title: Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination.
abstract: OBJECTIVE
  To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording, a multi-step research protocol and predictive modelling.
  
  
  BACKGROUND
  Factors known to affect responses to influenza vaccination include older age, past exposure to influenza viruses, and chronic diseases; however, constructing useful prediction models remains a challenge, because of the need to identify health parameters that are appropriate for general use in modelling patients' responses.
  
  
  METHODS
  The sample consisted of 93 patients aged 50-89 years (median 69), with multiple medical conditions, who were vaccinated against influenza. Literature searches identified potentially predictive health-related parameters, including age, gender, diagnoses of the main chronic ageing diseases, anthropometric measures, and haematological and biochemical tests. By applying data mining algorithms, patterns were identified in the data set. Candidate health parameters, selected in this way, were then combined with information on past influenza virus exposure to build the prediction model using logistic regression.
  
  
  RESULTS
  A highly significant prediction model was obtained, indicating that by using a systems biology approach it is possible to answer unresolved complex medical uncertainties. Adopting this systems biology approach can be expected to be useful in identifying the most appropriate target groups for other preventive programmes.
authors.affiliation: Department of Family Medicine, Medical School Osijek, University of Osijek, Osijek, Croatia. ljiljana.majnaric@hi.t-com.hr
authors.forename: Ljiljana
authors.initials: L
authors.lastname: Majnarić-Trtica
authors.forename: Branko
authors.initials: B
authors.lastname: Vitale
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003210:Concept Formation; D016001:Confidence Intervals; D005194:Family Practice / Q000379:methods*; D005260:Female; D006302:Health Services Research / Q000379:methods*; D006801:Humans; D007252:Influenza Vaccines; D007251:Influenza, Human / Q000517:prevention & control*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D017060:Patient Satisfaction; D011315:Preventive Medicine / Q000379:methods; D049490:Systems Biology / Q000379:methods*; D016896:Treatment Outcome
chemical_list: D007252:Influenza Vaccines