Work

Evaluation of a dynamic bayesian belief network to predict osteoarthritic knee pain using data from the osteoarthritis initiative.

Year: 2008

Type: article

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.

Source: PubMed Central

Authors: Emily W Watt, Emily Watt, Alex A T Bui, Alex At Bui

Institution: University of California, Los Angeles

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institute of Biomedical Imaging and Bioengineering, U.S. National Library of Medicine

Awards: Medical Imaging Informatics Training Program, Toward Individually-tailored Medicine: Probabilistic Models of Cerebral Aneurysms