A Bayesian network for diagnosis of primary bone tumors. J Digit Imaging 2001 Jun;14(2 Suppl 1):56-7
Date
07/10/2001Pubmed ID
11442121Pubmed Central ID
PMC3452681DOI
10.1007/BF03190296Scopus ID
2-s2.0-0035357333 (requires institutional sign-in at Scopus site) 34 CitationsAbstract
The authors developed a Bayesian network to differentiate among five benign and five malignant neoplasms of the appendicular skeleton using the patient's age and sex and 17 radiographic characteristics. In preliminary evaluation with physicians in training, the model identified the correct diagnosis in 19 cases (68%), and included the correct diagnosis among the two most probable diagnoses in 25 cases (89%). Bayesian networks can capture and apply knowledge of primary bone neoplasms. Further testing and refinement of the model are underway.
Author List
Kahn CE Jr, Laur JJ, Carrera GFMESH terms used to index this publication - Major topics in bold
Bayes TheoremBone Neoplasms
Diagnosis, Differential
Female
Humans
Male
Models, Statistical
Radiography
