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CN111383773B - Medical entity information processing method and device, storage medium and electronic equipment - Google Patents

Medical entity information processing method and device, storage medium and electronic equipment Download PDF

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CN111383773B
CN111383773B CN201811624476.7A CN201811624476A CN111383773B CN 111383773 B CN111383773 B CN 111383773B CN 201811624476 A CN201811624476 A CN 201811624476A CN 111383773 B CN111383773 B CN 111383773B
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CN111383773A (en
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王尧
李林峰
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Golden Panda Ltd
Yidu Cloud Beijing Technology Co Ltd
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Golden Panda Ltd
Yidu Cloud Beijing Technology Co Ltd
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    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H70/00ICT specially adapted for the handling or processing of medical references
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02ATECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

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Abstract

The disclosure belongs to the field of knowledge maps, and relates to a method and a device for processing medical entity information and electronic equipment. The method comprises the following steps: acquiring target medical information according to preset conditions, wherein the target medical information comprises a first medical entity list, a second medical entity list and a third medical entity list; determining a plurality of pieces of condition entity information from the first medical entity list, the second medical entity list and/or the third medical entity list, determining one piece of associated entity information from the first medical entity list or the second medical entity list, and acquiring a high-order entity relation pair according to the plurality of pieces of condition entity information and the associated entity information; wherein the plurality of conditional entity information is different from the associated entity information; and determining the association relation between different medical entity information according to the high-order entity relation pair. The method and the device can enable information expression in the medical knowledge graph to be more perfect and accurate, and improve the accuracy of reasoning calculation.

