Download PDF
Methods Inf Med 2001; 40(01): 1-5
DOI: 10.1055/s-0038-1634456
Editorial
Schattauer GmbH

Prognostic Models in Medicine

AI and Statistical Approaches
A. Abu-Hanna
1   Dept. of Medical Informatics, Academic Medical Center-University of Amsterdam, The Netherlands
,
P. J. F. Lucas
2   Dept. of Computing Science, University of Aberdeen, Scotland, UK
› Author Affiliations
Further Information

Publication History

Publication Date:
08 February 2018 (online)

    Permissions and Reprints

 

 

  • References

  • 1 Abu-Hanna A, Lucas PJF. Intelligent Prognostic Methods in Medical Diagnosis and Treatment Planning. In: Borne P, Iksouri el Kamel A. eds. Proceedings of Computational Engineering in Systems Applications (IMACS-IEEE UCIS Lille). 1998: 312-7.
    Search in Google Scholar
  • 2 Abu-Hanna A, Lucas PJF. eds. Prognostic Models in Medicine: AI and decision analysis approaches. Working Notes of the workshop on Prognostic Models in Medicine. Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making (AIMDM’99) Aalborg, Denmark.:
  • 3 Altman DG, Royston P. What do we mean by validating a prognostic model?. Stat Med 2000; 19: 453-73.
    CrossrefPubMedSearch in Google Scholar
  • 4 Aitchison TC, Sirel JM, Watt DC, MacKie RM. Prognostic trees to aid prognosis in patients with cutaneous malignant melanoma. BMJ 1995; 311: 1536-9.
    CrossrefPubMedSearch in Google Scholar
  • 5 Asch D, Patton J, Hershey J. Knowing for the sake of knowing: the value of prognostic information. Med Decis Making 1990; 10: 47-57.
    CrossrefPubMedSearch in Google Scholar
  • 6 Bishop CM. Neural Networks for Pattern Recognition. Oxford: Oxford University Press; 1995
    Search in Google Scholar
  • 7 Chang RWS. Individual outcome prediction models for the intensive care units. Lancet 1989; 1: 143-6.
    PubMedSearch in Google Scholar
  • 8 Dietterich TG. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Comput 1998; 10 (Suppl. 07) 1895-924.
    CrossrefPubMedSearch in Google Scholar
  • 9 De Dombal FT, Clamp SE, Softley A, Unwin BJ, Staniland JR. Prediction of Individual Patient Prognosis: Value of Computer Aided Systems. Med Decis Making 1986; 6: 18-22.
    CrossrefPubMedSearch in Google Scholar
  • 10 Ebell MH, Kruse JA, Smith M, Novak J, Drader-Wilcox J. Failure of three decision rules to predict the outcome of in-hospital cardiopulmonary resuscitation. Med Decis Making 1997; 17: 171-7.
    CrossrefPubMedSearch in Google Scholar
  • 11 George AL, Folk BP, Crecelius PL, Campbell WB. Pre-arrest morbidity and other correlates of survival after in-hospital cardiopulmonary arrest. Am J Med 1989; 87: 28-34.
    CrossrefPubMedSearch in Google Scholar
  • 12 Hand DJ. Construction and assessment of classification rules. Chichester: John Wiley and Sons; 1997
    Search in Google Scholar
  • 13 Hand DJ. Datamining: Statistics and more?. Am Stat 1998; 52 (Suppl. 02) 112-8.
    PubMedSearch in Google Scholar
  • 14 Hilden J, Habbema JDF. Prognosis in medicine: an analysis of its meaning and role. Theor Med 1987; 8: 249-365.
    PubMedSearch in Google Scholar
  • 15 Le Gall JR, Lemeshow SL, Sauliner F. A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA 1993; 270 (Suppl. 24) 2957-63.
    CrossrefPubMedSearch in Google Scholar
  • 16 Lucas PJF, Boot H, Taal BG. Computer-based decision-support in the management of primary gastric non-Hodgkin lymphoma. Methods of Information in Medicine 1998; 37: 206-19.
    Thieme ConnectPubMedSearch in Google Scholar
  • 17 Lucas PJF, Abu-Hanna A. Prognostic methods in medicine. Artif Intell Med 1999; 15 (Suppl. 02) 105-19.
    CrossrefPubMedSearch in Google Scholar
  • 18 Lucas PJF, Van der Gaag LC. Principles of Expert Systems. Wokingham: Addison-Wesley; 1991
    Search in Google Scholar
  • 19 Mazumdar M. and J. Glassman, Tutorial in biostatistics, categorizing a prognostic variable: review of methods, code for easy implementation and applications to decision-making about cancer treatments. Stat Med 2000; 19: 113-32.
    CrossrefPubMedSearch in Google Scholar
  • 20 Neuberger J. Prognostic models — what is their future?. Eur J Gastroenterol Hepatol 1997; 9: 1145-7.
    PubMedSearch in Google Scholar
  • 21 Raiffa H. Decision Analysis: Introductory Lectures on Choices under Uncertainty. Reading, MA: Addison-Wesley; 1968
    Search in Google Scholar
  • 22 Salzberg SL. On comparing classifiers: a critique of current research and methods. Data Mining Knowl Discov 1999; 1: 1-12.
    PubMedSearch in Google Scholar
  • 23 Schwarzer G, Vach W, Schumacher M. On misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 2000; 19: 541-61.
    CrossrefPubMedSearch in Google Scholar
  • 24 Shortliffe EH. Computer programs to support clinical decision making. JAMA 1987; 258: 61-6.
    CrossrefPubMedSearch in Google Scholar
  • 25 Sox HC, Blatt MA, Higgins MC, Marton KI. Medical Decision Making. Boston: Butter-worths; 1988
    Search in Google Scholar
  • 26 Steyerberg WW, Keizer HJ, Fossa SD. et al. Prediction of residual retroperitonal mass histology following chemotherapy for meta-static nonseminomatous germ cell tumor: multivariate analysis of individual patient data from 6 study groups. J Clin Oncol 1995; 13: 1177-87.
    CrossrefPubMedSearch in Google Scholar
  • 27 Van der Gaag LC. Bayesian belief networks: odds and ends. Comput J 1996; 39: 79-113.
    PubMedSearch in Google Scholar
  • 28 Windeler J. Prognosis - what does the clinician associate with this notion?. Stat Med 2000; 19: 425-30.
    CrossrefPubMedSearch in Google Scholar
  • 29 Wyatt JC, Altman DGF. Commentary – prognostic models: clinically useful or quickly forgotten. BMJ 1995; 311: 1539-41.
    CrossrefPubMedSearch in Google Scholar
  • 30 Wyatt JC, Spiegelhalter DJ. Evaluating medical expert systems: what to test for and how?. Med Inform 1990; 15 (Suppl. 03) 205-17.
    PubMedSearch in Google Scholar
  • 31 Zhu BP, Lemeshow S, Hosmer DH, Klaar J, Avrunin J, Teres D. Factors affecting the performance of the models in the Mortality Probability Model II system and strategies of customization: a simulation study. Crit Care Med. 1996
    PubMedSearch in Google Scholar