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MBF4CR: A Model-Based Framework for Supporting an Automated Cancer Registry System.

, , , , and . ECMFA, volume 9764 of Lecture Notes in Computer Science, page 191-204. Springer, (2016)

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EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System., , , , , and . ESEC/SIGSOFT FSE, page 1973-1984. ACM, (2023)Stratifying Cervical Cancer Risk with Registry Data., , , , , and . eScience, page 288-289. IEEE Computer Society, (2018)Automated Test Generation for Medical Rules Web Services: A Case Study at the Cancer Registry of Norway., , , , and . ESEC/SIGSOFT FSE, page 1937-1948. ACM, (2023)Phenotyping of Cervical Cancer Risk Groups via Generalized Low-Rank Models Using Medical Questionnaires., , , , and . NAIS, volume 1650 of Communications in Computer and Information Science, page 94-110. Springer, (2022)Can I trust my fake data - A comprehensive quality assessment framework for synthetic tabular data in healthcare., , , , , , , , , and . CoRR, (2024)Securing Federated GANs: Enabling Synthetic Data Generation for Health Registry Consortiums., and . ARES, page 89:1-89:9. ACM, (2023)Using clinical cancer registry data for estimation of quality indicators: Results from the Norwegian breast cancer registry., , , , and . Int. J. Medical Informatics, (2019)Automated Refactoring of OCL Constraints with Search., , , , and . IEEE Trans. Software Eng., 45 (2): 148-170 (2019)Matrix factorization for the reconstruction of cervical cancer screening histories and prediction of future screening results., , , , , and . BMC Bioinform., 23-S (12): 484 (2022)Cross-population evaluation of cervical cancer risk prediction algorithms., , , , , and . Int. J. Medical Informatics, (January 2024)