TY  - JOUR
PY  - 2013//
TI  - Bayesian networks: a new method for the modeling of bibliographic knowledge : Application to fall risk assessment in geriatric patients
JO  - Medical and biological engineering and computing
A1  - Lalande, Laure
A1  - Bourguignon, Laurent
A1  - Carlier, Chloé
A1  - Ducher, Michel
SP  - 657
EP  - 664
VL  - 51
IS  - 6
N2  - Falls in geriatry are associated with important morbidity, mortality and high healthcare costs. Because of the large number of variables related to the risk of falling, determining patients at risk is a difficult challenge. The aim of this work was to validate a tool to detect patients with high risk of fall using only bibliographic knowledge. Thirty articles corresponding to 160 studies were used to modelize fall risk. A retrospective case-control cohort including 288 patients (88 ± 7 years) and a prospective cohort including 106 patients (89 ± 6 years) from two geriatric hospitals were used to validate the performances of our model. We identified 26 variables associated with an increased risk of fall. These variables were split into illnesses, medications, and environment. The combination of the three associated scores gives a global fall score. The sensitivity and the specificity were 31.4, 81.6, 38.5, and 90 %, respectively, for the retrospective and the prospective cohort. The performances of the model are similar to results observed with already existing prediction tools using model adjustment to data from numerous cohort studies. This work demonstrates that knowledge from the literature can be synthesized with Bayesian networks.<p />  <p>Language: en</p>
LA  - en
SN  - 0140-0118
UR  - http://dx.doi.org/10.1007/s11517-013-1035-8
ID  - ref1
ER  -