Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine
10.1097/st9.0000000000000025
- Author:
Wei YANG
1
;
Danhui YI
2
;
XiaoHua ZHOU
3
;
Yuanming LENG
4
Author Information
1. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
2. School of Statistics, Renmin University of China, Beijing, China
3. Beijing International Center for Mathematical Research, Peking University, Beijing, China; Department ofBiostatistics, School of Public Health, Peking University, Beijing, China; National Engineering Laboratory of Big Data Analysis and Applied Technology, Peking University, Beijing, China
4. Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
- Publication Type:Journal Article
- Keywords:
Traditional Chinese medicine;
Real-world study;
Data science;
Causal learning;
Health information system;
Machine learning;
Artificial intelligence;
Treatment effects
- From:
Science of Traditional Chinese Medicine
2024;2(1):57-65
- CountryChina
- Language:English
-
Abstract:
Real-world clinical evaluation of traditional Chinese medicine (RWCE-TCM) is a method for comprehensively evaluating the clinical effects of TCM, with the aim of delving into the causality between TCM intervention and clinical outcomes. The study explored data science and causal learning methods to transform RWD into reliable real-world evidence, aiming to provide an innovative approach for RWCE-TCM. This study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in RWCE-TCM. The methodology involves several key steps, including data integration and warehouse building, high-dimensional feature selection, the use of interpretable statistical machine learning algorithms, complex networks, and graph network analysis, knowledge mining techniques such as natural language processing and machine learning, observational study design, and the application of artificial intelligence tools to build an intelligent engine for translational analysis. The goal is to establish a method for clinical positioning, applicable population screening, and mining the structural association of TCM characteristic therapies. In addition, the study adopts the principle of real-world research and a causal learning method for TCM clinical data. We constructed a multidimensional clinical knowledge map of "disease-syndrome-symptom-prescription-medicine" to enhance our understanding of the diagnosis and treatment laws of TCM, clarify the unique therapies, and explore information conducive to individualized treatment. The causal inference process of observational data can address confounding bias and reduce individual heterogeneity, promoting the transformation of TCM RWD into reliable clinical evidence. Intelligent data science improves efficiency and accuracy for implementing RWCE-TCM. The proposed data science methodology for TCM can handle complex data, ensure high-quality RWD acquisition and analysis, and provide in-depth insights into clinical benefits of TCM. This method supports the intelligent translation and demonstration of RWD in TCM, leads the data-driven translational analysis of causal learning, and innovates the path of RWCE-TCM.
