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 *

190 related articles for article (PubMed ID: 33808743)

  • 1. Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning.
    Sajeev S; Champion S; Beleigoli A; Chew D; Reed RL; Magliano DJ; Shaw JE; Milne RL; Appleton S; Gill TK; Maeder A
    Int J Environ Res Public Health; 2021 Mar; 18(6):. PubMed ID: 33808743
    [TBL] [Abstract][Full Text] [Related]  

  • 2. External validation and comparison of four cardiovascular risk prediction models with data from the Australian Diabetes, Obesity and Lifestyle study.
    Albarqouni L; Doust JA; Magliano D; Barr EL; Shaw JE; Glasziou PP
    Med J Aust; 2019 Mar; 210(4):161-167. PubMed ID: 30656697
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.
    Alaa AM; Bolton T; Di Angelantonio E; Rudd JHF; van der Schaar M
    PLoS One; 2019; 14(5):e0213653. PubMed ID: 31091238
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Comparing the performance of machine learning and conventional models for predicting atherosclerotic cardiovascular disease in a general Chinese population.
    Fan Z; Du Z; Fu J; Zhou Y; Zhang P; Shi C; Sun Y
    BMC Med Inform Decis Mak; 2023 Jul; 23(1):134. PubMed ID: 37488520
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Cardiovascular risk prediction in a population with the metabolic syndrome: Framingham vs. UKPDS algorithms.
    Zomer E; Liew D; Owen A; Magliano DJ; Ademi Z; Reid CM
    Eur J Prev Cardiol; 2014 Mar; 21(3):384-90. PubMed ID: 22588087
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.
    Zack CJ; Senecal C; Kinar Y; Metzger Y; Bar-Sinai Y; Widmer RJ; Lennon R; Singh M; Bell MR; Lerman A; Gulati R
    JACC Cardiovasc Interv; 2019 Jul; 12(14):1304-1311. PubMed ID: 31255564
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Framingham risk score conventional risk factors are potent to predict all-cause mortality using machine learning algorithms: a population-based prospective cohort study over 40 years in China.
    Huang Q; Zeng T; Zhang J; Min J; Zheng J; Tian S; Huang H; Liu X; Zhang H; Wang P; Hu X; Chen L
    J Investig Med; 2023 Aug; 71(6):586-590. PubMed ID: 37144834
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Development of machine learning prediction models to explore nutrients predictive of cardiovascular disease using Canadian linked population-based data.
    Morgenstern JD; Rosella LC; Costa AP; Anderson LN
    Appl Physiol Nutr Metab; 2022 May; 47(5):529-546. PubMed ID: 35113677
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Validation and recalibration of the Framingham cardiovascular disease risk models in an Australian Indigenous cohort.
    Hua X; McDermott R; Lung T; Wenitong M; Tran-Duy A; Li M; Clarke P
    Eur J Prev Cardiol; 2017 Oct; 24(15):1660-1669. PubMed ID: 28749178
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Cardiometabolic risk estimation using exposome data and machine learning.
    Atehortúa A; Gkontra P; Camacho M; Diaz O; Bulgheroni M; Simonetti V; Chadeau-Hyam M; Felix JF; Sebert S; Lekadir K
    Int J Med Inform; 2023 Nov; 179():105209. PubMed ID: 37729839
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Genetic Risk Score Increased Discriminant Efficiency of Predictive Models for Type 2 Diabetes Mellitus Using Machine Learning: Cohort Study.
    Wang Y; Zhang L; Niu M; Li R; Tu R; Liu X; Hou J; Mao Z; Wang Z; Wang C
    Front Public Health; 2021; 9():606711. PubMed ID: 33681127
    [No Abstract]   [Full Text] [Related]  

  • 12. Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar.
    Li Y; Sperrin M; Ashcroft DM; van Staa TP
    BMJ; 2020 Nov; 371():m3919. PubMed ID: 33148619
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: Comparison with traditional risk prediction approaches.
    Han D; Kolli KK; Gransar H; Lee JH; Choi SY; Chun EJ; Han HW; Park SH; Sung J; Jung HO; Min JK; Chang HJ
    J Cardiovasc Comput Tomogr; 2020; 14(2):168-176. PubMed ID: 31570323
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Independent external validation of cardiovascular disease mortality in women utilising Framingham and SCORE risk models: a mortality follow-up study.
    Goh LG; Welborn TA; Dhaliwal SS
    BMC Womens Health; 2014 Sep; 14():118. PubMed ID: 25255986
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Development of Health Parameter Model for Risk Prediction of CVD Using SVM.
    Unnikrishnan P; Kumar DK; Poosapadi Arjunan S; Kumar H; Mitchell P; Kawasaki R
    Comput Math Methods Med; 2016; 2016():3016245. PubMed ID: 27594895
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Recalibration and validation of the SCORE risk chart in the Australian population: the AusSCORE chart.
    Chen L; Tonkin AM; Moon L; Mitchell P; Dobson A; Giles G; Hobbs M; Phillips PJ; Shaw JE; Simmons D; Simons LA; Fitzgerald AP; De Backer G; De Bacquer D
    Eur J Cardiovasc Prev Rehabil; 2009 Oct; 16(5):562-70. PubMed ID: 19741542
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Cardiovascular risk assessment using ASCVD risk score in fibromyalgia: a single-centre, retrospective study using "traditional" case control methodology and "novel" machine learning.
    Surendran S; Mithun CB; Moni M; Tiwari A; Pradeep M
    Adv Rheumatol; 2021 Nov; 61(1):72. PubMed ID: 34838137
    [TBL] [Abstract][Full Text] [Related]  

  • 19.
    ; ; . PubMed ID:
    [No Abstract]   [Full Text] [Related]  

  • 20.
    ; ; . PubMed ID:
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 10.