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Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.<\/jats:p>","DOI":"10.1155\/2014\/341483","type":"journal-article","created":{"date-parts":[[2014,4,9]],"date-time":"2014-04-09T21:22:30Z","timestamp":1397078550000},"page":"1-20","source":"Crossref","is-referenced-by-count":30,"title":["An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection"],"prefix":"10.1155","volume":"2014","author":[{"given":"Panagiotis","family":"Bountris","sequence":"first","affiliation":[{"name":"Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Politechniou 9, 15773 Zografou Campus, Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Haritou","sequence":"additional","affiliation":[{"name":"Institute of Communication and Computer 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