Multidisciplinary fusion consultation method and system based on knowledge graph

Abstract

This application discloses a multidisciplinary integrated consultation method based on a knowledge graph, which relates to the field of integrated consultation. The method includes: receiving patient medical history information and medical resource information; constructing a patient knowledge graph and matching multiple consulting doctors based on the patient knowledge graph; establishing a multidisciplinary consultation meeting to share patient medical history information in real time; collecting the diagnosis and treatment opinions of all consulting doctors; and generating a target diagnosis and treatment plan. This application can effectively solve the problems of time coordination difficulties and the inconvenience of doctor-patient gathering during multidisciplinary integrated consultation.

Claims

1. A multidisciplinary fusion consultation method based on knowledge graph, characterized by: The method comprises the following steps: Receiving patient medical record information of a target patient and medical resource information of a location where the target patient is located; Construct a patient knowledge graph based on the patient's medical record information, and divide the patient knowledge graph into several patient subgraphs based on the subgraph partitioning rules; For any patient subgraph, the subgraph entities in the patient subgraph are mapped to the pre-built medical knowledge graph, and multiple medical subgraphs are extracted from the medical knowledge graph based on the mapping results; Complete the patient subgraph based on the medical knowledge graph to obtain the standard patient subgraph; Count the number of nodes and edges in the standard patient subgraph and the entire medical subgraph, and calculate the structural similarity between the standard patient subgraph and the entire medical subgraph based on the statistical results of the number of nodes and edges; Calculate the node similarity and edge similarity between the standard patient subgraph and all medical subgraphs, and combine the node similarity and edge similarity to determine the semantic similarity between the standard patient subgraph and all medical subgraphs; The structural similarity and semantic similarity are integrated to obtain the subgraph similarity between the standard patient subgraph and the entire medical subgraph; The medical subgraphs whose subgraph similarity is greater than a preset second similarity threshold are used as associated subgraphs of the standard patient subgraph; Trace back multiple related departments based on the positions of all related subgraphs in the medical knowledge graph; Get the doctor information of all related doctors in all related departments; For any associated doctor of any associated department, pre-process the doctor information of the associated doctor; Using the keyword extraction algorithm to extract all diagnosis and treatment keywords from the pre-processed doctor information, and integrating all diagnosis and treatment keywords into a diagnosis and treatment keyword set; Vectorize the diagnosis and treatment keyword set to obtain the diagnosis and treatment key vector; The similarity formula is used to calculate the feature similarity between the diagnosis and treatment key vector and the information entity of the patient knowledge graph; If the feature similarity is greater than or equal to the preset first similarity threshold, the associated doctor will be used as the consulting doctor corresponding to the target patient; Create a multidisciplinary consultation meeting and grant meeting permissions to all consulting doctors, and share patient medical records with all consulting doctors in real time during the multidisciplinary consultation meeting; After all consulting doctors have completed the consultation based on the patient's medical records, their diagnosis and treatment opinions are collected; After integrating all diagnosis and treatment opinions, an initial diagnosis and treatment plan is generated, and the initial diagnosis and treatment plan is revised based on medical resource information to obtain the target diagnosis and treatment plan.

2. The method according to claim 1, characterized in that The construction of a patient knowledge graph based on patient medical record information includes the following steps: Pre-process patient medical record information to obtain standard medical record information; Identify all information entities in standard medical record information based on the pre-trained entity recognition model; Conduct context analysis on all information entities based on standard medical record information, and determine the entity attributes of all information entities and the association relationships between all information entities based on the context analysis results; Construct a patient knowledge graph for the target patient based on all information entities, entity attributes, and association relationships.

3. The method according to claim 1, characterized in that Sharing the patient's medical history information with all consulting doctors in real time during a multidisciplinary consultation meeting includes the following steps: Divide patient medical record information into patient text information and patient image information according to information attributes; Encrypting the patient's text information using a text encryption algorithm to obtain encrypted text information; dividing the patient image information into a plurality of patient image blocks; For any patient image block, the image compression algorithm is used to perform lossless compression of the patient image block to obtain multiple compressed image sub-bands; Share encrypted text information with all consulting physicians in real time during multidisciplinary consultations, and progressively share all compressed image subbands in real time.

4. The method according to claim 3, characterized in that The method of using an image compression algorithm to perform lossless compression of a patient image block to obtain a plurality of compressed image sub-bands comprises the following steps: Extract the true pixel value of the patient image block; Predicting pixel prediction values ​​of compressed image blocks using a preset prediction function; Combine the true pixel value and the predicted pixel value to construct a pixel difference matrix; The pixel difference matrix is ​​input into the IWT encoder, and the pixel difference matrix is ​​transformed and decomposed by the IWT encoder to obtain multiple image sub-bands; The RLE coding algorithm is used to complete the coding compression of all image sub-bands to obtain multiple compressed image sub-bands.

5. The method according to claim 3, characterized in that The progressive real-time sharing of all compressed image sub-bands comprises the following steps: Analyze the frequency characteristics of all compressed image sub-bands and determine the transmission priority of all compressed image sub-bands based on the frequency characteristics; All compressed image subbands are gradually shared to the multidisciplinary consultation conference according to transmission priority.

6. The method according to claim 1, characterized in that The steps of generating an initial diagnosis and treatment plan after integrating all diagnosis and treatment opinions, and revising the initial diagnosis and treatment plan based on medical resource information to obtain a target diagnosis and treatment plan include the following: Integrate all diagnosis and treatment opinions to generate an initial diagnosis and treatment plan; Determine resource shortage information in the target patient's location by combining the initial diagnosis and treatment plan with medical resource information; Generate resource secondment plans based on resource shortage information; Use the resource loan plan to improve the initial diagnosis and treatment plan and obtain the target diagnosis and treatment plan.

7. A machine-readable storage medium, characterized in that The machine-readable storage medium stores instructions for enabling a machine to execute the method for multidisciplinary fusion consultation based on knowledge graph according to any one of claims 1 to 6.

8. A multidisciplinary fusion consultation system based on knowledge graph, characterized by: include: a memory configured to store instructions; as well as A processor is configured to call instructions from a memory and to implement the method of multidisciplinary fusion consultation based on knowledge graph according to any one of claims 1 to 6 when executing the instructions.

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