CN113505236B - Medical knowledge graph construction method, device, equipment and computer readable medium - Google Patents
Medical knowledge graph construction method, device, equipment and computer readable medium Download PDFInfo
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Abstract
The application provides a method, a device, equipment and a computer readable medium for constructing a medical knowledge graph. Wherein the method comprises the following steps: performing sensitive information elimination operation on original case data acquired from a case database or the Internet to obtain case source data from which sensitive information is eliminated; screening the human body structure data, the human body function data and the clinical presentation data of the cases from the case source data from which the sensitive information is eliminated according to the basic medical knowledge base; screening human body structure inspection technical data and human body function inspection technical data of cases from case source data from which sensitive information is eliminated according to a clinical medical knowledge base: the human body structure data and the human body structure checking technical data are corresponding, the human body function data and the human body function checking technical data are corresponding, and the corresponding result of the human body structure, the corresponding result of the human body function and the clinical manifestation data are organized according to a causal logic chain to construct a medical knowledge graph.
Description
Technical Field
The embodiment of the application relates to the technical field of medical treatment, in particular to a method and a device for constructing a medical knowledge graph, electronic equipment and a computer readable medium.
Background
Knowledge Graph (knowledgegraph) is essentially a Chinese network, nodes of which represent entities (entities), and links represent various semantic relationships (relationships) between the entities, so that scattered Knowledge can be connected with each other, thereby forming a huge and networked Knowledge system constructed by taking a semantic network as a framework. With more and more semantic web data being opened on the internet, various internet search engine companies at home and abroad begin to construct knowledge maps based on the semantic web data so as to improve service quality, such as Google knowledge maps (GoogleKnowledge Graph), hundred degrees 'awareness' and the like. Knowledge graph construction in the medical field is a current big research hotspot. Electronic medical record (Electronic MedicalRecords, EMRs) refers to digitized information generated by medical personnel during a medical activity using an Electronic medical system. Compared with a great deal of research in the field of foreign English electronic medical records, the research work of Chinese electronic medical records in China is still in a starting stage. Chinese electronic medical records are a valuable Chinese medical resource containing a great deal of valuable medical knowledge and patient health information, but at the same time Chinese electronic medical records are unstructured information, which creates a barrier to medical research thereabove. The medical knowledge graph stores, manages, transmits and reproduces medical knowledge in medical records in a structured manner, can help to establish a clinical auxiliary decision-making system, a personalized health model, intelligent medical questions and answers and the like, and has important significance for promoting the development of intelligent medical treatment.
Therefore, how to effectively construct the medical knowledge graph becomes a technical problem to be solved currently.
Disclosure of Invention
An embodiment of the application aims to provide a method, a device, electronic equipment and a computer readable medium for constructing a medical knowledge graph, which are used for solving the technical problem of how to effectively construct the medical knowledge graph in the prior art.
According to a first aspect of embodiments of the present application, a method for constructing a medical knowledge graph is provided. The method comprises the following steps: performing sensitive information elimination operation on original case data acquired from a case database or the Internet to obtain case source data from which sensitive information is eliminated; screening the human body structure data, the human body function data and the clinical presentation data of the cases from the case source data with the sensitive information eliminated according to a pre-configured basic medical knowledge base; screening out the human body structure checking technical data and human body function checking technical data of the case from the case source data of which the sensitive information is eliminated according to a pre-configured clinical medical knowledge base: and organizing the corresponding results of the human body structure data and the human body structure inspection technical data, the corresponding results of the human body function data and the human body function inspection technical data and the clinical presentation data according to a pre-configured causal logic chain to construct a medical knowledge graph.
Optionally, the operation of eliminating sensitive information of the original case data acquired from the case database or the internet includes: and carrying out sensitive information elimination operation on the original case data acquired from a case database or the Internet through a sensitive information elimination model so as to obtain case source data of the eliminated sensitive information.
Optionally, the screening the human body structure data, the human body function data and the clinical manifestation data of the cases from the case source data with the sensitive information eliminated according to the pre-configured basic medical knowledge base includes: comparing the case source data with human body structure data in a human body structure knowledge sub-base included in the basic medical knowledge base, and if the case source data and the human body structure data are the same, determining that the human body structure data screened from the case source data are the human body structure data in the human body structure knowledge sub-base; comparing the case source data with human body function data in a human body function knowledge sub-base included in the basic medical knowledge base, and if the case source data and the human body function data are the same, determining that the human body function data screened from the case source data are the human body function data in the human body function knowledge sub-base; and comparing the case source data with clinical manifestation data in a clinical manifestation sub-library included in the basic medical knowledge base, and if the case source data and the clinical manifestation data are the same, determining that the clinical manifestation data screened from the case source data are the clinical manifestation data in the clinical manifestation sub-library.
Optionally, the screening the human body structure checking technical data and human body function checking technical data of the case from the case source data of the removed sensitive information according to a pre-configured clinical medical knowledge base includes: comparing the case source data with human body structure inspection technology data in a human body structure inspection technology sub-library included in the clinical medical knowledge base, and if the case source data and the human body structure inspection technology data are the same, determining the human body structure inspection technology data screened from the case source data as the human body structure inspection technology data in the human body structure inspection technology sub-library; comparing the case source data with human body function inspection technology data in a human body function inspection technology sub-library included in the clinical medical knowledge base, and if the case source data and the human body function inspection technology data are the same, determining that the human body function inspection technology data screened from the case source data are the human body function inspection technology data in the human body function inspection technology sub-library.
