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Open AccessArticle

Hybrid Bayesian Network Models of Spinal Injury and Slip/Fall Events

Departments of Orthopaedic Surgery, Biomedical Engineering, and Industrial & Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Appl. Sci. 2020, 10(14), 4834; https://doi.org/10.3390/app10144834
Received: 10 April 2020 / Accepted: 9 July 2020 / Published: 14 July 2020
(This article belongs to the Special Issue Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics)
Background: Biomechanists are often asked to provide expert opinions in legal proceedings, especially personal injury cases. This often involves using deterministic analysis methods, although the expert is expected to opine using a civil standard of “more likely than not” that is inherently probabilistic. Methods: A method is proposed for converting a class of deterministic biomechanical models into hybrid Bayesian networks that produce a probability well suited for addressing the civil standard of proof. The method was developed for spinal injury during lifting. Its generalizability was assessed by applying it to slip and fall events based on the coefficients of friction at the shoe–floor interface. Results: The proposed method is shown to be generalizable beyond lifting by applying it to a slip and fall event. Both the lifting and slip and fall models showed that incorporating evidence of injury could change the probabilities of critical quantities exceeding a threshold from “less likely than not” to “more likely than not.” Conclusions: The present work shows that it is possible to develop Bayesian networks for legal use based on laws of engineering mechanics and probabilistic descriptions of measurement error and human variability. View Full-Text
Keywords: biomechanics; Bayesian network; artificial intelligence; spine; slip and fall; litigation; tort biomechanics; Bayesian network; artificial intelligence; spine; slip and fall; litigation; tort
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Hughes, R. Hybrid Bayesian Network Models of Spinal Injury and Slip/Fall Events. Appl. Sci. 2020, 10, 4834.

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