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Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

  • 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.

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Metadaten
Author:Michael Marschollek, Mehmet Gövercin, Stefan Rust, Matthias Gietzelt, Mareike SchulzeGND, Klaus-Hendrik Wolf, Elisabeth Steinhagen-Thiessen
URN:urn:nbn:de:1111-201607214692
DOI:https://doi.org/10.1186/1472-6947-12-19
Parent Title (English):BMC Medical Informatics and Decision Making
Document Type:Article
Language:English
Year of Completion:2012
Release Date:2017/07/26
Tag:accidental falls; data mining; geriatric assessment
Volume:2012
Issue:12:19
Institutes:Fakultät III - Medien, Information und Design
DDC classes:610 Medizin, Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung 2.0