Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine

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.

Controllable image generation based on causal representation learning

Artificial intelligence generated content(AIGC)has emerged as an indispensable tool for producing large-scale content in various forms,such as images,thanks to the significant role that AI plays in imitation and production.However,interpretability and controllability remain challenges.Existing AI methods often face challenges in producing images that are both flexible and controllable while considering causal relationships within the images.To address this issue,we have developed a novel method for causal controllable image generation(CCIG)that combines causal representation learning with bi-directional generative adversarial networks(GANs).This approach enables humans to control image attributes while considering the rationality and interpretability of the generated images and also allows for the generation of counterfactual images.The key of our approach,CCIG,lies in the use of a causal structure learning module to learn the causal relationships between image attributes and joint optimization with the encoder,generator,and joint discriminator in the image generation module.By doing so,we can learn causal representations in image’s latent space and use causal intervention operations to control image generation.We conduct extensive experiments on a real-world dataset,CelebA.The experimental results illustrate the effectiveness of CCIG.

Calculate Joint Probability Distribution of Steady Directed Cyclic Graph with Local Data and Domain Casual Knowledge

It is desired to obtain the joint probability distribution(JPD) over a set of random variables with local data, so as to avoid the hard work to collect statistical data in the scale of all variables. A lot of work has been done when all variables are in a known directed acyclic graph(DAG). However, steady directed cyclic graphs(DCGs) may be involved when we simply combine modules containing local data together, where a module is composed of a child variable and its parent variables. So far, the physical and statistical meaning of steady DCGs remain unclear and unsolved. This paper illustrates the physical and statistical meaning of steady DCGs, and presents a method to calculate the JPD with local data, given that all variables are in a known single-valued Dynamic Uncertain Causality Graph(S-DUCG), and thus defines a new Bayesian Network with steady DCGs. The so-called single-valued means that only the causes of the true state of a variable are specified, while the false state is the complement of the true state.