BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

109 related articles for article (PubMed ID: 17238386)

  • 1. Impact of different training strategies on the accuracy of a Bayesian network for predicting hospital admission.
    Leegon J; Aronsky D
    AMIA Annu Symp Proc; 2006; 2006():474-8. PubMed ID: 17238386
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Comparing decision support methodologies for identifying asthma exacerbations.
    Dexheimer JW; Brown LE; Leegon J; Aronsky D
    Stud Health Technol Inform; 2007; 129(Pt 2):880-4. PubMed ID: 17911842
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting hospital admission for Emergency Department patients using a Bayesian network.
    Leegon J; Jones I; Lanaghan K; Aronsky D
    AMIA Annu Symp Proc; 2005; 2005():1022. PubMed ID: 16779309
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Predicting hospital admission at triage in an emergency department.
    Dexheimer JW; Leegon J; Aronsky D
    AMIA Annu Symp Proc; 2007 Oct; ():937. PubMed ID: 18694037
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Predicting emergency department inpatient admissions to improve same-day patient flow.
    Peck JS; Benneyan JC; Nightingale DJ; Gaehde SA
    Acad Emerg Med; 2012 Sep; 19(9):E1045-54. PubMed ID: 22978731
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Predicting hospital admission in a pediatric Emergency Department using an Artificial Neural Network.
    Leegon J; Jones I; Lanaghan K; Aronsky D
    AMIA Annu Symp Proc; 2006; 2006():1004. PubMed ID: 17238623
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service.
    Acid S; de Campos LM; Fernández-Luna JM; Rodríguez S; María Rodríguez J; Luis Salcedo J
    Artif Intell Med; 2004 Mar; 30(3):215-32. PubMed ID: 15081073
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A decision support system to facilitate management of patients with acute gastrointestinal bleeding.
    Chu A; Ahn H; Halwan B; Kalmin B; Artifon EL; Barkun A; Lagoudakis MG; Kumar A
    Artif Intell Med; 2008 Mar; 42(3):247-59. PubMed ID: 18063351
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Automatic identification of patients eligible for a pneumonia guideline: comparing the diagnostic accuracy of two decision support models.
    Lagor C; Aronsky D; Fiszman M; Haug PJ
    Stud Health Technol Inform; 2001; 84(Pt 1):493-7. PubMed ID: 11604789
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Diagnosing community-acquired pneumonia with a Bayesian network.
    Aronsky D; Haug PJ
    Proc AMIA Symp; 1998; ():632-6. PubMed ID: 9929296
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Artificial intelligence techniques: predicting necessity for biopsy in renal transplant recipients suspected of acute cellular rejection or nephrotoxicity.
    Hummel AD; Maciel RF; Sousa FS; Cohrs FM; Falcão AE; Teixeira F; Baptista R; Mancini F; da Costa TM; Alves D; Rodrigues RG; Miranda R; Pisa IT
    Transplant Proc; 2011 May; 43(4):1343-4. PubMed ID: 21620125
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Combining decision support methodologies to diagnose pneumonia.
    Aronsky D; Fiszman M; Chapman WW; Haug PJ
    Proc AMIA Symp; 2001; ():12-6. PubMed ID: 11825148
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A comparison of methods for assessing penetrating trauma on retrospective multi-center data.
    Ahmed BA; Matheny ME; Rice PL; Clarke JR; Ogunyemi OI
    J Biomed Inform; 2009 Apr; 42(2):308-16. PubMed ID: 18929685
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Evaluation of neural network performance by receiver operating characteristic (ROC) analysis: examples from the biotechnology domain.
    Meistrell ML
    Comput Methods Programs Biomed; 1990 May; 32(1):73-80. PubMed ID: 2401136
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Learning Bayesian networks for clinical time series analysis.
    van der Heijden M; Velikova M; Lucas PJ
    J Biomed Inform; 2014 Apr; 48():94-105. PubMed ID: 24361389
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Spatiotemporal Bayesian networks for malaria prediction.
    Haddawy P; Hasan AHMI; Kasantikul R; Lawpoolsri S; Sa-Angchai P; Kaewkungwal J; Singhasivanon P
    Artif Intell Med; 2018 Jan; 84():127-138. PubMed ID: 29241658
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A decision aid for diagnosis of liver lesions on MRI.
    Tombropoulos R; Shiffman S; Davidson C
    Proc Annu Symp Comput Appl Med Care; 1993; ():439-43. PubMed ID: 8130512
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Detecting asthma exacerbations in a pediatric emergency department using a Bayesian network.
    Sanders DL; Aronsky D
    AMIA Annu Symp Proc; 2006; 2006():684-8. PubMed ID: 17238428
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting hospital admissions at emergency department triage using routine administrative data.
    Sun Y; Heng BH; Tay SY; Seow E
    Acad Emerg Med; 2011 Aug; 18(8):844-50. PubMed ID: 21843220
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A comparison of classification algorithms to automatically identify chest X-ray reports that support pneumonia.
    Chapman WW; Fizman M; Chapman BE; Haug PJ
    J Biomed Inform; 2001 Feb; 34(1):4-14. PubMed ID: 11376542
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.