BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

123 related articles for article (PubMed ID: 16779309)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. Predicting ambulance diversion in an adult Emergency Department using a Gaussian process.
    Leegon J; Hoot N; Aronsky D; Storkey A
    AMIA Annu Symp Proc; 2007 Oct; ():1026. PubMed ID: 18694124
    [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. Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.
    Zhang X; Kim J; Patzer RE; Pitts SR; Patzer A; Schrager JD
    Methods Inf Med; 2017 Oct; 56(5):377-389. PubMed ID: 28816338
    [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. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. Evaluation of a hospital admission prediction model adding coded chief complaint data using neural network methodology.
    Handly N; Thompson DA; Li J; Chuirazzi DM; Venkat A
    Eur J Emerg Med; 2015 Apr; 22(2):87-91. PubMed ID: 24509606
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making.
    Rendell K; Koprinska I; Kyme A; Ebker-White AA; Dinh MM
    Emerg Med Australas; 2019 Jun; 31(3):429-435. PubMed ID: 30469164
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Prospective evaluation of a Bayesian Network for detecting asthma exacerbations in a Pediatric Emergency Department.
    Sanders DL; Aronsky D
    AMIA Annu Symp Proc; 2006; 2006():1085. PubMed ID: 17238704
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Decreasing lab turnaround time improves emergency department throughput and decreases emergency medical services diversion: a simulation model.
    Storrow AB; Zhou C; Gaddis G; Han JH; Miller K; Klubert D; Laidig A; Aronsky D
    Acad Emerg Med; 2008 Nov; 15(11):1130-5. PubMed ID: 18638034
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Estimating patient's length of stay in the Emergency Department with an artificial neural network.
    Wrenn J; Jones I; Lanaghan K; Congdon CB; Aronsky D
    AMIA Annu Symp Proc; 2005; 2005():1155. PubMed ID: 16779441
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Predictive variables of an emergency department quality and performance indicator: a 1-year prospective, observational, cohort study evaluating hospital and emergency census variables and emergency department time interval measurements.
    Casalino E; Choquet C; Bernard J; Debit A; Doumenc B; Berthoumieu A; Wargon M
    Emerg Med J; 2013 Aug; 30(8):638-45. PubMed ID: 22906702
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Access block causes emergency department overcrowding and ambulance diversion in Perth, Western Australia.
    Fatovich DM; Nagree Y; Sprivulis P
    Emerg Med J; 2005 May; 22(5):351-4. PubMed ID: 15843704
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting prolonged length of hospital stay in older emergency department users: use of a novel analysis method, the Artificial Neural Network.
    Launay CP; Rivière H; Kabeshova A; Beauchet O
    Eur J Intern Med; 2015 Sep; 26(7):478-82. PubMed ID: 26142183
    [TBL] [Abstract][Full Text] [Related]  

  • 20. The effect of emergency department expansion on emergency department overcrowding.
    Han JH; Zhou C; France DJ; Zhong S; Jones I; Storrow AB; Aronsky D
    Acad Emerg Med; 2007 Apr; 14(4):338-43. PubMed ID: 17400996
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.