数字中医药（英文）2026，Vol.9Issue(2)：173-183,11.DOI:10.1016/j.dcmed.2026.05.002
"未病-已病"窗口期的中医临床诊疗模式构建:中医状态学视阈下客观化多模态数据的融合
Construction of the clinical diagnosis and treatment model during the"pre-disease to disease"window period in traditional Chinese medicine:integration of objective multimodal data from the perspective of traditional Chinese medicine stateology
摘要
Abstract
The philosophy of"treating disease before its onset"is a fundamental concept of traditional Chinese medicine(TCM),permeating its diagnostic and therapeutic framework,and is cen-tral to clinical practice.However,current TCM diagnostic and treatment models for the"pre-disease to disease"window period face several limitations,including the lack of comprehen-sive clinical parameters,difficulties in characterizing and integrating heterogeneous multi-modal data,and insufficient dynamic precision in interventions and efficacy evaluations.To address these issues,guided by Professor Candong Li's theory of TCM stateology,this study focuses on integrating objective multimodal data.It proposes a new model for personalized TCM diagnosis and treatment targeting the"pre-disease to disease"window period.This ap-proach first proposes the idea of restructuring the conceptual framework of"symptom"and integrating multi-source heterogeneous data at macroscopic,mesoscopic,and microscopic levels to form a three-dimensional assessment indicator system.By integrating graph neural networks,convolutional neural networks,attention mechanisms,and knowledge graph-guid-ed weight allocation,this approach enables collaborative representation,alignment,and fu-sion of multi-source data.Subsequently,it plans to construct a multimodal fusion model at both feature and decision levels,in order to establish mappings between indicators and TCM state elements,and to screen key indicators characterizing pathological evolution during the window period.Furthermore,it proposes a technical path for enhancing model interpretabili-ty using methods such as SHapley Additive exPlanations(SHAP)and Ablation-CAM++.Final-ly,with state assessment as the core,it proposes the concept of constructing a dynamic evalu-ation method for individualized diagnosis and treatment based on time-series data analysis using algorithms such as long short-term memory(LSTM)networks and gated recurrent units(GRUs).Moreover,a causal inference framework and semi-supervised learning strategies are introduced to enable quantitative evaluation of individual intervention effects and to provide interpretable therapeutic feedback,forming a complete technical path from data representa-tion and fusion,weight adjustment,and interpretability analysis,to dynamic diagnosis feed-back.This study aims to address deficiencies in the current TCM diagnosis and treatment model during the"pre-disease to disease"window period and to provide an operational framework for the clinical practice of TCM's"treating disease before its onset".关键词
中医状态学/窗口期/临床诊疗模式/多模态数据融合/深度学习Key words
Traditional Chinese medicine stateology/Window period/Clinical diagnostic and therapeutic model/Multimodal data fusion/Deep learning引用本文复制引用
李丹阳,艾民,周派,邓颖,杨朝阳,彭清华.."未病-已病"窗口期的中医临床诊疗模式构建:中医状态学视阈下客观化多模态数据的融合[J].数字中医药（英文）,2026,9(2):173-183,11.基金项目
Key Research Project of the National Key Research and Development Program(2023YFC3503003),Special Pro-ject for Central Government's Guidance on Subnational Science and Technology Development by the Fujian Provincial Department of Science and Technology(2024L3014),and Domestic First-class Discipline Devel-opment Project of Hunan University of Chinese Medicine(XJF[2022]No.57) （2023YFC3503003）
