From data to decision: an interpretable machine learning model for optimizing RAI therapy in Graves’ hyperthyroidism

The developed interpretable machine learning framework offers a precise, personalized tool for predicting RAI outcomes, potentially guiding optimizing dosing strategies to reduce treatment failure.

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Publication Details

Journal
Frontiers in Endocrinology
Published
2026-01-26
DOI
https://doi.org/10.3389/fendo.2025.1711029
Primary Topic
Thyroid Disorders and Treatments
Type
article
Field-Weighted Citation Impact
0.00
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article

From data to decision: an interpretable machine learning model for optimizing RAI therapy in Graves’ hyperthyroidism

Kehua Liao, Fahuan Song, Shaozhou Mo, Xiaojuan Wei et al.
Frontiers in Endocrinology
Thyroid Disorders and Treatments
article

From data to decision: an interpretable machine learning model for optimizing RAI therapy in Graves’ hyperthyroidism

Kehua Liao, Fahuan Song, Shaozhou Mo, Xiaojuan Wei, Wentan Huang, Zeyong Sun, Yan Chen, Dongyun Meng, Lu Lu
article en

Abstract

The developed interpretable machine learning framework offers a precise, personalized tool for predicting RAI outcomes, potentially guiding optimizing dosing strategies to reduce treatment failure.

Frontiers in EndocrinologyVol. 16
The People's Hospital of Guangxi Zhuang Autonomous Region (CN)
Gender equality
Openalex Percentile: Top 79%
Thyroid Disorders and Treatments
DOIOpen Access PDFOpenAlex
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