title: Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups.
abstract: BACKGROUND
  Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2).
  
  
  METHODS
  A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances.
  
  
  RESULTS
  The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity.
  
  
  CONCLUSIONS
  Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.
authors.affiliation: Peter L, Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hanover Medical School, Carl-Neuberg-Str, 1, 30625 Hanover, Germany. Michael.Marschollek@plri.de
authors.forename: Michael
authors.initials: M
authors.lastname: Marschollek
authors.forename: Mehmet
authors.initials: M
authors.lastname: Gövercin
authors.forename: Stefan
authors.initials: S
authors.lastname: Rust
authors.forename: Matthias
authors.initials: M
authors.lastname: Gietzelt
authors.forename: Mareike
authors.initials: M
authors.lastname: Schulze
authors.forename: Klaus-Hendrik
authors.initials: KH
authors.lastname: Wolf
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Steinhagen-Thiessen
mesh_terms: D000058:Accidental Falls / Q000706:statistics & numerical data*; D000368:Aged; D000369:Aged, 80 and over; D057225:Data Mining; D003663:Decision Trees; D017050:Episode of Care; D015577:Geriatric Assessment; D006760:Hospitalization / Q000706:statistics & numerical data / Q000639:trends; D006801:Humans; D007297:Inpatients / Q000145:classification*; D016015:Logistic Models; D010343:Patient Admission; D011237:Predictive Value of Tests; D018570:Risk Assessment / Q000379:methods*; D035862:Vulnerable Populations