کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4960294 1446427 2017 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Identification of thyroid gland activity in radioiodine therapy
ترجمه فارسی عنوان
شناسایی فعالیت غده تیروئید در درمان رادیو اکتیو
کلمات کلیدی
مدل دوهسته ای، محدودیت های پیشین، اطلاعات خارجی، انتشار لانژین، قانون توقف غیر پارامتری، برآورد دوز احتمالی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی


- Bayes identification of thyroid time-activity model with few data measured (2-3 pairs).
- prior 1: hard bounded parameter set; guarantees physically meaningful estimates.
- prior 2: processed historical data from patients' archive; strongly regularizes the posterior.
- numerical transformation to residence time using MCMC and Langevin diffusion.
- suitable for on-line probabilistic dose estimation using the MIRD methodology.

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Informatics in Medicine Unlocked - Volume 7, 2017, Pages 23-33
نویسندگان
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