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 *

980 related articles for article (PubMed ID: 26771782)

  • 1. Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.
    Churpek MM; Yuen TC; Winslow C; Meltzer DO; Kattan MW; Edelson DP
    Crit Care Med; 2016 Feb; 44(2):368-74. PubMed ID: 26771782
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Derivation of a cardiac arrest prediction model using ward vital signs*.
    Churpek MM; Yuen TC; Park SY; Meltzer DO; Hall JB; Edelson DP
    Crit Care Med; 2012 Jul; 40(7):2102-8. PubMed ID: 22584764
    [TBL] [Abstract][Full Text] [Related]  

  • 3. The value of vital sign trends for detecting clinical deterioration on the wards.
    Churpek MM; Adhikari R; Edelson DP
    Resuscitation; 2016 May; 102():1-5. PubMed ID: 26898412
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Multicenter development and validation of a risk stratification tool for ward patients.
    Churpek MM; Yuen TC; Winslow C; Robicsek AA; Meltzer DO; Gibbons RD; Edelson DP
    Am J Respir Crit Care Med; 2014 Sep; 190(6):649-55. PubMed ID: 25089847
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data.
    Rojas JC; Carey KA; Edelson DP; Venable LR; Howell MD; Churpek MM
    Ann Am Thorac Soc; 2018 Jul; 15(7):846-853. PubMed ID: 29787309
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Differences in vital signs between elderly and nonelderly patients prior to ward cardiac arrest.
    Churpek MM; Yuen TC; Winslow C; Hall J; Edelson DP
    Crit Care Med; 2015 Apr; 43(4):816-22. PubMed ID: 25559439
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Less is more: Detecting clinical deterioration in the hospital with machine learning using only age, heart rate, and respiratory rate.
    Akel MA; Carey KA; Winslow CJ; Churpek MM; Edelson DP
    Resuscitation; 2021 Nov; 168():6-10. PubMed ID: 34437996
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Comparison of early warning scores for predicting clinical deterioration and infection in obstetric patients.
    Arnolds DE; Carey KA; Braginsky L; Holt R; Edelson DP; Scavone BM; Churpek M
    BMC Pregnancy Childbirth; 2022 Apr; 22(1):295. PubMed ID: 35387624
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model.
    Hu SB; Wong DJ; Correa A; Li N; Deng JC
    PLoS One; 2016; 11(8):e0161401. PubMed ID: 27532679
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Using electronic health record data to develop and validate a prediction model for adverse outcomes in the wards*.
    Churpek MM; Yuen TC; Park SY; Gibbons R; Edelson DP
    Crit Care Med; 2014 Apr; 42(4):841-8. PubMed ID: 24247472
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting clinical deterioration in the hospital: the impact of outcome selection.
    Churpek MM; Yuen TC; Edelson DP
    Resuscitation; 2013 May; 84(5):564-8. PubMed ID: 23022075
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Validation of Early Warning Scores at Two Long-Term Acute Care Hospitals.
    Churpek MM; Carey KA; Dela Merced N; Prister J; Brofman J; Edelson DP
    Crit Care Med; 2019 Dec; 47(12):e962-e965. PubMed ID: 31567342
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest.
    Mayampurath A; Hagopian R; Venable L; Carey K; Edelson D; Churpek M;
    Crit Care Med; 2022 Feb; 50(2):e162-e172. PubMed ID: 34406171
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Using Time Series Analysis to Predict Cardiac Arrest in a PICU.
    Kennedy CE; Aoki N; Mariscalco M; Turley JP
    Pediatr Crit Care Med; 2015 Nov; 16(9):e332-9. PubMed ID: 26536566
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Real-Time Risk Prediction on the Wards: A Feasibility Study.
    Kang MA; Churpek MM; Zadravecz FJ; Adhikari R; Twu NM; Edelson DP
    Crit Care Med; 2016 Aug; 44(8):1468-73. PubMed ID: 27075140
    [TBL] [Abstract][Full Text] [Related]  

  • 16. An intelligent warning model for early prediction of cardiac arrest in sepsis patients.
    Layeghian Javan S; Sepehri MM; Layeghian Javan M; Khatibi T
    Comput Methods Programs Biomed; 2019 Sep; 178():47-58. PubMed ID: 31416562
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. Laboratory-derived early warning score for the prediction of in-hospital mortality, intensive care unit admission, medical emergency team activation and cardiac arrest in general medical wards.
    Ratnayake H; Johnson D; Martensson J; Lam Q; Bellomo R
    Intern Med J; 2021 May; 51(5):746-751. PubMed ID: 31424605
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU.
    Mayampurath A; Sanchez-Pinto LN; Hegermiller E; Erondu A; Carey K; Jani P; Gibbons R; Edelson D; Churpek MM
    Pediatr Crit Care Med; 2022 Jul; 23(7):514-523. PubMed ID: 35446816
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 49.