ArticleApplied bionics and biomechanics2017
Using a Bayesian Network to Predict L5/S1 Spinal Compression Force from Posture, Hand Load, Anthropometry, and Disc Injury Status.
Article in Applied bionics and biomechanics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- HIV testing factors among Men Who Have Sex with Men in China: an analysis based on Lasso regression and Bayesian networks.BMC public health · 2026Article
- Associations Between Personality Traits, Self-Efficacy and Complementary Feeding Behavior Among Infant Caregivers in Western Rural China.Maternal & child nutrition · 2025Article
- Article
- Factors Related to HPV Vaccination Intention among MSM in China: A Bayesian Network Model.International journal of environmental research and public health · 2022Article
- Exploring influencing factors of chronic obstructive pulmonary disease based on elastic net and Bayesian network.Scientific reports · 2022Article
- Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: a cross-sectional study.BMC public health · 2021Article
- Identification of Factors Influencing Out-of-county Hospitalizations in the New Cooperative Medical Scheme.Current medical science · 2019Article
- Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors.Scientific reports · 2018Article
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Authors and funding
1 author.
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Abstract
Stochastic biomechanical modeling has become a useful tool most commonly implemented using Monte Carlo simulation, advanced mean value theorem, or Markov chain modeling. Bayesian networks are a novel method for probabilistic modeling in artificial intelligence, risk modeling, and machine learning. The purpose of this study was to evaluate the suitability of Bayesian networks for biomechanical modeling using a static biomechanical model of spinal forces during lifting. A 20-node Bayesian network model was used to implement a well-established static two-dimensional biomechanical model for predicting L5/S1 compression and shear forces. The model was also implemented as a Monte Carlo simulation in MATLAB. Mean L5/S1 spinal compression force estimates differed by 0.8%, and shear force estimates were the same. The model was extended to incorporate evidence about disc injury, which can modify the prior probability estimates to provide posterior probability estimates of spinal compression force. An example showed that changing disc injury status from false to true increased the estimate of mean L5/S1 compression force by 14.7%. This work shows that Bayesian networks can be used to implement a whole-body biomechanical model used in occupational biomechanics and incorporate disc injury.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.