Diagnosis Method of Thyroid Disease Combining Knowledge Graph and Deep Learning

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.

Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

“…However, while those approaches build a bipartite graph of symptoms and diseases, the medicalvalues knowledge graph uses more complex diagnosis pathways and rules (see below). Some approaches have been proposed in the recent years which cover particular subfields of medicine [14] or diseases [4]. In contrast to those works, the medicalvalues knowledge graph aims at covering a larger variety of diseases.…”

“…We use jRDF2vec to extract the walks and train the embedding vectors. 4 The generated walks are used as input for word2vec using the skip-gram model [17]. Word2vec is a neural network that estimates the probabilities of words occurring in the context of other words.…”

[Paper] Deep Reinforcement Learning-based Music Recommendation with Knowledge Graph Using Acoustic Features

“…The music-related recommendation methods can provide music content based on the relationships between a target user and users with similar tastes [9]. One of the representative music recommendation methods is the knowledge graph-based method; it consists of a semantic network that describes entities corresponding to users and contents to consider their high-order relationships [10,11]. The advantage of knowledge graph-based methods is their high explainability.…”

Medical Knowledge Graph in Chinese Using Deep Semantic Mobile Computation Based on IoT and WoT

“…In recent years, as the maturity of technology and more emphasis on healthcare, it has become more and more popular to apply knowledge graph in the medical field and has attracted much attention from researchers in computer and medical to combine these two fields [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Rotmensch et al [11] in 2017 proposed learning a health knowledge graph from electronic medical records by using probabilistic.…”

“…Li et al [18] came up constructing a knowledge graph of knee osteoarthritis in Chinese using an automatic approach in 2020. Chai et al [19] in 2020 proposed the diagnosis method of thyroid disease combining knowledge graph and deep learning. Alawad et al [20] utilize medical knowledge graph combined with a deep learning method for cancer phenotyping in 2021.…”