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

0538241 20240103225227.420210121235959.9 Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy Praha ÚTIA AV ČR 2020 P Research Report 2388 Bayesian estimation thyroid carcinoma patient-specific inferences cav_un_auth*0403474 Murray Sean Ernest UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS IE S Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0370768 Quinn Anthony UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems IE Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2021/AS/quinn-0538241.pdf GA18-15970S GA ČR CZ cav_un_auth*0362986 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. BD 10000 10100 10102 2021 2 4 O 4o 20231122145512.2 RVO:67985556 http://hdl.handle.net/11104/0316080 S 2020 Soubory v repozitáři: 0538241.pdf 2020 Praha ÚTIA AV ČR