Bayesian networks to support the management of patients with ASCUS/LSIL pap tests

• Published in: 2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH) pp. 103 - 107
• Main Authors: Bountris, Panagiotis, Tsirmpas, Charalampos, Haritou, Maria, Pouliakis, Abraham, Karakitsos, Petros, Koutsouris, Dimitrios
• Format: Conference Proceeding
• Language: English
• Published: ICST 01.11.2014
• Subjects: Accuracy; Bayes methods; bayesian networks; cervical cancer; cytology; DNA; Educational institutions; human papillomavirus (HPV); Inference algorithms; Neoplasms; risk assessment; Risk management
• DOI: 10.4108/Mobihealth33544.2014.7015920

In the majority of cases, cervical cancer (CxCa) develops as a result of underestimated abnormalities in the Pap test. Nowadays, there are ancillary molecular biology techniques providing important information related to CxCa and the Human Papillomavirus (HPV) natural history, including HPV DNA test, HPV mRNA tests and immunocytochemistry tests. However, these techniques have their own performance, advantages and limitations, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this paper we present a risk assessment model based on a Bayesian Network which, by combining the results of Pap test and ancillary tests, may identify women at true risk of developing cervical cancer and support the management of patients with ASCUS or LSIL cytology. The model, following the paradigm of other implemented systems, can be integrated into existing platforms and be available on mobile terminals for anytime/anyplace medical consultation.