Abstract

The scale of medical data is growing rapidly, and these data come from different data sources. The amount of data is huge, the production speed is fast, and the format is different. Case data is very important because it contains a lot of medical knowledge about diseases, drugs, treatments, etc. It can provide important support for the development of smart medicine. Knowledge graph is a graph-based data structure, which can well represent the relationship between these medical data in reality and form a semantic network. This research uses knowledge graph technology to connect trivial and scattered knowledge in various medical information systems to assist in disease diagnosis. This research takes thyroid disease as an example, constructs a medical knowledge graph and applies it to intelligent medical diagnosis. First, extract the relationships between biomedical entities to construct a biomedical knowledge graph. Then, the entities and relationships in the knowledge graph are transformed into low-dimensional continuous vectors through the knowledge graph embedding method. Finally, the known pathological disease relationship data is used to train the disease diagnosis model of the bidirectional long short-term memory network (BSTLM). Experiments show that the thyroid disease diagnosis method that combines knowledge graphs and deep learning has a better diagnostic effect. This shows that smart medical care based on the knowledge graph will provide a solution path for alleviating the shortage of domestic high-quality medical resources.

Highlights

  • Intelligent medical care [1,2] refers to the use of technologies such as the Internet of Things and artificial intelligence to realize the interaction between patients and medical staff, medical institutions, and medical equipment, and gradually achieve informatization

  • The core and key of intelligent medical care is intelligent diagnosis and treatment, that is, making computers become a brain with medical knowledge, so as to provide assistant decision-making for doctors' diagnosis and treatment

  • The results show that the classification results based on the structured features extracted from the knowledge graph are better than those based on the vector space model

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Summary

INTRODUCTION

Intelligent medical care [1,2] refers to the use of technologies such as the Internet of Things and artificial intelligence to realize the interaction between patients and medical staff, medical institutions, and medical equipment, and gradually achieve informatization. The core and key of intelligent medical care is intelligent diagnosis and treatment, that is, making computers become a brain with medical knowledge, so as to provide assistant decision-making for doctors' diagnosis and treatment. Using the knowledge graph to describe the relevant medical knowledge in the EMR electronic medical record can improve its utilization rate, promote the development of intelligent medical care, and play an important auxiliary role in providing decision support for doctors [3]. Most of the above applications based on knowledge graphs in the medical field use traditional machine learning [32,33,34,35,36,37,38,39,40]. (2) Train the thyroid diagnostic model BLSTM by using the constructed knowledge map. The classification performance based on BLSTM shows better results than other deep learning algorithms

Knowledge Graph
Disease diagnosis based on knowledge graph
Thyroid diagnostic framework
Diagnosis model of thyroid disease
Experimental data and settings
Conclusion
Full Text
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