Surgical risk prediction method and device based on knowledge graph, equipment and medium

Abstract

The invention discloses an operation risk prediction method and device based on a knowledge graph, equipment and a medium, and the method comprises the steps: extracting to-be-processed data from a plurality of data sources, and carrying out the data preprocessing of the to-be-processed data, and obtaining processed data; obtaining a plurality of triads and constructing a surgical risk knowledge graph; on the basis of a preset term system, performing ontology alignment on the surgical risk knowledge graph; performing risk assessment updating on the plurality of triads belonging to the same relationship so as to update the surgical risk knowledge graph; and inputting the target patient features and the target operation type into the operation risk knowledge graph to obtain a target operation risk. The invention belongs to the field of operation risk prediction. According to the method, texts, numerical values and images are uniformly mapped into map nodes, and the problem of heterogeneous data integration is solved.

Description

Technical Field

[0001] The present invention relates to the field of surgical risk prediction, and in particular to a surgical risk prediction method, device, equipment and medium based on a knowledge graph. Background Art

[0002] A knowledge graph is a model used to represent information and knowledge. It organizes and connects entities and the relationships between them in the real world in a structured way. Specifically, a knowledge graph consists of a series of entities, attributes, and relationships. Each entity represents a specific concept, object, or event, while relationships describe the various connections between these entities.

[0003] At present, the prediction of surgical risk is usually carried out using neural networks. However, the prediction based on neural networks alone lacks dynamic correlation analysis of factors such as individual patient characteristics, surgical types, and postoperative complications. Therefore, the present invention proposes a surgical risk prediction method based on knowledge graphs. Summary of the invention

[0004] The present invention solves the technical problem of the lack of dynamic correlation analysis of factors such as patient individual characteristics, surgery type, and postoperative complications in the prior art by providing a surgical risk prediction method, device, equipment, and medium based on a knowledge graph, thereby achieving the technical effect of more comprehensively predicting the patient's surgical risk.

[0005] In a first aspect, the present invention provides a method for predicting surgical risks based on a knowledge graph, the method comprising: Extracting data to be processed from a number of data sources and performing data preprocessing on the data to be processed to obtain processed data; Perform entity recognition and relationship extraction on the processed data to obtain several triplets and construct a surgical risk knowledge graph. The triplets include patient characteristics, surgical type, and risk assessment. Based on the preset terminology system, the surgical risk knowledge graph is aligned with the ontology; According to the importance of the data source, multiple triples belonging to the same relationship are updated with risk assessment to update the surgical risk knowledge graph; Input the target patient characteristics and target surgery type into the surgical risk knowledge graph to obtain the target surgical risk.

[0006] Furthermore, according to the importance of the data source, multiple triples belonging to the same relationship are updated for risk assessment to update the surgical risk knowledge graph, including: According to the importance of data sources, each data source is graded, where each grade corresponds to a weight; Tracing back the data source of the triplet, and determining the first weight of the risk assessment in the triplet according to the level of the data source; Determining a second weight of risk assessment in the triplet according to a maximum value of update frequencies in the data source and an update frequency of the data source of the triplet; The risk assessment of the triplet is updated according to the first weight and the second weight of the risk assessment in the triplet and the risk assessment.

[0007] Further, based on the first weight and the second weight of the risk assessment in the triplet and the risk assessment, the risk assessment of the triplet is updated, including:

[0008]

[0009] in, For the The updated risk assessment of the triples is For the The first weight of the triples, For the The second weight of the triplet, For the Risk assessment before triple update, For the The level of each data source corresponds to the weight, The maximum level corresponding weight of the data source.

[0010] Furthermore, the data to be processed is extracted from a number of data sources and preprocessed to obtain processed data, including: Segment the unstructured text in the data to be processed based on the preset medical dictionary; Standardize the time series data in the data to be processed; Based on differential privacy technology, the data to be processed is desensitized.

[0011] Furthermore, based on the preset terminology system, the surgical risk knowledge graph is aligned with the ontology, including: Based on the terminology system of SNOMED CT, the vocabulary in the surgical risk knowledge graph is aligned ontology, and the vocabulary with concept alignment is removed; For the words that are not conceptually aligned, the edit distance is calculated with the words in the terminology system of SNOMED CT based on the fuzzy matching algorithm, and the pair of words with the shortest distance is aligned with the ontology; After the surgical risk knowledge graph is aligned with the ontology, the ontology alignment between the terms is determined to be reasonable based on the attribute relationship in the SNOMED CT terminology system. If it is unreasonable, a verification message is sent to the client, which includes the vocabulary group with unreasonable ontology alignment.