Optionally, after constructing the medical knowledge-graph, the method further comprises: receiving clinical performance data of a patient; deriving human body structure data, human body structure inspection technical data, human body function data, and human body function inspection technical data related to clinical manifestation data of the patient according to the medical knowledge graph; determining clinical exam items of the patient based on the human body structure data, the human body structure exam technique data, the human body function data, and the human body function exam technique data associated with the clinical manifestation data of the patient.
Optionally, the method further comprises: if the abnormal human body structure of the patient is determined according to the examination result of the clinical examination item of the patient, the human body structure treatment technical data of the patient is determined according to the abnormal human body structure data of the patient and the causal logic chain.
Optionally, the method further comprises: if the abnormal human body function of the patient is determined according to the examination result of the clinical examination item of the patient, the human body function treatment technical data of the patient is determined according to the abnormal human body function data of the patient and the causal logic chain.
According to a second aspect of embodiments of the present application, a medical knowledge graph construction apparatus is provided. The device comprises: the elimination module is used for carrying out elimination operation of sensitive information on the original case data acquired from the case database or the Internet so as to obtain case source data of which the sensitive information is eliminated; the first screening module is used for screening the human body structure data, the human body function data and the clinical presentation data of the cases from the case source data with the sensitive information eliminated according to a pre-configured basic medical knowledge base; the second screening module is used for screening the human body structure inspection technical data and the human body function inspection technical data of the cases from the case source data of which the sensitive information is eliminated according to a pre-configured clinical medical knowledge base; the knowledge graph construction module is used for corresponding the human body structure data to the human body structure inspection technology data, corresponding the human body function data to the human body function inspection technology data, and organizing the corresponding results of the human body structure data and the human body structure inspection technology data, the corresponding results of the human body function data and the human body function inspection technology data and the clinical presentation data according to a pre-configured causal logic chain so as to construct a medical knowledge graph.
According to a third aspect of embodiments of the present application, there is provided an electronic device, including: one or more processors; a storage device configured to store one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method for constructing a medical knowledge graph according to the first aspect of the embodiments of the present application.
According to a fourth aspect of embodiments of the present application, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of constructing a medical knowledge graph according to the first aspect of embodiments of the present application.
According to the construction scheme of the medical knowledge graph provided by the embodiment of the application, sensitive information elimination operation is carried out on original case data acquired from a case database or the Internet so as to obtain case source data with the sensitive information eliminated; screening the human body structure data, the human body function data and the clinical presentation data of the cases from the case source data with the sensitive information eliminated according to a pre-configured basic medical knowledge base; screening out the human body structure checking technical data and human body function checking technical data of the case from the case source data of which the sensitive information is eliminated according to a pre-configured clinical medical knowledge base: the human body structure data and the human body structure checking technical data are corresponding, the human body function data and the human body function checking technical data are corresponding, and according to a pre-configured causal logic chain, the corresponding result of the human body structure data and the human body structure checking technical data, the corresponding result of the human body function data and the human body function checking technical data and the clinical presentation data are organized, so that a medical knowledge graph can be effectively constructed.
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Some specific embodiments of the present application will be described in detail below by way of example and not by way of limitation with reference to the accompanying drawings. The same reference numbers will be used throughout the drawings to refer to the same or like parts or portions. It will be appreciated by those skilled in the art that the drawings are not necessarily drawn to scale. In the accompanying drawings:
fig. 1 is a step flowchart of a method for constructing a medical knowledge graph according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a device for constructing a medical knowledge graph according to a second embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device in a third embodiment of the present application;
fig. 4 is a hardware structure of an electronic device in a fourth embodiment of the present application.
Detailed Description
In order to better understand the technical solutions in the embodiments of the present application, the following descriptions will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the embodiments of the present application shall fall within the scope of protection of the embodiments of the present application.
Referring to fig. 1, a flowchart of steps of a method for constructing a medical knowledge graph according to an embodiment of the present application is shown.
Specifically, the method for constructing the medical knowledge graph provided by the embodiment includes the following steps:
in step S101, a cancellation operation of sensitive information is performed on raw case data acquired from a case database or the internet to obtain case source data from which the sensitive information has been cancelled.
In this embodiment, the sensitive information may be identified for the original case data, and the sensitive information may be removed therefrom, or data other than the sensitive information may be directly extracted from the original case data as case source data. The sensitive information mainly comprises the name, home address, identification card number, telephone number and the like of the patient. The raw case data may be understood as raw data describing a medical record of a patient, and the case source data may be understood as raw case data from which sensitive information has been eliminated.
