title: Evaluation of a dynamic bayesian belief network to predict osteoarthritic knee pain using data from the osteoarthritis initiative.
abstract: The most common cause of disability in older adults in the United States is osteoarthritis. To address the problem of early disease prediction, we have constructed a Bayesian belief network (BBN) composed of knee OA-related symptoms to support prognostic queries. The purpose of this study is to evaluate a static and dynamic BBN--based on the NIH Osteoarthritis Initiative (OAI) data--in predicting the likelihood of a patient being diagnosed with knee OA. Initial validation results are promising: our model outperforms a logistic regression model in several designed studies. We can conclude that our model can effectively predict the symptoms that are commonly associated with the presence of knee OA.
authors.affiliation: University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Emily W
authors.initials: EW
authors.lastname: Watt
authors.forename: Emily
authors.initials: E
authors.lastname: Watt
authors.forename: Alex A T
authors.initials: AA
authors.lastname: Bui
authors.forename: Alex At
authors.initials: AA
authors.lastname: Bui
mesh_terms: D000465:Algorithms; D018771:Arthralgia / Q000175:diagnosis* / Q000209:etiology*; D001185:Artificial Intelligence; D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D009323:Natural Language Processing; D020370:Osteoarthritis, Knee / Q000175:diagnosis* / Q000209:etiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity