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ObjectiveTo systematically sort out the knowledge framework and conceptual logic relationship of "disease-syndrome-treatment-prescription-medicine" in the existing literature on traditional Chinese medicine（TCM） treatment of diabetic peripheral neuropathy（DPN）， to construct of the knowledge map of TCM treatment of DPN， and to promote the explicitation of the implicit knowledge in the literature on the treatment of DPN with TCM. MethodTaking the literature of China National Knowledge Infrastructure about TCM treatment of DPN as the main data source， TCM-related concepts and entities were constructed by manual citation， and the corresponding relationships between the entities were established. Structured data were formed by processing with Python 3.7， and the knowledge graph was constructed based on Neo4j 3.5.34 graph database. ResultThe resulting knowledge graph with TCM diagnosis and treatment logic， defined 12 node labels such as prescriptions， Chinese medicines and syndrome types at the schema layer， as well as 4 types of relationships， such as inclusion， correspondence， selection and composition. It could support the query and discovery of nodes（syndrome elements， syndrome types and treatment methods）， as well as the relationship between each node. ConclusionBased on the literature data， this study constructed a knowledge map for TCM treatment of DPN， which brought together various methods of TCM treatment of DPN， including internal and external treatment. The whole chain knowledge structure of syndrome differentiation and classification for DPN treatment is formed from syndrome element analysis， syndrome type composition to treatment method selection， which can provide new ideas and methods for literature data to serve clinical and scientific research work， as well as reference for visualization of TCM literature knowledge， intellectualization of TCM knowledge services and the standardization of TCM diagnosis and treatment.
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Objective To build a knowledge graph;To visualize the knowledge structure relationships and clinical thinking in the treatment of coronary heart disease by renowned TCM doctors;To provide methodological reference for the inheritance of experience of renowned TCM doctors.Methods Medical records about treatment of coronary heart disease by renowned TCM doctors were retrieved from CNKI from the establishment of the database to 30th,Nov.2022.The characteristics of TCM diagnosis and treatment and the characteristics of the theoretical system of syndrome differentiation and treatment in TCM were analyzed.Concept types and relationships between concepts were sorted out and extracted to form a pattern layer of knowledge graph;based on the characteristics of the pattern layer,Python 3.11(PyCharm 2022.3.2)was used to write rules,and knowledge extraction and data import were implemented through the Pandas library,Openpyxl library and Py2neo library,which were stored in the graph database Neo4j-Community-5.2.0 to complete the construction of the knowledge graph.Implementing query application was realized through Cypher language.Results The data of 643 medical cases of 144 renowned TCM doctors were included,which were entered into the Neo4j graph database,forming a knowledge graph consisting of 2 744 nodes and 23 795 relationships under 8 concepts and 10 relationships,to achieve visual presentation and query application of the diagnosis and treatment process of coronary heart disease by renowned TCM doctors.Conclusion The knowledge graph can intuitively display the relationship of diseases-symptoms-syndromes-treatments-prescriptions-medicine in medical records,develop a knowledge system that is easy to retrieve,and improve the accessibility of domain knowledge,which can provide methodological reference for the inheritance of experience of renowned TCM doctors.