In some alternative embodiments, when the sensitive information elimination operation is performed on the original case data acquired from the case database or the internet, the sensitive information elimination operation is performed on the original case data acquired from the case database or the internet through a sensitive information elimination model, so as to obtain case source data of the eliminated sensitive information. By doing so, the sensitive information elimination operation is performed on the original case data acquired from the case database or the internet through the sensitive information elimination model, so that the sensitive information in the original case data acquired from the case database or the internet can be effectively eliminated.
In a specific example, the sensitive information cancellation model may be any suitable neural network model that may enable feature extraction or target object detection, including but not limited to convolutional neural networks, reinforcement learning neural networks, generation networks in antagonistic neural networks, and the like. The specific structure of the neural network can be set by those skilled in the art according to practical requirements, such as the number of layers of the convolution layer, the size of the convolution kernel, the number of channels, and the like. When the sensitive information elimination model is trained, the sensitive information elimination model can be trained based on sensitive information elimination marks in the original case data samples through a back propagation algorithm or a random gradient descent algorithm.
In step S102, the case body structure data, the body function data and the clinical manifestation data are screened out from the case source data from which the sensitive information has been eliminated according to the pre-configured basic medical knowledge base.
In this embodiment, the preconfigured underlying medical knowledge base may be understood as a preconfigured database for storing underlying medical knowledge. The basic medical knowledge includes a series of basic medical knowledge from a general structure to a microstructure, such as anatomy, pathology, histology, cytology, and molecular biology, and a series of basic medical knowledge about functions, such as physiology, biochemistry, pharmacophysiology, cytophysiology, and molecular biology, from a general function to a microstructure. The human body structure data may be understood as data describing a human body structure of a case, the human body function data may be understood as data describing a human body function of the case, and the clinical manifestation data may be understood as data describing a clinical manifestation of the case.
In some optional embodiments, when screening case body structure data, body function data and clinical presentation data from the case source data from which the sensitive information has been eliminated according to a pre-configured basic medical knowledge base, comparing the case source data with body structure data in a body structure knowledge sub-base included in the basic medical knowledge base, and if the case source data and the body structure data are the same, determining that the body structure data screened from the case source data are the body structure data in the body structure knowledge sub-base; comparing the case source data with human body function data in a human body function knowledge sub-base included in the basic medical knowledge base, and if the case source data and the human body function data are the same, determining that the human body function data screened from the case source data are the human body function data in the human body function knowledge sub-base; and comparing the case source data with clinical manifestation data in a clinical manifestation sub-library included in the basic medical knowledge base, and if the case source data and the clinical manifestation data are the same, determining that the clinical manifestation data screened from the case source data are the clinical manifestation data in the clinical manifestation sub-library. The human body structure knowledge sub-base can be understood as a sub-database for storing human body structure knowledge, the human body function knowledge sub-base can be understood as a sub-database for storing human body function knowledge, and the clinical manifestation sub-base can be understood as a sub-database for storing clinical manifestation knowledge. Thus, the human body structure data, the human body function data and the clinical manifestation data of the case can be effectively screened from the case source data of which the sensitive information is eliminated.
In step S103, the human body structural inspection technical data and the human body functional inspection technical data of the case are screened out from the case source data from which the sensitive information has been eliminated according to a pre-configured clinical medical knowledge base.
In this embodiment, the preconfigured clinical medical knowledge base may be understood as a preconfigured database for storing clinical medical knowledge. In order to determine whether there is a pathological change in the structure and function of the human body, qualitative and quantitative measurements of the structure and function of the body are required, and the techniques and indexes of these measurements belong to clinical medical knowledge. Techniques and indices of measurement include, from gross to fine measurement, e.g., blood pressure, heart rate, electrocardiogram, renal function, echocardiography, gene mutation site screening, etc. The human body structural inspection technology data may be understood as data for describing a human body structural inspection technology of the case, and the human body functional inspection technology data may be understood as data for describing a human body functional inspection technology of the case.
In some optional embodiments, when the case source data from which the sensitive information has been removed is screened out of the case source data of the case and the human body function inspection technology data according to a pre-configured clinical medical knowledge base, comparing the case source data with the human body structure inspection technology data in a human body structure inspection technology sub-base included in the clinical medical knowledge base, and if the case source data and the human body structure inspection technology data are the same, determining that the human body structure inspection technology data screened out of the case source data is the human body structure inspection technology data in the human body structure inspection technology sub-base; comparing the case source data with human body function inspection technology data in a human body function inspection technology sub-library included in the clinical medical knowledge base, and if the case source data and the human body function inspection technology data are the same, determining that the human body function inspection technology data screened from the case source data are the human body function inspection technology data in the human body function inspection technology sub-library. The human body structure inspection technology sub-library is understood to be a sub-database for storing human body structure inspection technology knowledge, and the human body function inspection technology sub-library is understood to be a sub-database for storing human body function inspection technology knowledge. Thereby, the human body structure inspection technical data and the human body function inspection technical data of the case can be effectively screened from the case source data of which the sensitive information is eliminated.
In step S104, the human body structure data corresponds to the human body structure inspection technology data, the human body function data corresponds to the human body function inspection technology data, and the corresponding result of the human body structure data and the human body structure inspection technology data, the corresponding result of the human body function data and the human body function inspection technology data, and the clinical presentation data are organized according to a pre-configured causal logic chain to construct a medical knowledge graph.