[0012] Furthermore, the target patient characteristics and target surgery type are input into the surgical risk knowledge graph to obtain the target surgical risk, including: Input the target patient characteristics and target surgery type into the surgical risk knowledge graph to obtain several surgical risk results; Based on the results of several surgical risks, a risk range is constructed and used as the target surgical risk.

[0013] Furthermore, based on the results of several surgical risks, a risk range is constructed and used as the target surgical risk, including: The minimum value of several surgical risk results is taken as the minimum value of the risk range; The maximum value of several surgical risk results is taken as the maximum value of the risk range.

[0014] In a second aspect, the present invention provides a surgical risk prediction device based on a knowledge graph, the device comprising: An extraction module is used to extract the data to be processed from several data sources and perform data preprocessing on the data to be processed to obtain processed data; The graph module is used to perform entity recognition and relationship extraction on the processed data to obtain a number of triplets and construct a surgical risk knowledge graph. The triplets include patient characteristics, surgical types, and risk assessments. An alignment module, used to align the surgical risk knowledge graph ontology based on a preset terminology system; An evaluation and updating module is used to update the risk evaluation of multiple triples belonging to the same relationship according to the importance of the data source, so as to update the surgical risk knowledge graph; The risk module is used to input the target patient characteristics and the target surgery type into the surgical risk knowledge graph to obtain the target surgical risk.

[0015] In a third aspect, the present invention provides an electronic device, comprising: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to execute to implement the surgical risk prediction method based on knowledge graph as provided in the first aspect.

[0016] In a fourth aspect, the present invention provides a non-temporary computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to implement the surgical risk prediction method based on the knowledge graph as provided in the first aspect.

[0017] One or more technical solutions provided in the present invention have at least the following technical effects or advantages: This invention maps text (medical records), numerical values ​​(vital signs), and images (surgical videos) into graph nodes to solve the problem of heterogeneous data integration. Based on the streaming computing framework, it realizes the closed loop of "data input-graph update-model optimization" and proposes a "risk transmission weight" algorithm to quantify the cascading effect between risk factors.

[0018] The present invention considers the importance of data sources to distinguish the authority and reliability of different sources, and assigns high weights to more credible data sources, thereby improving the accuracy of the overall assessment; secondly, by considering the update frequency of data sources, it ensures that risk assessment reflects the latest medical discoveries and clinical practices, enhances the information freshness of the knowledge graph, and improves its dynamic adaptability, so that it can quickly respond to emerging data and changes; in addition, combining the two to update risk assessment helps optimize resource allocation, give priority to high-quality and latest data, and avoid the impact of outdated information.

[0019] The present invention constructs a risk range through the prediction results of multiple triples, so that relevant personnel can have a more accurate expectation of surgical risks, thereby reducing doctor-patient risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A schematic diagram of the process of the surgical risk prediction method based on the knowledge graph provided by the present invention; Figure 2 A schematic diagram of the structure of a surgical risk prediction device based on a knowledge graph provided by the present invention. DETAILED DESCRIPTION

[0022] The embodiment of the present invention solves the technical problem of the lack of dynamic correlation analysis of factors such as patient individual characteristics, surgery type, and postoperative complications in the prior art by providing a surgical risk prediction method based on a knowledge graph.

[0023] The technical solution of the present invention is to solve the above technical problems, and the overall idea is as follows: A surgical risk prediction method based on a knowledge graph comprises: extracting data to be processed from a number of data sources and performing data preprocessing on the data to be processed to obtain processed data; performing entity recognition and relationship extraction on the processed data to obtain a number of triplets and constructing a surgical risk knowledge graph, wherein the triplets include patient characteristics, surgical types, and risk assessments; performing ontology alignment on the surgical risk knowledge graph based on a preset terminology system; performing risk assessment updates on multiple triplets belonging to the same relationship according to the importance of the data source to update the surgical risk knowledge graph; and inputting target patient characteristics and target surgical types into the surgical risk knowledge graph to obtain target surgical risks.

[0024] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] First of all, the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0026] The present invention provides Figure 1 The surgical risk prediction method based on knowledge graph shown includes: S11, extracting data to be processed from a plurality of data sources and performing data preprocessing on the data to be processed to obtain processed data.

