Original title: Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy
Authors: Murray, Sean Ernest ; Quinn, Anthony
Document type: Research reports
Year: 2020
Language: eng
Series: Research Report, volume: 2388
Abstract: This research report outlines a selective transfer approach for Bayesian estimation of patient-specific levels of radioiodine activity in the thyroid during the treatment of differentiated thyroid carcinoma. The work seeks to address some limitations of previous approaches [4] which involve generic, non-selective transfer of archival data. It is proposed that improvements in patient-specific inferences may be achieved via transferring external population knowledge selectively. This involves matching the patient to a similar sub-population based on available metadata, generating a Gaussian Mixture Model within the partitioned data, and optimally transferring a data predictive distribution from the sub-population to the specific patient. Additionally, a performance evaluation method is proposed and early-stage results presented.
Keywords: Bayesian estimation; patient-specific inferences; thyroid carcinoma
Project no.: GA18-15970S (CEP)
Funding provider: GA ČR

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2021/AS/quinn-0538241.pdf
Original record: http://hdl.handle.net/11104/0316080

Permalink: http://www.nusl.cz/ntk/nusl-432899


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