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ObjectiveTo construct the syndrome differentiation and treatment process in the diagnosis and treatment guideline into a visual knowledge graph using knowledge graph technology， and visualize the process from the input of clinical manifestations to the output of corresponding traditional Chinese medicine （TCM） diagnosis and prescriptions through programs， to visually display the diagnosis and treatment process as well as the data relationship for TCM practitioners. This paper facilitated the standardized and normalized TCM diagnosis and treatment of coronary heart disease， and provided technical support for the inheritance and promotion of TCM diagnosis and treatment. MethodNeo4j and py2neo were used to construct a knowledge graph based on the Guideline for Diagnosis and Treatment of Coronary Heart Disease with Stable Angina Pectoris published by China Association of Chinese Medicine Cardiovascular Disease Branch. A knowledge graph regarding the input of clinical manifestations was built through programs， visually displaying the standardized TCM diagnosis and treatment process of coronary heart disease with stable angina pectoris. ResultThe structured data were extracted from the guideline by py2neo connecting to Neo4j and imported into Neo4j to construct the knowledge graph of TCM diagnosis and treatment of coronary heart disease with stable angina pectoris， which had graph database query function. ConclusionAiming at the problems existing in the inheritance of TCM diagnosis and treatment， this paper proposed a diagnosis and treatment guideline integrating the experience of TCM experts and evidence-based evidence for coronary heart disease with stable angina pectoris， and realized the visualization process of knowledge graph based on TCM diagnosis and treatment guideline and the experience of TCM experts. It is helpful to intuitively display the whole TCM diagnosis and treatment process from symptom input to prescriptions and inherit TCM experience， providing a new paradigm for standardized and normalized TCM diagnosis and treatment.
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ObjectiveIn view of the standardization of clinical diagnosis and treatment of the acute abdomen and the inheritance of diagnosis and treatment experience of prestigious veteran traditional Chinese medicine（TCM） doctors， a diagnosis and treatment reasoning algorithm based on association rule mining under incomplete evidence（AMIE）+ random walk was proposed to provide information services and technical support for primary doctors by recommending personalized diagnosis and treatment plans based on medical records. MethodThe experience of diagnosis and treatment of acute abdomen of prestigious veteran TCM doctors and the text data of clinical diagnosis and treatment guidelines of integrated TCM and western medicine were collected to complete the task of knowledge extraction and construct acute abdomen knowledge graph based on Neo4j. On the basis of ontology-supported rule-based reasoning， the rule reasoning based on similar syndromes was used to expand the syndrome combinations whose Jaccard similarity was greater than the threshold in the syndrome recommendation results. The semantic path coverage algorithm was used to calculate the semantic similarity between the symptom nodes. The symptom nodes were divided into 10 categories， and the symptom nodes in the same category were extended. The random walk algorithm was used to search the symptom nodes connected with the syndrome， and the connection rules between the syndrome and symptom nodes were extended to realize the knowledge reasoning of AMIE+ random walk. ResultThe acute abdomen knowledge graph included 1 320 nodes and 2 464 relationships. According to the link prediction evaluation index of knowledge reasoning， the reasoning results of the three algorithms in the auxiliary diagnosis and treatment of acute abdomen were compared. The AMIE+ random walk algorithm complemented the knowledge graph by extending the similar syndrome connection rules and the syndrome-symptom connection rules. Compared with the knowledge reasoning algorithm based on ontology rules， the area under the curve （AUC） was 15.18% higher and the accuracy was 30.36% higher， which achieved more accurate and effective knowledge inference. ConclusionThis study used knowledge graph technology to visualize the diagnosis and treatment of acute abdomen with TCM and western medicine， assisting primary clinicians in intuitively viewing the diagnosis and treatment process and data relationship. The proposed diagnosis and treatment reasoning algorithm can realize the personalized diagnosis and treatment plan recommendation at the level of "disease-syndrome-diagnosis-treatment-prescription"， which can assist primary doctors in disease diagnosis and treatment and clinical decision-making， contribute to the knowledge sharing and application of diagnosis and treatment experience and clinical guidelines of prestigious veteran TCM doctors， improve the level of primary clinical diagnosis and treatment， and promote the normalization and standardization of the diagnosis and treatment process of acute abdomen with integrated TCM and western medicine.
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Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.
Subject(s)
Biology , Data MiningABSTRACT
Research of co-authorship network can reveal the scientific research collaboration network and can thus help us to have an understanding of it. Graphic database and Neo4J were described in detail due to the limitations of relationship database in processing the data of co-authorship network. Graphic database Neo4J-based research and practice of co-authorship network were analyzed with the Institutional Knowledge System of AMMS that we were involved in its construction as an example, and the advantages of Neo4J-based co-authorship network were summarized.