数字中医药（英文）2026，Vol.9Issue(3)：331-343,13.DOI:10.1016/j.dcmed.2026.08.003
中医精-神-寐理论的数字化框架及其在哺乳动物寿命建模中的应用
A theory-constrained digital framework of the traditional Chinese medicine Jing-Shen-Mei theory for mammalian lifespan modeling
摘要
Abstract
Objective To construct a theory-constrained mathematical framework for the traditional Chi-nese medicine(TCM)Jing(精,essence)-Shen(神,spirit)-Mei(寐,sleep)theory and to evalu-ate the applicability of a pre-specified rational closure to cross-species mammalian lifespan prediction. Methods Jing,Shen,and Mei were operationalized as a finite life-budget reservoir,effective homeostatic capacity,and sleep-mediated daily burden reduction,respectively.A harmo-nized complete-case dataset was constructed by integrating maximum lifespan,sleep dura-tion,and cortical/pallial neuronal abundance from three sources:the AnAge database within the Human Ageing Genomic Resources(HAGR),the Boston University mammalian sleep records,and curated comparative neuroanatomical compilations.The primary dataset in-cluded 53 species,comprising 48 directly measured or curated neuronal records and 5 explic-itly labelled records derived from brain-mass-estimated.The rational exponents(α=2/5,β=1/4,and η=1/5)were pre-specified prior to any empirical fitting,leaving only the global coef-ficient C to be estimated within training folds.Model performance was evaluated using re-peated five-fold cross-validation with 100 repeats,alongside sensitivity analyses on the 48-species direct-measurement subset and leave-one-out cross-validation(LOOCV).Compara-tors included an unconstrained power-law model and representative machine learning(ML)regressors.Root mean square error(RMSE),mean absolute error(MAE),and coefficient of determination(R2)served as primary metrics,and a 729-combination parameter-grid analy-sis was conducted to assess parameter identifiability. Results On the 53-species curated dataset,the theory-selected rational closure achieved cross-validated RMSE=13.73 years,MAE=9.84 years,and R2=0.660.Compared with repre-sentative ML and algebraic benchmark models under identical out-of-fold evaluation,the ra-tional closure showed competitive predictive performance while maintaining a lower-dimen-sional interpretable structure.Performance remained robust after excluding the five brain-mass-estimated neuronal records:repeated five-fold cross-validation on the 48 directly mea-sured species achieved RMSE=14.27 years,MAE=10.14 years,and R2=0.647,and LOOCV achieved RMSE=14.16 years,MAE=10.08 years,and R2=0.652.The grid-search analysis identified a minimum RMSE of 13.03 years,while the pre-specified rational closure delivered an RMSE of 13.73 years.Notably,11,27,and 69 parameter combinations fell within 1%,5%,and 10%of the minimum,respectively,indicating that the rational closure is near-optimal yet non-unique. Conclusion This study provides a preliminary,computationally grounded formalization of the TCM Jing-Shen-Mei framework and demonstrates that a pre-specified,one-coefficient ra-tional closure can maintain empirical utility in a sparse-data,cross-species predictive proto-col.关键词
数字中医/精-神-寐框架/哺乳动物寿命/理论选择有理闭包/整合能力/稀疏数据协议/机器学习基准测试Key words
Digitalization of traditional Chinese medicine/Jing-Shen-Mei framework/Mammalian lifespan/Theory-selected rational closure/Integrative capacity/Sparse-data protocol/Machine learning benchmarking引用本文复制引用
陈俊名,金霞..中医精-神-寐理论的数字化框架及其在哺乳动物寿命建模中的应用[J].数字中医药（英文）,2026,9(3):331-343,13.基金项目
Scientific Research Start-up Fund under the Second Batch of the Guangxi Young Talent Universal Support Program(No.325,2025). （No.325,2025）