In this embodiment, the human body structure data may be corresponding to the human body structure inspection technology data through a human body structure keyword. The human body function data can be corresponding to the human body function inspection technology data through human body function keywords. The preconfigured cause and effect logic chain may be understood as a preconfigured logic chain for characterizing cause and effect logic.
In a specific example, the human body structure knowledge and the human body structure checking technology are integrated, the human body function knowledge and the human body function checking technology are integrated, and the human body structure, the human body function and the human body state are organized according to the causal logic of' human body structure, human body function execution, human body state, namely the medical knowledge graph of the following table:
The specific expression form of the medical knowledge graph can be, but is not limited to, a clinical case library, which finds out human body structure data, human body structure inspection technical data, human body function inspection technical data and clinical expression data in the cases according to the desensitized clinical cases through the steps described in the patent, and establishes a database containing various contents in the table according to the causal logic of 'human body structure → (execution) human body function → (embodiment) physical state'.
In a specific example, the representation of the medical knowledge graph may include, but is not limited to, a clinical case database. Specifically, the human body structural etiology or the human body functional etiology of the medical knowledge graph can be the diagnosis etiology of the clinical case database, the physical state of the medical knowledge graph can be the clinical manifestation of the clinical case database, and the causal logic 'human body constitution → human body function → physical state' of the medical knowledge graph can be the path of the etiology-clinical manifestation of the clinical case database.
For example, case 1:
■ Clinical manifestations: chest distress and chest pain of patient for 1 month
■ Diagnosis of etiology: viral myocarditis
■ Etiology-route of clinical manifestations: virus blood entering, myocardial infection, left ventricular myocardial ischemia and hypoxia, left ventricular myocardial injury (chest pain), left ventricular contraction weakening, pulmonary vein reflux disorder, pulmonary congestion (chest distress)
Case 2:
■ Clinical manifestations: intermittent chest pain is half a year and aggravated for 15 hours again
■ Diagnosis of etiology: coronary heart disease and myocardial infarction
■ Etiology-route of clinical manifestations: coronary atherosclerosis, vascular stenosis, myocardial ischemia, myocardial infarction (chest pain), ventricular motion weakening, compensatory heart rate acceleration, myocardial stimulation, gastrointestinal tract stimulation caused by reflex vagus nerve, nausea and vomiting
Case 3:
■ Clinical manifestations: intermittent chest distress for 6 days and aggravation for 1 day
■ Diagnosis of etiology: coronary heart disease and myocardial infarction
■ Etiology-route of clinical manifestations: coronary atherosclerosis, extensive stenosis of coronary arteries, myocardial ischemia, acute right ventricular myocardial infarction, chest distress and arrhythmia, mitral valve and tricuspid valve insufficiency, heart failure, left and right atrial pressure rise, pulmonary congestion (lung damp-sound), and systemic congestion (hydrothorax and slightly filling jugular vein)
In the course of hospital treatment, the same disease is frequently encountered, A expert is a set of talking, B expert is a set of talking, C expert has another talking, and the patient is helped to be out of the best. The symptom of this problem is that the knowledge of the same disease is not profound and is not interpreted by authority, resulting in a confusing state of diagnosis and treatment. Assuming that the etiology of a disease is 0, there are many different clinical manifestations such as A manifestation, B manifestation, C manifestation, D manifestation, there are different paths from etiology 0 to clinical manifestation such as 0-A,0-B,0-C,0-D, if each step in the path from etiology to clinical manifestation is analyzed, a clinical case database is built, so that it is known which paths from etiology 0 to clinical manifestation are present, and what cause a manifestation is, what cause B manifestation is, and what cause C manifestation is, so that there is a comprehensive, authoritative interpretation of disease 0. It is assumed that the clinical manifestations of different patients are a, but their etiologies are a and b different diseases, respectively. Only in a knowledgeable anatomical analysis of the etiology-clinical presentation pathways A and B A can the differences be clarified and explained. The more cases, the more detailed and rich the etiology-clinical manifestation route. Therefore, the constructed clinical case database has great practical significance.
In some alternative embodiments, after constructing the medical knowledge-graph, the method further comprises: receiving clinical performance data of a patient; deriving human body structure data, human body structure inspection technical data, human body function data, and human body function inspection technical data related to clinical manifestation data of the patient according to the medical knowledge graph; determining clinical exam items of the patient based on the human body structure data, the human body structure exam technique data, the human body function data, and the human body function exam technique data associated with the clinical manifestation data of the patient. Thereby, by the human body structure data, the human body structure examination technology data, the human body function data, and the human body function examination technology data related to the clinical manifestation data of the patient, the clinical examination item of the patient can be accurately determined.
In general, the application of the knowledge graph is based on the causal logic of 'human body structure → human body function → physical state', which human body structure is related to the symptom and the sign, which human body function is related to the symptom and the sign, and which corresponding human body structure checking technology and human body function checking technology are related to the symptom and the sign, so that the necessary clinical checking project can be determined.