[0027] Specifically, it includes: segmenting the unstructured text in the data to be processed based on the preset medical dictionary; standardizing the time series data in the data to be processed; and performing privacy desensitization on the data to be processed based on differential privacy technology.

[0028] Data sources can be electronic medical records (EMRs), PubMed documents, surgical video reports, medical device logs, and patient physiological signals.

[0029] In the medical field, data (such as medical records, doctor's diagnoses, patient descriptions, etc.) can exist in the form of unstructured text and usually need to be processed before further analysis or modeling.

[0030] The preset medical dictionary may be an enhanced medical-specific dictionary, which includes professional terms in the medical field, disease names, drug names, symptom descriptions, and other vocabulary.

[0031] Segment the unstructured text, that is, segment the continuous text into independent words or phrases. The preset medical dictionary will be referenced during the segmentation to ensure that the medical terms can be correctly identified and retained. Convert the unstructured text into a structured vocabulary set to facilitate subsequent natural language processing (NLP) tasks such as information extraction, classification, clustering, etc.

[0032] Time series data (such as temperature changes, heart rate monitoring data, medication time records, etc.) is an important part of medical data. However, different devices, systems or institutions may record this data in different formats.

[0033] Timestamps can be converted into a unified format (such as ISO 8601 standard); the values ​​of time series data can be standardized (such as Z-score standardization or Min-Max normalization) to make them have the same dimension or distribution range; if the sampling frequency of time series data from different sources is inconsistent (such as once per second or once per minute), they can be aligned through interpolation or resampling.

[0034] Standardization eliminates differences in data formats and scales, allowing time series data to be analyzed and modeled within a unified framework.

[0035] Medical data usually contains sensitive information (such as patient name, ID number, medical history, etc.), and direct use may lead to privacy leakage.

[0036] Differential privacy is a mathematically rigorous privacy protection method that conceals the true information of an individual by adding random noise to the original data while ensuring the accuracy of the overall statistical properties.

[0037] In the data preprocessing stage, differential privacy algorithms can be applied to sensitive fields (such as age, gender, diagnosis results, etc.) to add appropriate noise. Differential privacy technology preserves the statistical characteristics of the data as much as possible while protecting personal privacy, thereby supporting subsequent analysis and machine learning tasks.

[0038] S12, perform entity recognition and relationship extraction on the processed data to obtain several triplets and construct a surgical risk knowledge graph. The triplets include patient characteristics, surgical type, and risk assessment.

[0039] Pre-trained medical language models (such as BioBERT+CRF) can be used to extract entities, including: patient characteristics (age, underlying diseases, allergy history); surgical elements (operative procedures, incision types, anesthesia methods); risk factors (bleeding volume, infection indicators, complications).

[0040] After entity recognition, a "patient-surgery-risk" triplet can be constructed based on a graph neural network (GNN), for example: (patient-orthopedic surgery-venous thrombosis risk), where the risk of venous thrombosis is expressed as a percentage. Patient refers to patient data, which includes the patient's physical signs, such as body temperature, blood pressure, emotions, etc. Orthopedic surgery refers to orthopedic surgery data, which includes data such as the steps, drugs, and duration required for the surgery.

[0041] S13, based on the preset terminology system, ontology alignment of surgical risk knowledge graph is performed.

[0042] Specifically, it includes: based on the terminology system of SNOMED CT, the vocabulary in the surgical risk knowledge graph is ontologically aligned, and the vocabulary with concept alignment is eliminated; for the vocabulary that is not conceptually aligned, the edit distance is calculated with the vocabulary in the terminology system of SNOMEDCT based on the fuzzy matching algorithm, and the pair of vocabulary with the shortest distance is ontologically aligned; after the surgical risk knowledge graph completes the ontology alignment, based on the attribute relationship in the terminology system of SNOMED CT, it is determined whether the ontology alignment between the vocabulary is reasonable; if it is unreasonable, verification information is sent to the client, and the verification information includes the vocabulary group with unreasonable ontology alignment.

[0043] By comparing the vocabulary in the knowledge graph with the terminology system of SNOMED CT, semantically consistent concepts are identified; once a match is found, the vocabulary is considered to have completed ontology alignment.

[0044] For the words that are not conceptually aligned, the edit distance (or Euclidean distance) is calculated with the words in the terminology system of SNOMED CT based on the fuzzy matching algorithm, and the pair of words with the shortest distance is aligned into the ontology.

