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使用稀缺数据对初级甲状腺功能低下症进行特定对象的剂量估计

Devleena Ghosh¹, Chittaranjan Mandal¹

¹Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India.

Journal of computational biology : a journal of computational molecular cell biology

|February 17, 2025

概括

这项研究通过模拟患者特定的参数和使用先进的计算分析来估计针对低甲状腺症的个性化甲状腺药物剂量,以确定最佳药物范围,即使数据有限.

科学领域:

计算生物学计算生物学
内分泌学在内分泌学.
数学建模的数学建模

背景情况:

个性化医疗需要准确的剂量估计,如初级甲状腺功能低下症等条件.
数据稀缺和参数不确定性挑战了传统的剂量优化方法.
当模型参数具有固有的不确定性时,现有的优化技术可能会失败.

研究的目的:

开发一种针对主体特定的剂量估计方法,用于原发性甲状腺功能低下症.
在个性化建模中解决数据稀疏性和参数不确定性.
用数学建模和先进分析来确定最佳药物剂量范围.

主要方法:

使用治疗前和治疗后的观测,以不确定性估计了特定对象的模型参数.
在普通微分方程模型中,使用富里埃数列近似来建模周期性药物管理.
基于满意度模块理论 (SMT) 的分析被应用来确定最佳剂量范围.
一个回归模型被用于初始剂量估计.

主要成果:

开发并验证了计算程序,用于对特定患者的剂量估计.
该研究成功估计了最佳药物剂量范围,尽管数据有限和参数不确定性.
开发的方法为个性化甲状腺功能低下症治疗提供了强大的方法.

结论:

基于SMT的分析有效地确定了原发性甲状腺功能低下症的最佳药物剂量范围.
该方法适应参数不确定性,提高个性化剂量建议的可靠性.
这项工作为内分泌学中的精密医学提供了一个新的计算框架.

关键词:

药物管理建模药物管理建模参数估计的参数估计.满足性模块理论的满足性稀少的数据稀少的数据.甲状腺热调节路径

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