In some alternative embodiments, the method further comprises: if the abnormal human body structure of the patient is determined according to the examination result of the clinical examination item of the patient, the human body structure treatment technical data of the patient is determined according to the abnormal human body structure data of the patient and the causal logic chain. Thereby, the patient's anatomy treatment technical data can be accurately determined from the patient's abnormal anatomy data and the causal logic chain.
In some alternative embodiments, the method further comprises: if the abnormal human body function of the patient is determined according to the examination result of the clinical examination item of the patient, the human body function treatment technical data of the patient is determined according to the abnormal human body function data of the patient and the causal logic chain. Thus, the human body function treatment technical data of the patient can be accurately determined according to the abnormal human body function data of the patient and the causal logic chain.
In a specific example, after each examination result of the clinical examination item of the patient comes out, it can be explained according to the examination result which human body structure and which human body function are abnormal, then the abnormal human body structure and abnormal human body function found are connected in series according to the causal logic of human body structure, human body function and physical state, so as to explain the illness state, and thus the diagnosis and the treatment strategy are definitely determined.
In a specific example, as shown in the following table, the composition of the medical knowledge graph includes: (1) etiology, (2) clinical manifestation-etiology route: pathophysiological logic chain, (3) therapeutic strategy.
In a specific single clinical case database example, chronic Obstructive Pulmonary Disease (COPD) is a chronic disease characterized by progressive sustained expiratory airflow limitation, which is the leading cause of death worldwide and is the leading disease burden, with a high incidence rate of disease disability rate up to 13.6% over 40 years old in our country, a heavy medical burden, and a large impact on patient life, resulting in a huge economic and social burden. By analyzing large sample COPD cases, summarizing and summarizing the causes, pathological changes, pathophysiology, clinical manifestations of COPD and the association between the causes, pathological changes, pathophysiology and clinical manifestations, and establishing a COPD clinical case database (clinical case library), an important data foundation is laid for COPD pathogenesis, laboratory diagnosis and drug research and development. In addition, the COPD clinical data is managed in a form of COPD clinical case library sharing platform, so that the COPD clinical case data information integration and standardization management are facilitated, the data resource management and use efficiency is improved, the COPD clinical case library sharing platform is beneficial to clinical teaching, clinical research and academic communication, the cognition of the onset and development of COPD is deepened, and the subject progress is promoted. When the clinical case database of COPD is established, the method for constructing the medical knowledge graph provided by the embodiment can be used for establishing the clinical case database of COPD.
In particular, the manifestation of the medical knowledge graph may include, but is not limited to, a COPD clinical case database. The human body structural lesion or the human body functional lesion of the medical knowledge graph can be pathological change or pathophysiology of a clinical case database of COPD, the physical state of the medical knowledge graph can be clinical manifestation of the clinical case database of COPD, and the causal logic ' human body constitution → human body function → physical condition ' of the medical knowledge graph can be causal logic chain ' pathological change → pathophysiology → clinical manifestation of the clinical case database of COPD. The causal logic chain of pathological change, pathophysiology and clinical manifestation is an objective rule of occurrence and development of diseases and is also a theoretical basis for doctors to judge the conditions of the diseases and diagnose and treat the diseases. However, in different cases, the same etiology (pathological changes) may have different states of disease (pathophysiology) development and clinical manifestations; different etiologies (pathological changes) may have the same disease (pathophysiological) state and clinical manifestations. The embodiment is based on a large number of clinical cases, performs case analysis by a clinical thinking method, finds out the logic association of pathological change, pathophysiology and clinical manifestation of each case, summarizes different pathophysiology and clinical manifestations of the same pathological change, and the same pathophysiology and clinical manifestation of different pathological changes, and deeply discusses the reasons thereof to form a clinical case library. The innovation is that: the first establishes a clinical case database by taking a causal logic chain of pathological change, pathophysiology and clinical manifestation in clinical cases as a main line; the method realizes a clinical case database structured by clinical unstructured data for the first time, and establishes a foundation for large-scale data analysis; a first clinical case database of respiratory single disease COPD; the first to share a clinical case database.
By the medical knowledge graph construction method provided by the embodiment of the application, sensitive information elimination operation is carried out on original case data acquired from a case database or the Internet so as to obtain case source data with the sensitive information eliminated; screening the human body structure data, the human body function data and the clinical presentation data of the cases from the case source data with the sensitive information eliminated according to a pre-configured basic medical knowledge base; screening out the human body structure checking technical data and human body function checking technical data of the case from the case source data of which the sensitive information is eliminated according to a pre-configured clinical medical knowledge base: the human body structure data and the human body structure checking technical data are corresponding, the human body function data and the human body function checking technical data are corresponding, and according to a pre-configured causal logic chain, the corresponding result of the human body structure data and the human body structure checking technical data, the corresponding result of the human body function data and the human body function checking technical data and the clinical presentation data are organized, so that a medical knowledge graph can be effectively constructed.
The method for constructing a medical knowledge graph provided in this embodiment may be performed by any suitable device having data processing capability, including but not limited to: cameras, terminals, mobile terminals, PCs, servers, vehicle-mounted devices, entertainment devices, advertising devices, personal Digital Assistants (PDAs), tablet computers, notebook computers, palm game consoles, smart glasses, smart watches, wearable devices, virtual display devices or display enhancement devices, and the like.
