Evaluation of a dynamic bayesian belief network to predict osteoarthritic knee pain using data from the osteoarthritis initiative.
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.
Watt
Emily W
EW
University of California Los Angeles, Los Angeles, CA, USA.
Watt
Emily
E
Bui
Alex A T
AA
Bui
Alex At
AA
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