On December 11th and 12th, 2020, the "2020 Smart Healthcare Academic Conference" was held in Guangzhou, co-organized by Sun Yat-sen University, University of Science and Technology Beijing, Beijing University of Posts and Telecommunications, and Sun Yat-sen University Zhongshan Ophthalmic Center, initiated by the Chinese Association for Artificial Intelligence. Nearly 200 experts, scholars, and representatives from hospitals, universities, and well-known enterprises in the field of smart healthcare "industry-academia-research-application" gathered to discuss the basic research, cutting-edge technologies, and key technological development of smart healthcare, aiming to accelerate the application of artificial intelligence technology in the field of smart healthcare in China. Futong Technology, as an artificial intelligence technology company, was invited to attend the conference. During the conference, the Intelligent Medical Committee of the Chinese Association for Artificial Intelligence held a re-election, and Dr. Li Rui from Futong Hengxian Artificial Intelligence Laboratory was elected as a standing committee member of the Intelligent Medical Committee. He was also invited to give a speech on the topic of "Knowledge Reasoning and Intelligent Optimization in Active Health Management" at the conference.

Knowledge Reasoning and Intelligent Optimization in Active Health Management

In recent years, emerging technologies such as artificial intelligence and big data have been increasingly applied in the medical field, promoting changes in medical service models and health management concepts, and greatly improving medical productivity. Dr. Li Rui Rui focused on introducing active health management, construction of medical knowledge graphs, application of knowledge reasoning, and intelligent optimization decision-making in his speech.

01 Active Health Management

Currently, chronic diseases such as diabetes, cardiovascular and cerebrovascular diseases, and cancer in China are showing a trend of occurring at younger ages. The uneven distribution of medical resources, outdated screening, and intervention measures lead to a lack of awareness and intervention for some chronic diseases or late intervention, which greatly affects patients' prognosis and quality of life. With the change in consumption concepts and the improvement of living standards, the public's demand for health management is increasingly strong, and active health management has become an important measure to prevent diseases and reduce medical expenses.

Dr. Li Rui Rui stated that active health management is an important way to achieve a continuous healthy lifestyle and good social adaptability. Its characteristics include proactive discovery, scientific assessment, active adjustment, and health promotion. Health management covers the entire process of an individual from being healthy to sub-healthy, through examination to disease discovery, treatment, and recovery. We hope to intervene in a timely manner to prevent the occurrence of diseases. However, due to the many links involved, the broad dimensions, and the high information load, it is necessary to establish a knowledge graph for the management of knowledge and information.

Futong Dongfang, in response to the public's health management demands, has constructed an intelligent active health management application "5+AI Health" based on the health management system. This application features causal reasoning capabilities, automatic knowledge updates, and personalized decision-making recommendations.

02 Construction and Application of Medical Knowledge Graphs

Medical knowledge graphs describe concepts and relationships in the objective world in a structured form and are a type of large-scale semantic network. Their construction is divided into four stages: data acquisition, information extraction, knowledge fusion, and knowledge processing. In the stages of information extraction and knowledge fusion, many deep learning techniques are employed, such as attribute extraction, entity extraction, relationship extraction, co-reference resolution, and entity disambiguation.

In practice, due to the complex relationships in medical knowledge graphs, knowledge reasoning can be relied upon to eliminate noise and complete the graph. Dr. Li Rui pointed out that there are currently two mainstream reasoning methods in knowledge reasoning: one based on graph structure and statistical rule mining, and the other based on knowledge graph embedding. These two types of methods are complementary, allowing for joint reasoning of rules and paths, mining rules from the knowledge graph, and embedding representations of entities, relationships, and rules.

03 Multi-Objective Resource Optimization Algorithm Platform

In the intelligent active health management application "5+AI Health," Futong Dongfang has leveraged its multi-objective resource optimization algorithm platform to construct a healthy diet recommendation optimization model, aiding the public in intelligent health management.

The Multi-Objective Resource Optimization Algorithm Platform is an enterprise-level resource planning and scheduling platform developed by the Futong Hengxian Artificial Intelligence Laboratory. It originates from multi-objective optimization theory and is primarily aimed at resource allocation and planning and scheduling problems. For the first time, it combines multi-objective optimization theory, AI algorithms, and knowledge reasoning technology to construct multi-objective decision optimization models for various business scenarios, obtaining precise and feasible optimization solutions within a specified time, solving multi-constraint and multi-objective optimization decision-making problems.

Booth Interaction: Addressing Doubts and Questions

At this conference, Futong Dongfang was also invited to participate in the on-site exhibition, showcasing their smart healthcare products and service systems comprehensively. During the event, conference attendees frequently visited the Futong Dongfang booth to inquire about related issues. The staff of Futong Dongfang answered each question and actively discussed the application and development of artificial intelligence technology in the field of smart healthcare.