Referring to fig. 2, a schematic structural diagram of a device for constructing a medical knowledge graph according to a second embodiment of the present application is shown.
The medical knowledge graph construction device provided in this embodiment includes: a cancellation module 201, configured to perform a cancellation operation of sensitive information on original case data acquired from a case database or the internet, so as to obtain case source data from which the sensitive information has been cancelled; a first screening module 202, configured to screen out human body structure data, human body function data and clinical presentation data of the cases from the case source data from which the sensitive information has been eliminated according to a pre-configured basic medical knowledge base; a second screening module 203, configured to screen out the human body structure inspection technical data and the human body function inspection technical data of the case from the case source data with the sensitive information removed according to a pre-configured clinical medical knowledge base; the knowledge graph construction module 204 is configured to organize the human body structure data and the human body structure inspection technology data, the human body function data and the human body function inspection technology data, and the corresponding result of the human body structure data and the human body structure inspection technology data, the corresponding result of the human body function data and the human body function inspection technology data, and the clinical presentation data according to a pre-configured causal logic chain to construct a medical knowledge graph.
Optionally, the cancellation module 201 is specifically configured to: and carrying out sensitive information elimination operation on the original case data acquired from a case database or the Internet through a sensitive information elimination model so as to obtain case source data of the eliminated sensitive information.
Optionally, the first screening module 202 is specifically configured to: comparing the case source data with human body structure data in a human body structure knowledge sub-base included in the basic medical knowledge base, and if the case source data and the human body structure data are the same, determining that the human body structure data screened from the case source data are the human body structure data in the human body structure knowledge sub-base; comparing the case source data with human body function data in a human body function knowledge sub-base included in the basic medical knowledge base, and if the case source data and the human body function data are the same, determining that the human body function data screened from the case source data are the human body function data in the human body function knowledge sub-base; and comparing the case source data with clinical manifestation data in a clinical manifestation sub-library included in the basic medical knowledge base, and if the case source data and the clinical manifestation data are the same, determining that the clinical manifestation data screened from the case source data are the clinical manifestation data in the clinical manifestation sub-library.
Optionally, the second screening module 203 is specifically configured to: comparing the case source data with human body structure inspection technology data in a human body structure inspection technology sub-library included in the clinical medical knowledge base, and if the case source data and the human body structure inspection technology data are the same, determining the human body structure inspection technology data screened from the case source data as the human body structure inspection technology data in the human body structure inspection technology sub-library; comparing the case source data with human body function inspection technology data in a human body function inspection technology sub-library included in the clinical medical knowledge base, and if the case source data and the human body function inspection technology data are the same, determining that the human body function inspection technology data screened from the case source data are the human body function inspection technology data in the human body function inspection technology sub-library.
Optionally, the apparatus further comprises: a first determination module for receiving clinical performance data of the patient after constructing the medical knowledge graph; deriving human body structure data, human body structure inspection technical data, human body function data, and human body function inspection technical data related to clinical manifestation data of the patient according to the medical knowledge graph; determining clinical exam items of the patient based on the human body structure data, the human body structure exam technique data, the human body function data, and the human body function exam technique data associated with the clinical manifestation data of the patient.
Optionally, the apparatus further comprises: and the second determining module is used for determining the human body structure treatment technical data of the patient according to the abnormal human body structure data of the patient and the causal logic chain if the human body structure of the patient is determined to be abnormal according to the examination result of the clinical examination item of the patient.
Optionally, the apparatus further comprises: and the third determining module is used for determining the human body function treatment technical data of the patient according to the abnormal human body function data of the patient and the causal logic chain if the human body function abnormality of the patient is determined according to the examination result of the clinical examination item of the patient.
The device for constructing the medical knowledge graph in the embodiment is used for realizing the method for constructing the corresponding medical knowledge graph in the method embodiments, and has the beneficial effects of the corresponding method embodiments, and is not described herein.
Fig. 3 is a schematic structural diagram of an electronic device in a third embodiment of the present application; the electronic device may include:
one or more processors 301;
the computer readable medium 302, may be configured to store one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method for constructing a medical knowledge graph as described in the first embodiment.
Fig. 4 is a hardware structure of an electronic device in a fourth embodiment of the present application; as shown in fig. 4, the hardware structure of the electronic device may include: a processor 401, a communication interface 402, a computer readable medium 403 and a communication bus 404;
wherein the processor 401, the communication interface 402, and the computer readable medium 403 perform communication with each other through the communication bus 404;
alternatively, the communication interface 402 may be an interface of a communication module, such as an interface of a GSM module;
wherein the processor 401 may be specifically configured to: performing sensitive information elimination operation on original case data acquired from a case database or the Internet to obtain case source data from which sensitive information is eliminated; screening the human body structure data, the human body function data and the clinical presentation data of the cases from the case source data with the sensitive information eliminated according to a pre-configured basic medical knowledge base; screening out the human body structure checking technical data and human body function checking technical data of the case from the case source data of which the sensitive information is eliminated according to a pre-configured clinical medical knowledge base: and organizing the corresponding results of the human body structure data and the human body structure inspection technical data, the corresponding results of the human body function data and the human body function inspection technical data and the clinical presentation data according to a pre-configured causal logic chain to construct a medical knowledge graph.
