Identification of thyroid gland activity in radioiodine therapy
| Autor: | Anthony Quinn, Ladislav Jirsa, Ferdinand Varga |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Posterior probability Thyroid Inference Health Informatics Context (language use) Radioiodine therapy lcsh:Computer applications to medicine. Medical informatics 01 natural sciences 030218 nuclear medicine & medical imaging 010104 statistics & probability 03 medical and health sciences Identification (information) 0302 clinical medicine medicine.anatomical_structure Bayesian identification Linear regression medicine lcsh:R858-859.7 0101 mathematics Nuclear medicine business Mathematics |
| Zdroj: | Informatics in Medicine Unlocked, Vol 7, Iss, Pp 23-33 (2017) |
| ISSN: | 2352-9148 |
| Popis: | The Bayesian identification of a linear regression model (called the biphasic model) for time dependence of thyroid gland activity in 131I radioiodine therapy is presented. Prior knowledge is elicited via hard parameter constraints and via the merging of external information from an archive of patient records. This prior regularization is shown to be crucial in the reported context, where data typically comprise only two or three high-noise measurements. The posterior distribution is simulated via a Langevin diffusion algorithm, whose optimization for the thyroid activity application is explained. Excellent patient-specific predictions of thyroid activity are reported. The posterior inference of the patient-specific total radiation dose is computed, allowing the uncertainty of the dose to be quantified in a consistent form. The relevance of this work in clinical practice is explained. Keywords: Biphasic model, Prior constraints, External information, Langevin diffusion, Nonparametric stopping rule, Probabilistic dose estimation |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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