Trial lecture
- August 17, 2023 12:15 AM, Kristen Nygaards sal (5370), Ole Johan Dahls hus
- Title: "Data driven cancer diagnostics"
Main research findings
To administer the Norwegian Cervical Cancer Screening program in accordance with national guidelines, the Cancer Registry of Norway (CRN) utilizes routinely collected information on diagnostic exam types and corresponding results for each individual. The availability of this data presents an opportunity to leverage data-driven technology for more personalized strategies in population-level cervical cancer prevention. However, existing machine learning methods for cervical cancer risk assessment rely on more extensive data material, including lifestyle and risk factor information, which is unattainable for the entire screening population. To address this gap, this thesis focuses on developing and validating machine learning methods derived from only the CRN screening data. By introducing methodology designed specifically for the Norwegian cervical cancer screening data, this thesis presents novel approaches to predicting the time-varying risk of cervical cancer development based on individual exam histories. Through numerical experiments, the thesis expands the understanding of the potential applications and limitations of these algorithms in personalized cervical cancer risk estimation.
Adjudication committee:
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Professor Edwin van den Heuvel, Department of Mathematics and Computer Science, Eindhoven University of Technology
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Professor Kjersti Engan, Department of Electrical Engineering and Computer Science, University of Stavanger, Norway
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Professor Ole Christian Lingjærde, Department of Informatics, University of Oslo, Norway
Supervisors
- Dr Jan F Nygård, Department of Registry Informatics, Cancer Registry of Norway
- Dr Valeriya Naumova, Cognite
- Professor Are Magnus Bruaset, Department of Informatics, University of Oslo
Chair of defence:
Professor Carsten Griwodz
Contact information at Department: Pernille Adine Nordby