The processor 401 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), and the like; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The computer readable medium 403 may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code configured to perform the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU). It should be noted that, the computer readable medium described in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage media element, a magnetic storage media element, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code configured to carry out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computers may be connected via any kind of network: including a Local Area Network (LAN) or a Wide Area Network (WAN), to connect to the user's computer, or may be connected to external computers (e.g., by way of the internet using an internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions configured to implement the specified logical function(s). The specific relationships in the embodiments described above are merely exemplary, and fewer, more, or an adjusted order of execution of the steps may be possible in a specific implementation. That is, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments described in the present application may be implemented by software, or may be implemented by hardware. The described modules may also be provided in a processor, for example, as: a processor includes an elimination module, a first screening module, a second screening module, and a knowledge-graph construction module. The names of these modules do not constitute a limitation on the module itself in some cases, and for example, the elimination module may also be described as "a module that performs an elimination operation of sensitive information on original case data acquired from a case database or the internet to obtain case source data from which the sensitive information has been eliminated".
As another aspect, the present application also provides a computer-readable medium having stored thereon a computer program which, when executed by a processor, implements the method of constructing a medical knowledge graph as described in the above embodiment one.
As another aspect, the present application also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: performing sensitive information elimination operation on original case data acquired from a case database or the Internet to obtain case source data from which sensitive information is eliminated; screening the human body structure data, the human body function data and the clinical presentation data of the cases from the case source data with the sensitive information eliminated according to a pre-configured basic medical knowledge base; screening out the human body structure checking technical data and human body function checking technical data of the case from the case source data of which the sensitive information is eliminated according to a pre-configured clinical medical knowledge base: and organizing the corresponding results of the human body structure data and the human body structure inspection technical data, the corresponding results of the human body function data and the human body function inspection technical data and the clinical presentation data according to a pre-configured causal logic chain to construct a medical knowledge graph.
The terms "first," "second," "the first," or "the second," as used in various embodiments of the present disclosure, may modify various components without regard to order and/or importance, but these terms do not limit the corresponding components. The above description is only configured for the purpose of distinguishing an element from other elements. For example, the first user device and the second user device represent different user devices, although both are user devices. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure.
When an element (e.g., a first element) is referred to as being "coupled" (operatively or communicatively) to "another element (e.g., a second element) or" connected "to another element (e.g., a second element), it is understood that the one element is directly connected to the other element or the one element is indirectly connected to the other element via yet another element (e.g., a third element). In contrast, it will be understood that when an element (e.g., a first element) is referred to as being "directly connected" or "directly coupled" to another element (a second element), then no element (e.g., a third element) is interposed therebetween.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the invention referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or equivalents thereof is possible without departing from the spirit of the invention. Such as the above-described features and technical features having similar functions (but not limited to) disclosed in the present application are replaced with each other.
Claims (7)
1. The method for constructing the medical knowledge graph is characterized by comprising the following steps of:
performing sensitive information elimination operation on original case data acquired from a case database or the Internet to obtain case source data from which sensitive information is eliminated;
screening the human body structure data, the human body function data and the clinical presentation data of the cases from the case source data with the sensitive information eliminated according to a pre-configured basic medical knowledge base;
screening out the human body structure checking technical data and human body function checking technical data of the case from the case source data of which the sensitive information is eliminated according to a pre-configured clinical medical knowledge base:
The human body structure data corresponds to the human body structure checking technical data, the human body function data corresponds to the human body function checking technical data, and the corresponding result of the human body structure data and the human body structure checking technical data, the corresponding result of the human body function data and the human body function checking technical data and the clinical presentation data are organized according to a pre-configured causal logic chain so as to construct a medical knowledge graph;
wherein, according to the preconfigured basic medical knowledge base, the human body structure data, the human body function data and the clinical manifestation data of the case are screened from the case source data of which the sensitive information is eliminated, and the method comprises the following steps:
comparing the case source data with human body structure data in a human body structure knowledge sub-base included in the basic medical knowledge base, and if the case source data and the human body structure data are the same, determining that the human body structure data screened from the case source data are the human body structure data in the human body structure knowledge sub-base;
comparing the case source data with human body function data in a human body function knowledge sub-base included in the basic medical knowledge base, and if the case source data and the human body function data are the same, determining that the human body function data screened from the case source data are the human body function data in the human body function knowledge sub-base;
Comparing the case source data with clinical manifestation data in a clinical manifestation sub-base included in the basic medical knowledge base, and if the case source data and the clinical manifestation data are the same, determining that the clinical manifestation data screened from the case source data are the clinical manifestation data in the clinical manifestation sub-base;
wherein the screening the human body structure checking technical data and human body function checking technical data of the case from the case source data of the removed sensitive information according to a pre-configured clinical medical knowledge base comprises the following steps:
comparing the case source data with human body structure inspection technology data in a human body structure inspection technology sub-library included in the clinical medical knowledge base, and if the case source data and the human body structure inspection technology data are the same, determining the human body structure inspection technology data screened from the case source data as the human body structure inspection technology data in the human body structure inspection technology sub-library;
comparing the case source data with human body function inspection technology data in a human body function inspection technology sub-library included in the clinical medical knowledge base, and if the case source data and the human body function inspection technology data are the same, determining that the human body function inspection technology data screened from the case source data are the human body function inspection technology data in the human body function inspection technology sub-library;
Wherein after constructing the medical knowledge-graph, the method further comprises:
receiving clinical performance data of a patient;
deriving human body structure data, human body structure inspection technical data, human body function data, and human body function inspection technical data related to clinical manifestation data of the patient according to the medical knowledge graph;
determining clinical exam items of the patient based on the human body structure data, the human body structure exam technique data, the human body function data, and the human body function exam technique data associated with the clinical manifestation data of the patient.
