; Saarela O; Chen T; O'Neill B; Meaney C; Gronsbell J; Sejdic E; Escobar M; Aliarzadeh B; Moineddin R; Pow C; Sullivan F; Greiver M
    IEEE J Biomed Health Inform; 2022 Aug; 26(8):4197-4206. PubMed ID: 35588417
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 12.