Malignancy in dermatomyositis: A Bayesian Belief Network approach

Background: Dermatomyositis (DMS) is an inflammatory disease affecting the muscle and the skin and is known to be associated with a definite risk of malignancy. Extensive malignancy screening may not be cost-effective in all patients. Several predictive factors have been postulated in DMS.
Aims: This study attempts to build a Bayesian Belief Network model based on data available from literature to assign a numerical risk to each patient by taking the predictive factors into consideration.
Methods: Relevant frequency data was collected from literature reports of dermatomyositis cases for four independent factors: age over 40, male sex, elevated erythrocyte sedimentation rate (ESR) and cutaneous necrosis. The model had ′malignancy risk′ as the single decision variable. All evidence nodes had only two outcomes. The model was constructed using the GeNIe modeling environment and the user interface was implemented using VisualBasic.NET.
Results: Four studies provided data for 151 DMS patients out of which 44 patients had malignancy. The constructed model had one decision node and four evidence nodes. The software to calculate the numerical risk is available for download from http://www.gulfdoctor.net/derm/dmbbn.htm.
Conclusion: Bayesian Belief networks (BBN) can be used in situations like this where predictive factors are clearly associated with uncertainty. However, the present model may still be inaccurate because of the lack of reliable data and extensive testing.