Exploring the importance of mixed autogenous vaccines as a potential determinant of lung consolidation in lambs using Bayesian networks.

Bayesian networks are used to evaluate the effectiveness of mixed autogenous vaccines in fattening lambs to prevent the ovine respiratory syndrome. An experiment was performed with 460 fattening lambs, which were clustered into four groups according to the kind of vaccine received (Pasteurella spp., Mycoplasma spp., Mixed Mycoplasma-Pasteurella or placebo). After slaughtering, lungs were collected, and macroscopic and microscopic studies were performed. A microbiological study was carried out to evaluate the presence of Mycoplasma spp. and Pasteurellaceae by conventional culture and identification by nested polymerase chain reaction. To the best of the authors' knowledge, Bayesian networks have not been used to evaluate the effect of vaccines on the absence/presence of lung consolidation. Our results revealed that the use of mixed autogenous vaccines can decrease lung consolidation from 15.75% (12.42-19.08) to 9.24% (6.59-11.89). Therefore, the use of these autogenous vaccines in farms could be considered an effective control tool against ovine respiratory syndrome.

Computer-aided diagnosis system: a Bayesian hybrid classification method.

A novel method to classify multi-class biomedical objects is presented. The method is based on a hybrid approach which combines pairwise comparison, Bayesian regression and the k-nearest neighbor technique. It can be applied in a fully automatic way or in a relevance feedback framework. In the latter case, the information obtained from both an expert and the automatic classification is iteratively used to improve the results until a certain accuracy level is achieved, then, the learning process is finished and new classifications can be automatically performed. The method has been applied in two biomedical contexts by following the same cross-validation schemes as in the original studies. The first one refers to cancer diagnosis, leading to an accuracy of 77.35% versus 66.37%, originally obtained. The second one considers the diagnosis of pathologies of the vertebral column. The original method achieves accuracies ranging from 76.5% to 96.7%, and from 82.3% to 97.1% in two different cross-validation schemes. Even with no supervision, the proposed method reaches 96.71% and 97.32% in these two cases. By using a supervised framework the achieved accuracy is 97.74%. Furthermore, all abnormal cases were correctly classified.