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Bayesian networks: a new method for the modeling of bibliographic knowledge

Application to fall risk assessment in geriatric patients

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Abstract

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.

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Acknowledgments

LL conducted the literature search, designed the network, performed the statistical analyses, and wrote the manuscript. LB helped with the analyses and the writing. CC supervised the data collection. MD had the idea for the study, supervised the study, co-supervised the analyses, and helped for the writing. All authors read and approved the final version of the manuscript.

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  1. Hospices Civils de Lyon, Groupement Hospitalier de Gériatrie, Service Pharmacie 40 Avenue de La Table de Pierre, 69340, Francheville, France

    Laure Lalande, Laurent Bourguignon, Chloé Carlier & Michel Ducher

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  1. Laure Lalande
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  2. Laurent Bourguignon
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  3. Chloé Carlier
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  4. Michel Ducher
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Correspondence to Michel Ducher.

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Lalande, L., Bourguignon, L., Carlier, C. et al. Bayesian networks: a new method for the modeling of bibliographic knowledge. Med Biol Eng Comput 51, 657–664 (2013). https://doi.org/10.1007/s11517-013-1035-8

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  • DOI: https://doi.org/10.1007/s11517-013-1035-8

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