2. The method for constructing a medical knowledge graph according to claim 1, wherein the operation of eliminating sensitive information of original case data acquired from a case database or the internet comprises:
and carrying out sensitive information elimination operation on the original case data acquired from a case database or the Internet through a sensitive information elimination model so as to obtain case source data of the eliminated sensitive information.
3. The method for constructing a medical knowledge graph according to claim 1, wherein the method further comprises:
if the abnormal human body structure of the patient is determined according to the examination result of the clinical examination item of the patient, the human body structure treatment technical data of the patient is determined according to the abnormal human body structure data of the patient and the causal logic chain.
4. The method for constructing a medical knowledge graph according to claim 1, wherein the method further comprises:
if the abnormal human body function of the patient is determined according to the examination result of the clinical examination item of the patient, the human body function treatment technical data of the patient is determined according to the abnormal human body function data of the patient and the causal logic chain.
5. A medical knowledge graph construction apparatus, characterized in that the apparatus comprises:
the elimination module is used for carrying out elimination operation of sensitive information on the original case data acquired from the case database or the Internet so as to obtain case source data of which the sensitive information is eliminated;
the first screening module is used for screening the human body structure data, the human body function data and the clinical presentation data of the cases from the case source data with the sensitive information eliminated according to a pre-configured basic medical knowledge base;
the second screening module is used for screening the human body structure inspection technical data and the human body function inspection technical data of the cases from the case source data of which the sensitive information is eliminated according to a pre-configured clinical medical knowledge base;
the knowledge graph construction module is used for organizing the human body structure data and the human body structure checking technical data, the human body function data and the human body function checking technical data according to a pre-configured causal logic chain, and organizing the corresponding result of the human body structure data and the human body structure checking technical data, the corresponding result of the human body function data and the human body function checking technical data and the clinical presentation data to construct a medical knowledge graph;
Wherein, according to the preconfigured basic medical knowledge base, the human body structure data, the human body function data and the clinical manifestation data of the case are screened from the case source data of which the sensitive information is eliminated, and the method comprises the following steps:
comparing the case source data with human body structure data in a human body structure knowledge sub-base included in the basic medical knowledge base, and if the case source data and the human body structure data are the same, determining that the human body structure data screened from the case source data are the human body structure data in the human body structure knowledge sub-base;
comparing the case source data with human body function data in a human body function knowledge sub-base included in the basic medical knowledge base, and if the case source data and the human body function data are the same, determining that the human body function data screened from the case source data are the human body function data in the human body function knowledge sub-base;
comparing the case source data with clinical manifestation data in a clinical manifestation sub-base included in the basic medical knowledge base, and if the case source data and the clinical manifestation data are the same, determining that the clinical manifestation data screened from the case source data are the clinical manifestation data in the clinical manifestation sub-base;
wherein the screening the human body structure checking technical data and human body function checking technical data of the case from the case source data of the removed sensitive information according to a pre-configured clinical medical knowledge base comprises the following steps:
Comparing the case source data with human body structure inspection technology data in a human body structure inspection technology sub-library included in the clinical medical knowledge base, and if the case source data and the human body structure inspection technology data are the same, determining the human body structure inspection technology data screened from the case source data as the human body structure inspection technology data in the human body structure inspection technology sub-library;
comparing the case source data with human body function inspection technology data in a human body function inspection technology sub-library included in the clinical medical knowledge base, and if the case source data and the human body function inspection technology data are the same, determining that the human body function inspection technology data screened from the case source data are the human body function inspection technology data in the human body function inspection technology sub-library;
wherein after constructing the medical knowledge graph, further comprising:
receiving clinical performance data of a patient;
deriving human body structure data, human body structure inspection technical data, human body function data, and human body function inspection technical data related to clinical manifestation data of the patient according to the medical knowledge graph;
determining clinical exam items of the patient based on the human body structure data, the human body structure exam technique data, the human body function data, and the human body function exam technique data associated with the clinical manifestation data of the patient.
6. An electronic device, the device comprising:
one or more processors;
a computer readable medium configured to store one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of constructing a medical knowledge graph of any of claims 1-4.
7. A computer-readable medium, characterized in that a computer program is stored thereon, which program, when being executed by a processor, implements a method of constructing a medical knowledge-graph according to any one of claims 1-4.
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