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Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy

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    0538241 - ÚTIA 2021 CZ eng V - Research Report
    Murray, Sean Ernest - Quinn, Anthony
    Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy.
    Praha: ÚTIA AV ČR, 2020. Research Report, 2388.
    R&D Projects: GA ČR(CZ) GA18-15970S
    Institutional support: RVO:67985556
    Keywords : Bayesian estimation * thyroid carcinoma * patient-specific inferences
    OECD category: Applied mathematics

    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.
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