4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"

Research Article

Bayesian Networks to Support the Management of Patients with ASCUS/LSIL Pap Tests

Download56 downloads
  • @INPROCEEDINGS{10.4108/icst.mobihealth.2014.257381,
        author={Panagiotis Bountris and Charalampos Tsirmpas and Maria Haritou and Abraham Pouliakis and Petros Karakitsos and Dimitrios Koutsouris},
        title={Bayesian Networks to Support the Management of Patients with ASCUS/LSIL Pap Tests},
        proceedings={4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"},
        publisher={IEEE},
        proceedings_a={MOBIHEALTH},
        year={2014},
        month={12},
        keywords={cervical cancer cytology human papillomavirus (hpv) bayesian networks risk assessment},
        doi={10.4108/icst.mobihealth.2014.257381}
    }
    
  • Panagiotis Bountris
    Charalampos Tsirmpas
    Maria Haritou
    Abraham Pouliakis
    Petros Karakitsos
    Dimitrios Koutsouris
    Year: 2014
    Bayesian Networks to Support the Management of Patients with ASCUS/LSIL Pap Tests
    MOBIHEALTH
    IEEE
    DOI: 10.4108/icst.mobihealth.2014.257381
Panagiotis Bountris1,*, Charalampos Tsirmpas1, Maria Haritou2, Abraham Pouliakis3, Petros Karakitsos3, Dimitrios Koutsouris1
  • 1: Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens
  • 2: Institute of Communication and Computer Systems, National Technical University of Athens
  • 3: Department of Cytopathology, "ATTIKON" University Hospital, University of Athens
*Contact email: pbountris@biomed.ntua.gr

Abstract

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.