Gepubliceerd als abstract

  1. Arterial carbon dioxide levels predict in-hospital mortality independent of arterial oxygen after resuscitation from cardiac arrest
    H.J. Helmerhorst, M.-J. Blom, D.J. van Westerloo, A. Abu-Hanna, N.F. de Keizer, E. de Jonge
    Intensive Care Med. 2014 Sep;40 Suppl 1: S236 #879
  2. A modified real AdaBoost algorithm to discover intensive care unit subgroups with a poor outcome.
    Koetsier A, de Keizer NF, Abu-Hanna A, Peek N
    AMIA Annu Symp Proc. 2013 Nov 16;2013:798-803
  3. Intoxicated Intensive Care Unit patients: Long-term mortality?
    Brandenburg R, Brinkman S, de Keizer NF, Meulenbelt J, de Lange DW.
    Congress of the European Association of Poisons Centres and Clinical Toxicologists (EAPCCT), Brussels. Clinical Toxicology (2014), 52, 299-300
  4. Length of stay of Intensive Care Unit patients: effects on outcome.
    C.M.A. Bles, L. Peelen, J.G. van der Hoeven, P.Picckers.
    Nederlandse Intensivisten Dagen 2008, abstract # 42. Ede, February 2008.
  5. The protective effect of obesity on ICU mortality is mediated by an altered immune response.
    Weerheijm, D., Dusseljee, J., De Keizer, N., De Jonge, E., Van der Hoeve, H., and Pickkers, P.
    Netherlands Journal of Critical Care, abstract # 47, December 2007.
  6. Stratified analysis of multivariate state changes in critically ill patients.
    L. Peelen, N. Peek, N.F. de Keizer, R.J.Bosman.
    Proceedings BENELEARN conference 2007 (pp 61-68), Amsterdam, The Netherlands, May 2007.
  7. A high FiO2 and a high PaO2 are risk factors for in-hospital mortality in ICU-patients, independent of severity of pulmonary dysfunction and severity of illness.
    P. Keijzers, L. Peelen, R.J. Bosman, N.F. de Keizer, E. de Jonge.
    Nederlandse Intensivisten Dagen 2007, abstract #33. Ede, January 2007.
  8. Description Logics with approximate definitions. Precise modeling of vague concepts.
    S. Schlobach, M. Klein, L. Peelen.
    Proceedings International Joint Conferences on Artificial Intelligence (IJCAI) 2007 (pp 557-562), Hyderabad, India, January 2007.
  9. Describing scenarios for disease episodes and estimating their probability: a new approach with an application in Intensive Care.
    L. Peelen, N. Peek, R.J. Bosman.
    Working notes of the workshop on Intelligent Data Analysis in BioMedicine and Pharmacology (IDAMAP-06). Verona, August 2006, pp77-82.
  10. Modeling changes in organ failure for patients at the Intensive Care Unit.
    L. Peelen, N. Peek, N.F. de Keizer, E. de Jonge, G.J. Scheffer.
    Proceedings of the Medical Informatics Europe (MIE) conference, 2006. Stud Health Technol Inform. 2006;124:555-60. PMID: 17108576
  11. Instance-based prognosis in Intensive Care using severity-of-illness scores.
    L. Peelen, C.H.K. Tan, N. Peek.
    Annual Conference of the European Society on Computing and Technology in Anaesthesia and Intensive Care (ESCTAIC), Aalborg, Denmark, September 2005
  12. Investigating the effect of differences in entry criteria on trial populations: a method with an application in severe sepsis.
    L. Peelen, N. Peek, N.F. de Keizer, E. de Jonge.
    Proceedings of the Medical Informatics Europe (MIE) conference 2005. Geneva, August 2005. Stud Health Technol Inform. 2005; 116:1004-1009. PMID: 16160390
  13. Instance-based prognosis in Intensive Care using severity-of-illness scores.
    C. Tan, L. Peelen, N. Peek.
    Working notes of the workshop on Intelligent Data Analysis in BioMedicine and Pharmacology (IDAMAP-05). Aberdeen, July 2005, pp21-26.
  14. Body Mass Index (BMI) and hospital mortality.
    R.J. Bosman, L.M. Peelen, E. de Jonge.
    Intensive Care Medicine, 2004; 30 (Suppl) p S9 #20.
  15. Comparing entry criteria in severe sepsis trials: a Bayesian approach.
    L. Peelen and N. Peek.
    Proceedings of the 25th Annual Congress of the International Society of Clinical Biostatistics, Leiden, August 2004, p183.
  16. Statistical methods to compare different definitions of disease with an application to severe sepsis.
    L. Peelen, N. Peek, A. Zwinderman.
    Working notes of the joint workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-03) and Knowledge-Based Information Management in Anaesthesia and Intensive Care (KBM-AIC), Cyprus October 2003, pp13-16.
  17. Different definitions for severe sepsis yield significantly different study populations.
    L.M. Peelen, N.F. de Keizer, N.Peek, E. de Jonge, R.J. Bosman, G.J. Scheffer.
    Intensive Care Medicine, 2003; 29(Suppl) p74, # 272.
  18. Intensive Care Information Systems improve quality of physician documantation.
    Bosman, R. J., Joore, J. C., De Jonge, E., and de Keizer, N.
    Intensive Care Med. 29(supp1), S39. 2003. Ref Type: Abstract
  19. Different definitions in sepsis trials yield significantly different study populations.
    Peelen, L. M., de Keizer, N., Peek, N. B., De Jonge, E., Bosman, R. J., and Scheffer, G. J.
    Intensive Care Med. 29(supp1), S74. 2003. Ref Type: Abstract
  20. Differences in efficacy between hospitals in treating pneumonia.
    Bosman, R. J., Joore, J. C., De Jonge, E., and de Keizer, N.
    Intensive Care Med. 29(supp1), S39. 2003. Ref Type: Abstract
  21. Differences in hospital mortality paterns depending on weekday of ICU discharge.
    Bosman, R. J. and de Keizer, N.
    Intensive Care Med. 26(supp3), S237. 2000. Ref Type: Abstract
  22. Training improves quality of prognostic scoring data.
    Arts, D., Bosman, R. J., de Keizer, N., and De Jonge, E.
    Intensive Care Med. 26(supp3), S260. 2000. Ref Type: Abstract
  23. APACHE II and SAPS II in cardiosurgical patients.
    Bosman, R. J. and De Keizer, N. F.
    Intensive Care Med. 25(supp), S13. 1999. Ref Type: Abstract