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Risk Stratification in Cervical Cancer Screening – Validation and Generalization of a Data-driven  Screening Recall Model
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. (Komorowski Lab)ORCID iD: 0000-0001-8505-403x
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.ORCID iD: 0000-0002-0766-8789
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(English)Manuscript (preprint) (Other academic)
Keywords [en]
Cervical Cancer, Screening, Classification, Bioinformatics, Rough Sets
National Category
Bioinformatics and Systems Biology
Research subject
Bioinformatics; Bioinformatics
Identifiers
URN: urn:nbn:se:uu:diva-394291OAI: oai:DiVA.org:uu-394291DiVA, id: diva2:1358372
Available from: 2019-10-07 Created: 2019-10-07 Last updated: 2019-10-07
In thesis
1. Predictive Healthcare: Cervical Cancer Screening Risk Stratification and Genetic Disease Markers
Open this publication in new window or tab >>Predictive Healthcare: Cervical Cancer Screening Risk Stratification and Genetic Disease Markers
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The use of Machine Learning is rapidly expanding into previously uncharted waters. In the medicine fields there are vast troves of data available from hospitals, biobanks and registries that now are being explored due to the tremendous advancement in computer science and its related hardware. The progress in genomic extraction and analysis has made it possible for any individual to know their own genetic code. Genetic testing has become affordable and can be used as a tool in treatment, discovery, and prognosis of individuals in a wide variety of healthcare settings. This thesis addresses three different approaches to-wards predictive healthcare and disease exploration; first, the exploita-tion of diagnostic data in Nordic screening programmes for the purpose of identifying individuals at high risk of developing cervical cancer so that their screening schedules can be intensified in search of new dis-ease developments. Second, the search for genomic markers that can be used either as additions to diagnostic data for risk predictions or as can-didates for further functional analysis. Third, the development of a Ma-chine Learning pipeline called ||-ROSETTA that can effectively process large datasets in the search for common patterns. Together, this provides a functional approach to predictive healthcare that allows intervention at early stages of disease development resulting in treatments with reduced health consequences at a lower financial burden

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 62
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1862
Keywords
Bioinformatics, Cervical Cancer, Screening, Computer Science, Algorithmics, Machine Learning, Genetics, SNPs, Rough Sets
National Category
Bioinformatics and Systems Biology
Research subject
Bioinformatics
Identifiers
urn:nbn:se:uu:diva-394293 (URN)978-91-513-0768-8 (ISBN)
Public defence
2019-11-28, Room A1:111, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2019-11-06 Created: 2019-10-07 Last updated: 2019-11-06

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Baltzer, NicholasKomorowski, Jan

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