title: [Modeling asthma evolution by a multi-state model].
abstract: BACKGROUND
  There are many scores for the evaluation of asthma. However, most do not take into account the evolutionary aspects of this illness. We propose a model for the clinical course of asthma by a homogeneous Markov model process based on data provided by the A.R.I.A. (Association de Recherche en Intelligence Artificielle dans le cadre de l'asthme et des maladies respiratoires).
  
  
  METHODS
  The criterion used is the activity of the illness during the month before consultation. The activity is divided into three levels: light (state 1), mild (state 2) and severe (state 3). The model allows the evaluation of the strength of transition between states.
  
  
  RESULTS
  We found that strong intensities were implicated towards state 2 (lambda(12) and lambda(32)), less towards state 1 (lambda(21) and lambda(31)), and minimum towards state 3 (lambda(23)). This results in an equilibrium distribution essentially divided between state 1 and 2 (44.6% and 51.0% respectively) with a small proportion in state 3 (4.4%).
  
  
  CONCLUSIONS
  In the future, the increasing amount of available data should permit the introduction of covariables, the distinction of subgroups and the implementation of clinical studies. The interest of this model falls within the domain of the quantification of the illness as well as the representation allowed thereof, while offering a formal framework for the clinical notion of time and evolution.
authors.affiliation: Département d'Information Médicale, C.H. de Gap, 1 Place Muret, 05007 Gap Cedex.
authors.forename: T
authors.initials: T
authors.lastname: Boudemaghe
authors.forename: J P
authors.initials: JP
authors.lastname: Daurès
mesh_terms: D001185:Artificial Intelligence; D001249:Asthma / Q000503:physiopathology*; D019992:Databases as Topic; D018450:Disease Progression; D005500:Follow-Up Studies; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012720:Severity of Illness Index