[0045] Specifically, we can use the edit distance (such as Levenshtein distance) as a measure to calculate the similarity between the unaligned vocabulary and all possible related vocabulary in the SNOMED CT terminology system. Then we select the pair of vocabulary with the highest similarity (i.e. the shortest edit distance) to pair them and try to complete the ontology alignment.

[0046] After the surgical risk knowledge graph completes the ontology alignment, the rationality of the ontology alignment between the vocabularies is determined based on the attribute relationships in the terminology system of SNOMED CT, so as to verify whether the relationship between the vocabularies that have completed the ontology alignment conforms to the logic and structure defined within SNOMED CT.

[0047] According to the attributes and relationships defined in SNOMED CT (e.g., parent-child relationships, synonyms, etc.), check whether the relationships between the vocabularies in each set of ontology alignments are reasonable.

[0048] If it is unreasonable, verification information will be sent to the client, which includes the unreasonable vocabulary groups of the ontology alignment. By transmitting the information to the client, relevant personnel can make judgments based on experience.

[0049] S14, based on the importance of the data source, risk assessment is updated for multiple triples belonging to the same relationship to update the surgical risk knowledge graph.

[0050] Specifically, the method includes: classifying each data source according to its importance, wherein each level corresponds to a weight; tracing the data source of the triplet, and determining the first weight of the risk assessment in the triplet according to the level of the data source; determining the second weight of the risk assessment in the triplet according to the maximum value of the update frequency in the data source and the update frequency of the data source of the triplet; and updating the risk assessment of the triplet according to the first weight and the second weight of the risk assessment in the triplet and the risk assessment.

[0051] Also includes: Includes:

[0052]

[0053] in, For the The updated risk assessment of the triples is For the The first weight of the triples, For the The second weight of the triplet, For the Risk assessment before triple update, For the The level of each data source corresponds to the weight, The maximum level corresponding weight of the data source.

[0054] First, the data sources are graded according to their importance. Each grade corresponds to a weight, and the weight setting of each grade can be determined based on historical experience, such as 0.9, 0.8, 0.6, 0.5, etc. from high to low.

[0055] For each triple, the data source from which it originates is traced, and the first weight of the triple is determined according to the level of the data source, where the first weight is directly equal to the weight of the level of the data source.

[0056] In addition, weighted fusion can be performed according to the importance of the data source.

[0057] Specifically, the corresponding weights of the levels of each data source can be obtained, and multiple triples belonging to the same relationship can be weighted and fused according to the corresponding weights. It can be understood that even under the same relationship, the risks may be different.

[0058] Taking the above as an example, patient - orthopedic surgery - venous thrombosis risk, the information in "patient" and "orthopedic surgery" is the same, but the "venous thrombosis risk" may be different (because the credibility of the data source is different, and the credibility is positively correlated with the importance). The results are weighted and fused, and the average is taken to obtain the fused venous thrombosis risk.

[0059] Of course, if the weighted fusion method is not adopted, the risk assessment of the triplet can also be updated by the above method provided by the present invention.

[0060] The present invention considers the importance of data sources to distinguish the authority and reliability of different sources, and assigns high weights to more credible data sources, thereby improving the accuracy of the overall assessment; secondly, by considering the update frequency of data sources, it ensures that risk assessment reflects the latest medical discoveries and clinical practices. This method not only enhances the information freshness of the knowledge graph, but also improves its dynamic adaptability, enabling it to respond quickly to emerging data and changes. In addition, combining these two to update risk assessments helps optimize resource allocation, prioritize high-quality and up-to-date data, and avoid the impact of outdated information.

[0061] S15, input the target patient characteristics and target surgery type into the surgery risk knowledge graph to obtain the target surgery risk.

[0062] Specifically, it includes: inputting target patient characteristics and target surgery type into the surgical risk knowledge graph to obtain several surgical risk results; based on the several surgical risk results, constructing a risk range and using it as the target surgical risk.

[0063] Including: constructing a risk range according to the results of several surgical risks, and taking the risk range as the target surgical risk, including: taking the minimum value of several surgical risk results as the minimum value of the risk range; taking the maximum value of several surgical risk results as the maximum value of the risk range.

[0064] It is understandable that after the target patient characteristics and the target surgery type are input, the same information may exist in multiple triplets at the same time, and each triplet corresponds to a surgical risk result. Since the surgical risk results have been updated, it can be considered that the confidence level of each triplet corresponding to a surgical risk result is high.

[0065] The present invention constructs a risk range through the prediction results of multiple triples, so that relevant personnel can have a more accurate expectation of surgical risks, thereby reducing doctor-patient risks.

