{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T16:09:18Z","timestamp":1701965358418},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"S12","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005416","name":"Norges Forskningsr\u00e5d","doi-asserted-by":"crossref","award":["300034"],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"Abstract<\/jats:title>\n Background<\/jats:title>\n Mass screening programs for cervical cancer prevention in the Nordic countries have strongly reduced cancer incidence and mortality at the population level. An alternative to the current mass screening is a more personalised screening strategy adapting the recommendations to each individual. However, this necessitates reliable risk prediction models accounting for disease dynamics and individual data. Herein we propose a novel matrix factorisation framework to classify females by the time-varying risk of being diagnosed with cervical cancer. We cast the problem as a time-series prediction model where the data from females in the Norwegian screening population are represented as sparse vectors in time and then combined into a single matrix. Using novel temporal regularisation and discrepancy terms for the cervical cancer screening context, we reconstruct complete screening profiles from this scarce matrix and use these to predict the next exam results indicating the risk of cervical cancer. The algorithm is validated on both synthetic and registry screening data by measuring the probability of agreement<\/jats:italic> (PoA) between Kaplan-Meier estimates.<\/jats:p>\n <\/jats:sec>\n Results<\/jats:title>\n In numerical experiments on synthetic data, we demonstrate that the novel regularisation and discrepancy term can improve the data reconstruction ability as well as prediction performance over varying data scarcity. Using a hold-out set of screening data, we compare several numerical models and find that the proposed framework attains the strongest PoA. We observe strong correlations between the empirical survival curves from our method and the hold-out data, and evaluate the ability of our framework to predict the females\u2019 next results for up to five years ahead in time using only their current screening histories as input.<\/jats:p>\n <\/jats:sec>\n Conclusions<\/jats:title>\n We have proposed a matrix factorization model for predicting future screening results and evaluated its performance in a female cohort to demonstrate the potential for developing prediction models for more personalized cervical cancer screening.<\/jats:p>\n <\/jats:sec>","DOI":"10.1186\/s12859-022-04949-8","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T09:03:45Z","timestamp":1668589425000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Matrix factorization for the reconstruction of cervical cancer screening histories and prediction of future screening results"],"prefix":"10.1186","volume":"23","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-7787-5663","authenticated-orcid":false,"given":"Geir Severin R. E.","family":"Langberg","sequence":"first","affiliation":[]},{"given":"Mikal","family":"Stapnes","sequence":"additional","affiliation":[]},{"given":"Jan F.","family":"Nyg\u00e5rd","sequence":"additional","affiliation":[]},{"given":"Mari","family":"Nyg\u00e5rd","sequence":"additional","affiliation":[]},{"given":"Markus","family":"Grasmair","sequence":"additional","affiliation":[]},{"given":"Valeriya","family":"Naumova","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"issue":"5","key":"4949_CR1","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1038\/bjc.2014.362","volume":"111","author":"S Vaccarella","year":"2014","unstructured":"Vaccarella S, Franceschi S, Engholm G, L\u00f6nnberg S, Khan S, Bray F. 50 years of screening in the Nordic countries: quantifying the effects on cervical cancer incidence. British J Cancer. 2014;111(5):965\u20139.","journal-title":"British J Cancer"},{"issue":"10167","key":"4949_CR2","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/S0140-6736(18)32470-X","volume":"393","author":"PA Cohen","year":"2019","unstructured":"Cohen PA, Jhingran A, Oaknin A, Denny L. Cervical cancer. Lancet. 2019;393(10167):169\u201382. https:\/\/doi.org\/10.1016\/S0140-6736(18)32470-X.","journal-title":"Lancet"},{"key":"4949_CR3","unstructured":"WHO: Cervical Cancer. https:\/\/www.who.int\/health-topics\/cervical-cancer"},{"issue":"4","key":"4949_CR4","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1158\/1055-9965.EPI-12-1406","volume":"22","author":"M Schiffman","year":"2013","unstructured":"Schiffman M, Wentzensen N. Human papillomavirus infection and the multistage carcinogenesis of cervical cancer. Cancer Epidemiol Prevent Biomark. 