Artificial intelligence(AI)serves as a key technology in global industrial transformation and technological restructuring and as the core driver of the fourth industrial revolution.Currently,deep learning techniques,s...Artificial intelligence(AI)serves as a key technology in global industrial transformation and technological restructuring and as the core driver of the fourth industrial revolution.Currently,deep learning techniques,such as convolutional neural networks,enable intelligent information collection in fields such as tongue and pulse diagnosis owing to their robust feature-processing capabilities.Natural language processing models,including long short-term memory and transformers,have been applied to traditional Chinese medicine(TCM)for diagnosis,syndrome differentiation,and prescription generation.Traditional machine learning algorithms,such as neural networks,support vector machines,and random forests,are also widely used in TCM diagnosis and treatment because of their strong regression and classification performance on small structured datasets.Future research on AI in TCM diagnosis and treatment may emphasize building large-scale,high-quality TCM datasets with unified criteria based on syndrome elements;identifying algorithms suited to TCM theoretical data distributions;and leveraging AI multimodal fusion and ensemble learning techniques for diverse raw features,such as images,text,and manually processed structured data,to increase the clinical efficacy of TCM diagnosis and treatment.展开更多
The development of machine learning in complex system is hindered by two problems nowadays.The first problem is the inefficiency of exploration in state and action space,which leads to the data-hungry of some state-of...The development of machine learning in complex system is hindered by two problems nowadays.The first problem is the inefficiency of exploration in state and action space,which leads to the data-hungry of some state-of-art data-driven algorithm.The second problem is the lack of a general theory which can be used to analyze and implement a complex learning system.In this paper,we proposed a general methods that can address both two issues.We combine the concepts of descriptive learning,predictive learning,and prescriptive learning into a uniform framework,so as to build a parallel system allowing learning system improved by self-boosting.Formulating a new perspective of data,knowledge and action,we provide a new methodology called parallel learning to design machine learning system for real-world problems.展开更多
基金supported by grants from the National Natural Science Foundation of China(Key Program)(No.82230124)Traditional Chinese Medicine Inheritance and Innovation“Ten million”talent project-Qihuang Project Chief Scientist Project(No.0201000401)+1 种基金State Administration of Traditional Chinese Medicine 2nd National Traditional Chinese Medicine Inheritance Studio Construction Project(Official Letter of the State Office of Traditional Chinese Medicine[2022]No.245)National Natural Science Foundation of China(General Program)(No.81974556).
文摘Artificial intelligence(AI)serves as a key technology in global industrial transformation and technological restructuring and as the core driver of the fourth industrial revolution.Currently,deep learning techniques,such as convolutional neural networks,enable intelligent information collection in fields such as tongue and pulse diagnosis owing to their robust feature-processing capabilities.Natural language processing models,including long short-term memory and transformers,have been applied to traditional Chinese medicine(TCM)for diagnosis,syndrome differentiation,and prescription generation.Traditional machine learning algorithms,such as neural networks,support vector machines,and random forests,are also widely used in TCM diagnosis and treatment because of their strong regression and classification performance on small structured datasets.Future research on AI in TCM diagnosis and treatment may emphasize building large-scale,high-quality TCM datasets with unified criteria based on syndrome elements;identifying algorithms suited to TCM theoretical data distributions;and leveraging AI multimodal fusion and ensemble learning techniques for diverse raw features,such as images,text,and manually processed structured data,to increase the clinical efficacy of TCM diagnosis and treatment.
基金supported in part by the National Natural Science Foundation of China(91520301)
文摘The development of machine learning in complex system is hindered by two problems nowadays.The first problem is the inefficiency of exploration in state and action space,which leads to the data-hungry of some state-of-art data-driven algorithm.The second problem is the lack of a general theory which can be used to analyze and implement a complex learning system.In this paper,we proposed a general methods that can address both two issues.We combine the concepts of descriptive learning,predictive learning,and prescriptive learning into a uniform framework,so as to build a parallel system allowing learning system improved by self-boosting.Formulating a new perspective of data,knowledge and action,we provide a new methodology called parallel learning to design machine learning system for real-world problems.
