Research Progress and Optimization Ideas of Risk Prediction Models Combining Osteoporosis Syndrome and Disease
- VernacularTitle:骨质疏松症病证结合风险预测模型研究进展及优化思路
- Author:
Xu WEI
1
;
Zikai JIN
;
Yili ZHANG
;
Hao SHEN
;
Yanming XIE
;
Liguo ZHU
Author Information
- Publication Type:Journal Article
- Keywords: Osteoporosis; Disease-syndrome combination; Risk assessment; Machine learning algorithms
- From: World Science and Technology-Modernization of Traditional Chinese Medicine 2025;27(9):2444-2452
- CountryChina
- Language:Chinese
- Abstract: The risk prediction approach integrating disease and syndrome aligns more precisely with the clinical diagnosis and treatment needs of osteoporosis.Prior research has established a consensus on the model development methodology encompassing"Target outcome selection→ Key information collection→ Data mining and modeling →Model performance evaluation".Building on this foundation,a cohort of osteoporosis patients and syndrome cases with stable follow-up is established.Utilizing artificial intelligence algorithms,critical information in traditional Chinese medicine(TCM)symptoms and syndromes is objectively characterized and quantified alongside imaging data.Employing multi-omics sequencing technology,we seek to identify highly specific microscopic molecular information,analyze potential correlations among various dimensions of information,and develop a multidimensional risk prediction model for osteoporosis with distinctive TCM attributes.This model aims to identify biomarkers with both"disease"and"syndrome"characteristics,thereby advancing the precision diagnosis and treatment system for osteoporosis.
