title: Selecting the best prediction model for readmission.
abstract: OBJECTIVE
  This study aims to determine the risk factors predicting rehospitalization by comparing three models and selecting the most successful model.
  
  
  METHODS
  In order to predict the risk of rehospitalization within 28 days after discharge, 11 951 inpatients were recruited into this study between January and December 2009. Predictive models were constructed with three methods, logistic regression analysis, a decision tree, and a neural network, and the models were compared and evaluated in light of their misclassification rate, root asymptotic standard error, lift chart, and receiver operating characteristic curve.
  
  
  RESULTS
  The decision tree was selected as the final model. The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine, 10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater.
  
  
  CONCLUSIONS
  When a patient is to be discharged within 2 days, the appropriateness of discharge should be considered, with special concern of undiscovered complications and co-morbidities. In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured. Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.
authors.affiliation: College of Pharmacy, Gachon University, Incheon, Korea. ewlee@gachon.ac.kr
authors.forename: Eun Whan
authors.initials: EW
authors.lastname: Lee
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D003663:Decision Trees; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007902:Length of Stay; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010343:Patient Admission / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D012307:Risk Factors; D055815:Young Adult