[0066] In addition, the present invention adopts an attribute graph model (Neo4j) to support complex relationship queries; it can access postoperative follow-up data in real time to trigger graph updates; and it can automatically eliminate outdated knowledge based on node importance evaluation based on reinforcement learning.

[0067] You can also trace back through the graph path to generate a risk cause chain (for example: diabetes → delayed wound healing → infection risk). The risk cause chain can further explain the causes of the risk, improve the trust between patients and medical staff, and reduce medical risks.

[0068] Take laparoscopic cholecystectomy as an example: The system extracts the patient's "obesity, hypertension" entity from the medical record; Risk rules in the literature: Obesity + pneumoperitoneum pressure > 15 mmHg → 40% increased risk of deep vein thrombosis; The use of intermittent pneumatic pressure devices during surgery is recommended.

[0069] Effect: Risk assessment accuracy: AUC on the test set reached 0.92 (compared to 0.78 for the traditional logistic regression model); Supports real-time warning: When the intraoperative monitoring data is abnormal, the associated risk map is automatically pushed (such as a sudden increase in heart rate → indicating the possibility of gas embolism).

[0070] In summary, the present invention provides a method for predicting surgical risks based on a knowledge graph, the method comprising: extracting data to be processed from several data sources and preprocessing the data to be processed to obtain processed data; performing entity recognition and relationship extraction on the processed data to obtain several triples and construct a surgical risk knowledge graph, the triples including patient characteristics, surgical types and risk assessments; ontology alignment of the surgical risk knowledge graph based on a preset terminology system; updating risk assessments of multiple triples belonging to the same relationship according to the importance of the data source to update the surgical risk knowledge graph; inputting target patient characteristics and target surgical types into the surgical risk knowledge graph to obtain target surgical risks. The present invention uniformly maps text (medical records), numerical values ​​(vital signs), and images (surgical videos) into graph nodes to solve the problem of heterogeneous data integration. Based on a streaming computing framework, a closed loop of "data input-graph update-model optimization" is realized; a "risk conduction weight" algorithm is proposed to quantify the cascade effect between risk factors. The present invention considers the importance of data sources to distinguish the authority and reliability of different sources, and assigns high weights to more credible data sources, thereby improving the accuracy of the overall assessment; secondly, by considering the update frequency of data sources, it ensures that risk assessment reflects the latest medical discoveries and clinical practices, which not only enhances the information freshness of the knowledge graph, but also improves its dynamic adaptability, enabling it to quickly respond to emerging data and changes; combining these two to update risk assessment helps optimize resource allocation, prioritize high-quality and up-to-date data, and avoid the impact of outdated information. The present invention constructs a risk range through the prediction results of multiple triples, so that relevant personnel can have a more accurate expectation of surgical risks and reduce doctor-patient risks.

[0071] Based on the same inventive concept, the present invention provides Figure 2 The surgical risk prediction device based on the knowledge graph shown includes: The extraction module 21 is used to extract the data to be processed from a number of data sources and perform data preprocessing on the data to be processed to obtain processed data; A graph module 22, which is used to perform entity recognition and relationship extraction on the processed data, obtain a number of triples and construct a surgical risk knowledge graph, wherein the triples include patient characteristics, surgical type and risk assessment; An alignment module 23, used for ontology alignment of the surgical risk knowledge graph based on a preset terminology system; An evaluation and updating module 24 is used to update the risk evaluation of multiple triples belonging to the same relationship according to the importance of the data source, so as to update the surgical risk knowledge graph; The risk module 25 is used to input the target patient characteristics and the target surgery type into the surgery risk knowledge graph to obtain the target surgery risk.

[0072] Based on the same inventive concept, the present invention also provides an electronic device as shown, including: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to execute to implement the surgical risk prediction method based on the knowledge graph as provided above.

[0073] Based on the same inventive concept, the present invention also provides a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute and implement the surgical risk prediction method based on the knowledge graph as provided above.

[0074] Since the electronic device introduced in this embodiment is an electronic device used to implement the information processing method in the embodiment of the present invention, based on the information processing method introduced in the embodiment of the present invention, a person skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present invention is not described in detail here. As long as the electronic device used by a person skilled in the art to implement the information processing method in the embodiment of the present invention, it belongs to the scope of protection of the present invention.

