TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Marschollek, Michael
A1  - Gövercin, Mehmet
A1  - Rust, Stefan
A1  - Gietzelt, Matthias
A1  - Schulze, Mareike
A1  - Wolf, Klaus-Hendrik
A1  - Steinhagen-Thiessen, Elisabeth
T1  - Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups
JF  - BMC Medical Informatics and Decision Making
N2  - 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.
KW  - accidental falls
KW  - geriatric assessment
KW  - data mining
Y1  - 2012
UN  - https://nbn-resolving.org/urn:nbn:de:1111-201607214692
U6  - https://doi.org/10.25968/opus-1128
DO  - https://doi.org/10.25968/opus-1128
VL  - 2012
IS  - 12:19
ER  -