2013;22(4):553\u201360.","journal-title":"Cancer Epidemiol Prevent Biomark"},{"issue":"5","key":"4949_CR5","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/j.currproblcancer.2018.06.004","volume":"42","author":"JS Laurent","year":"2018","unstructured":"Laurent JS, Luckett R, Feldman S. Hpv vaccination and the effects on rates of hpv-related cancers. Current Probl Cancer. 2018;42(5):493\u2013506.","journal-title":"Current Probl Cancer"},{"key":"4949_CR6","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.ejca.2017.12.018","volume":"91","author":"K Pedersen","year":"2018","unstructured":"Pedersen K, Burger EA, Nyg\u00e5rd M, Kristiansen IS, Kim JJ. Adapting cervical cancer screening for women vaccinated against human papillomavirus infections: the value of stratifying guidelines. European J Cancer. 2018;91:68\u201375.","journal-title":"European J Cancer"},{"key":"4949_CR7","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8681","author":"BC Soper","year":"2020","unstructured":"Soper BC, Nyg\u00e5rd M, Abdulla G, Meng R, Nyg\u00e5rd JF. A hidden Markov model for population-level cervical cancer screening data. Stat Med. 2020. https:\/\/doi.org\/10.1002\/sim.8681.","journal-title":"Stat Med"},{"key":"4949_CR8","doi-asserted-by":"publisher","DOI":"10.1136\/jms.9.2.86","author":"JF Nyg\u00e5rd","year":"2002","unstructured":"Nyg\u00e5rd JF, Thoresen SO, Skare GB. The cervical cancer screening program in Norway, 1992\u20132000 Changes in pap-smear coverage and cervical cancer incidence. Int J Cancer. 2002. https:\/\/doi.org\/10.1136\/jms.9.2.86.","journal-title":"Int J Cancer"},{"key":"4949_CR9","unstructured":"Yu H-F, Rao N, Dhillon IS. Temporal regularized matrix factorization for high-dimensional time series prediction. In: Advances in Neural Information Processing Systems, 2016;847\u2013855."},{"key":"4949_CR10","unstructured":"Monti F, Bronstein MM, Bresson X. Geometric matrix completion with recurrent multi-graph neural networks. arXiv preprint. 2017. arXiv:1704.06803."},{"issue":"4","key":"4949_CR11","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","volume":"34","author":"MM Bronstein","year":"2017","unstructured":"Bronstein MM, Bruna J, LeCun Y, Szlam A, Vandergheynst P. Geometric deep learning: going beyond Euclidean data. IEEE Signal Process Mag. 2017;34(4):18\u201342.","journal-title":"IEEE Signal Process Mag"},{"key":"4949_CR12","doi-asserted-by":"crossref","unstructured":"Zhou J, Wang F, Hu J, Ye J. From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. 2014;135\u2013144.","DOI":"10.1145\/2623330.2623711"},{"issue":"30","key":"4949_CR13","doi-asserted-by":"publisher","first-page":"4621","DOI":"10.1002\/sim.8744","volume":"39","author":"NT Stevens","year":"2020","unstructured":"Stevens NT, Lu L. Comparing kaplan-meier curves with the probability of agreement. Stat Med. 2020;39(30):4621\u201335.","journal-title":"Stat Med"},{"key":"4949_CR14","unstructured":"Schnabel T, Swaminathan A, Singh A, Chandak N, Joachims T. Recommendations as treatments: debiasing learning and evaluation. In: International conference on machine learning. 2016;1670\u20131679. PMLR."},{"key":"4949_CR15","unstructured":"Ma W, Chen GH. 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J Global Optim. 1997;11(4):341\u201359.","journal-title":"J Global Optim"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04949-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-04949-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04949-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T09:04:39Z","timestamp":1668589479000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-04949-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,16]]},"references-count":17,"journal-issue":{"issue":"S12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["4949"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-04949-8","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,16]]},"assertion":[{"value":"12 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The project conducting this study is approved by the South East Norway Regional Committee for Medical and Health Research Ethics (application ID: 11752). All the research herein was performed in accordance with the relevant guidelines and regulations. The health registry data used in this study does not originate from clinical trials and therefore the ethical committee granted this study with an exception from informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"484"}}