[0075] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0076] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0079] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0080] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A surgical risk prediction method based on knowledge graph, characterized in that: The method comprises: Extracting data to be processed from a number of data sources and performing data preprocessing on the data to be processed to obtain processed data; Performing entity recognition and relationship extraction on the processed data to obtain a number of triples and construct a surgical risk knowledge graph, wherein the triples include patient characteristics, surgical type, and risk assessment; Based on a preset terminology system, ontology alignment is performed on the surgical risk knowledge graph; According to the importance of the data source, risk assessment is updated for multiple triples belonging to the same relationship to update the surgical risk knowledge graph; The target patient characteristics and the target surgery type are input into the surgery risk knowledge graph to obtain the target surgery risk.

2. The method for predicting surgical risks based on knowledge graph according to claim 1, characterized in that: According to the importance of the data source, multiple triples belonging to the same relationship are updated with risk assessment to update the surgical risk knowledge graph, including: According to the importance of data sources, each data source is graded, where each grade corresponds to a weight; Tracing back the data source of the triplet, and determining the first weight of the risk assessment in the triplet according to the level of the data source; Determining a second weight of risk assessment in the triplet according to a maximum value of update frequencies in the data source and an update frequency of the data source of the triplet; The risk assessment of the triplet is updated according to the first weight and the second weight of the risk assessment in the triplet and the risk assessment.

3. The method for predicting surgical risks based on knowledge graph according to claim 2, characterized in that: The risk assessment of the triplet is updated according to the first weight and the second weight of the risk assessment in the triplet and the risk assessment, including: in, For the The updated risk assessment of the triples is For the The first weight of the triples, For the The second weight of the triplet, For the Risk assessment before triple update, For the The level of each data source corresponds to the weight, The maximum level corresponding weight of the data source.

4. The method for predicting surgical risks based on knowledge graph according to claim 1, characterized in that: Extract the data to be processed from several data sources and perform data preprocessing on the data to obtain processed data, including: Segment the unstructured text in the data to be processed based on the preset medical dictionary; Standardize the time series data in the data to be processed; Based on differential privacy technology, the data to be processed is desensitized.

5. The method for predicting surgical risks based on knowledge graph according to claim 1, characterized in that: Based on the preset terminology system, ontology alignment is performed on the surgical risk knowledge graph, including: Based on the terminology system of SNOMED CT, the vocabulary in the surgical risk knowledge graph is aligned ontology, and the vocabulary with concept alignment is removed; For the words that are not conceptually aligned, the edit distance is calculated with the words in the terminology system of SNOMED CT based on the fuzzy matching algorithm, and the pair of words with the shortest distance is aligned with the ontology; After the surgical risk knowledge graph completes the ontology alignment, based on the attribute relationship in the terminology system of the SNOMED CT, determine whether the ontology alignment between the terms is reasonable; If it is unreasonable, a verification message is sent to the client, which includes the vocabulary group with unreasonable ontology alignment.

6. The method for predicting surgical risks based on knowledge graph according to claim 1, characterized in that: Input the target patient characteristics and the target surgery type into the surgical risk knowledge graph to obtain the target surgical risk, including: Inputting target patient characteristics and target surgery type into the surgical risk knowledge graph to obtain several surgical risk results; Based on the results of several surgical risks, a risk range is constructed and used as the target surgical risk.

7. The method for predicting surgical risks based on knowledge graph according to claim 6, characterized in that: Based on the results of several surgical risks, a risk range is constructed and used as the target surgical risk, including: The minimum value of several surgical risk results is taken as the minimum value of the risk range; The maximum value of several surgical risk results is taken as the maximum value of the risk range.

8. A surgical risk prediction device based on knowledge graph, characterized in that: The device comprises: An extraction module is used to extract the data to be processed from several data sources and perform data preprocessing on the data to be processed to obtain processed data; A graph module, used for performing entity recognition and relationship extraction on the processed data, obtaining a number of triples and constructing a surgical risk knowledge graph, wherein the triples include patient characteristics, surgical type, and risk assessment; An alignment module, used for ontology alignment of the surgical risk knowledge graph based on a preset terminology system; An evaluation and updating module, used to perform risk evaluation and update on multiple triples belonging to the same relationship according to the importance of the data source, so as to update the surgical risk knowledge graph; The risk module is used to input target patient characteristics and target surgery type into the surgery risk knowledge graph to obtain target surgery risks.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; Wherein, the processor is configured to execute to implement the surgical risk prediction method based on knowledge graph as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to implement the surgical risk prediction method based on a knowledge graph as described in any one of claims 1 to 7.

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