Prognostic methods in medicine: Editorial

Steen Andreassen, Christian Riekehr, Brian Kristensen, Henrik C. Schønheyder, Leonard Leibovici

Research output: Contribution to journalEditorial

Abstract

Prognosis-the prediction of the course and outcome of disease processes- plays an important role in patient management tasks like diagnosis and treatment planning. As a result, prognostic models form an integral part of a number of systems supporting these tasks. Furthermore, prognostic models constitute instruments to evaluate the quality of health care and the consequences of health care policies by comparing predictions according to care norms with actual results. Approaches to developing prognostic models vary from using traditional probabilistic techniques, originating from the field of statistics, to more qualitative and model-based techniques, originating from the field of artificial intelligence (AI). In this paper, various approaches to constructing prognostic models, with emphasis on methods from the field of AI, are described and compared.

Original languageEnglish
Pages (from-to)105-119
Number of pages15
JournalArtificial Intelligence in Medicine
Volume15
Issue number2
DOIs
StatePublished - Feb 1999
Externally publishedYes

Keywords

  • Machine learning
  • Medical AI
  • Medical decision making
  • Medical prognostic models

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