Publication: Bayesian-based decision support system for assessing the needs for orthodontic treatment
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Issued Date
2018
Resource Type
File Type
application/pdf
ISSN
20933681
Other identifier(s)
2-s2.0-85041800496
Rights Holder(s)
มหาวิทยาลัยศรีนครินทรวิโรฒ
Bibliographic Citation
Healthcare Informatics Research. Vol 24, No.1 (2018), p.22-28
Suggested Citation
Thanathornwong B. Bayesian-based decision support system for assessing the needs for orthodontic treatment. Healthcare Informatics Research. Vol 24, No.1 (2018), p.22-28. doi:10.4258/hir.2018.24.1.22 Retrieved from: https://hdl.handle.net/20.500.14740/3810
Author(s)
Abstract
Objectives: In this study, a clinical decision support system was developed to help general practitioners assess the need for orthodontic treatment in patients with permanent dentition. Methods: We chose a Bayesian network (BN) as the underlying model for assessing the need for orthodontic treatment. One thousand permanent dentition patient data sets chosen from a hospital record system were prepared in which one data element represented one participant with information for all variables and their stated need for orthodontic treatment. To evaluate the system, we compared the assessment results based on the judgements of two orthodontists to those recommended by the decision support system. Results: In a BN decision support model, each variable is modelled as a node, and the causal relationship between two variables may be represented as a directed arc. For each node, a conditional probability table is supplied that represents the probabilities of each value of this node, given the conditions of its parents. There was a high degree of agreement between the two orthodontists (kappa value = 0.894) in their diagnoses and their judgements regarding the need for orthodontic treatment. Also, there was a high degree of agreement between the decision support system and orthodontists A (kappa value = 1.00) and B (kappa value = 0.894). Conclusions: The study was the first testing phase in which the results generated by the proposed system were compared with those suggested by expert orthodontists. The system delivered promising results; it showed a high degree of accuracy in classifying patients into groups needing and not needing orthodontic treatment. © 2018 The Korean Society of Medical Informatics.