Description

Medical entity information processing method and device, storage medium and electronic equipment
Technical Field
The disclosure relates to the technical field of knowledge maps, and in particular relates to a medical entity information processing method, a medical entity information processing device, a computer readable storage medium and electronic equipment.
Background
The knowledge map is also called a scientific knowledge map, is called a knowledge domain visualization or knowledge domain mapping map in the book emotion, is a series of different graphs for displaying the knowledge development process and the structural relationship, describes knowledge resources and carriers thereof by using a visualization technology, digs, analyzes, constructs, draws and displays knowledge and the interrelation between the knowledge resources and the carriers, breaks the limit of an original relational database, has very strong expression capability, plays an increasingly important role in the fields of information retrieval, information integration and the like, and can provide a wider and deeper knowledge system for users and continuously expand.
Along with the development of electronic information technology, in the medical field, a medical knowledge graph is formed by inducing and arranging medical knowledge, wherein the medical knowledge graph comprises the intricate and complex relationship among symptoms, diseases and diagnosis and treatment means, and a good auxiliary diagnosis means can be provided for medical staff through the medical knowledge graph. However, when the medical knowledge graph is constructed, only the relation between every two medical entities is designed, when reasoning is carried out according to the medical knowledge graph, the assumption of 'condition independent' is needed to be introduced, and the multi-element condition is converted into the single-element condition calculation, but not all the conditions are mutually independent, so that the condition independent assumption does not meet the actual condition, and the calculation error can be caused in many scenes.
Accordingly, there is a need in the art for a new method and apparatus for processing medical entity information.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The disclosure aims to provide a method for constructing a medical knowledge graph, a data processing device, a computer-readable storage medium and an electronic device, so as to overcome the problems of calculation errors and the like caused by independent assumption conditions when reasoning according to the medical knowledge graph due to limitations and defects of related technologies at least to a certain extent.
According to one aspect of the present disclosure, there is provided a method of processing medical entity information, including:
acquiring target medical information according to preset conditions, wherein the target medical information comprises a first medical entity list, a second medical entity list and a third medical entity list;
determining a plurality of pieces of condition entity information from the first medical entity list, the second medical entity list and/or the third medical entity list, determining one piece of associated entity information from the first medical entity list or the second medical entity list, and acquiring a high-order entity relation pair according to the plurality of condition entities and the associated entity; wherein the plurality of conditional entity information is different from the associated entity information;
And determining the association relation between different medical entity information according to the high-order entity relation pair.
In an exemplary embodiment of the present disclosure, acquiring target medical information according to a preset condition, the target medical information including a first medical entity list, a second medical entity list, and a third medical entity list, includes:
acquiring medical information corresponding to the visit ID from an electronic medical record database according to the visit ID;
and preprocessing the medical information to acquire the target medical information, wherein the target medical data comprises the first medical entity list, the second medical entity list and the third medical entity list.
In an exemplary embodiment of the present disclosure, determining an association relationship between different medical entity information according to the pair of higher-order entity relationships includes:
calculating conditional probabilities among the different medical entity information according to the high-order entity relation pairs;
and determining the association relation between the different medical entity information according to the conditional probability.
In an exemplary embodiment of the present disclosure, calculating conditional probabilities between the different medical entity information from the pair of higher-order entity relationships comprises:
Acquiring a first number of entity relation pairs which are the same as the high-order entity relation pairs in an electronic medical record database;
acquiring a second number of entity relation pairs with the same conditional entity information as the higher-order entity relation pair conditional entity information in the electronic medical record database;
the first number is compared to the second number to obtain the conditional probability.
In an exemplary embodiment of the present disclosure, the method further comprises:
mapping the diagnosis entity information in the first medical entity list with the non-diagnosis entity information in the second medical entity list and the patient information entity information in the third medical entity list respectively to obtain a first-order entity relation pair;
mapping non-diagnostic entity information in the second medical entity list with patient information entity information in the third medical entity list to obtain a second-order entity relationship pair;
mapping the target non-diagnostic entity information in the second medical entity list with other non-diagnostic entity information to obtain a third-order entity relation pair;
and determining a first-order entity relation pair according to the first-order entity relation pair, the second first-order entity relation pair and the third first-order entity relation pair.
In an exemplary embodiment of the present disclosure, the first list of medical entities is a list of diagnostic entities, the second list of medical entities is a list of non-diagnostic entities, and the third list of medical entities is a list of patient information entities.
In an exemplary embodiment of the present disclosure, the method further comprises:
and respectively carrying out Cartesian product on the diagnosis entity list and the non-diagnosis entity list, the diagnosis entity list and the patient information entity list, and the non-diagnosis entity list and the patient information entity list, and forming entity relation pairs according to target non-diagnosis entity information and other non-diagnosis entity information in the non-diagnosis entity list so as to obtain first-order entity relation pairs.
According to an aspect of the present disclosure, there is provided a processing apparatus of medical entity information, including:
the target medical data acquisition module is used for acquiring target medical information according to preset conditions, wherein the target medical information comprises a first medical entity list, a second medical entity list and a third medical entity list;
a higher-order entity relation pair obtaining module, configured to determine a plurality of condition entity information from the first medical entity list, the second medical entity list and/or the third medical entity list, determine one associated entity information from the first medical entity list or the second medical entity list, and obtain a higher-order entity relation pair according to the plurality of condition entities and the associated entity; wherein the plurality of conditional entity information is different from the associated entity information;
And the entity association relation determining module is used for determining association relations among different medical entity information according to the high-order entity relation pairs.
According to an aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of processing medical entity information according to any one of the above.
According to one aspect of the present disclosure, there is provided an electronic device including:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of processing medical entity information according to any of the preceding claims via execution of the executable instructions.
The method comprises the steps of determining a plurality of pieces of condition entity information from a first medical entity list, a second medical entity list and/or a third medical entity list, determining one piece of associated entity information from the first medical entity list or the second medical entity list, obtaining a high-order entity relation pair according to the plurality of pieces of condition entity information and the associated entity information, and finally determining the association relation between different pieces of medical entity information according to the high-order entity relation pair. On the one hand, the method determines the association relation between different medical entity information according to the higher-order entity relation pair, can avoid the assumption of independent conditions, can obtain the multi-element condition probability, and improves the accuracy of calculation; on the other hand, the medical knowledge graph is constructed according to the first-order entity relation pair, the higher-order entity relation pair and the association relation between the medical entity information, so that the information expression in the medical knowledge graph can be more perfect and more accurate.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 schematically shows a flow diagram of a method of processing medical entity information;
FIG. 2 schematically illustrates an example diagram of an application scenario of a method of processing medical entity information;
FIG. 3 schematically illustrates a flow diagram for determining a higher-order entity-relationship pair;
FIG. 4 schematically illustrates a flow diagram for obtaining a first-order entity-relationship pair;
FIG. 5 schematically shows a flow diagram for constructing a medical knowledge graph;
fig. 6 schematically shows a schematic structural diagram of a processing device of medical entity information;
FIG. 7 schematically shows an example block diagram of an electronic device for implementing the above-described method of processing medical entity information;
Fig. 8 schematically shows a computer readable storage medium for implementing the above-described processing method of medical entity information.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
In the related art in the art, knowledge is generally derived from two aspects for medical related workers, on the one hand, learning textbooks, clinical guidelines, monographs, papers, and other literature knowledge, and on the other hand, empirical knowledge accumulated in clinical practice. The literature knowledge and the experience knowledge are not cleavable or replaceable, but are complementary. With the further understanding of the value of real-world clinical data by the medical industry and the modification of diagnosis and treatment methods based on real-world data, it is necessary to simulate human knowledge sources, and computer algorithm systems are required to construct their own medical knowledge maps based on literature knowledge and real-world data.
However, in the related art, the method of constructing the medical knowledge graph according to the literature knowledge and the real world data inevitably has corresponding defects, specifically, the medical knowledge graph is only designed with the relation between every two medical entities, when the medical knowledge graph is inferred, the conditions are required to be independent, the description is performed through the univariate condition probability, but the entity relation in the medicine is not described by the univariate condition probability in many cases, for example, the patient suffers from pneumonia, the symptom is the milk cough, if the medical knowledge graph is constructed according to [ pneumonia, milk cough ] and there is a problem that only infants will generally appear the symptom of milk cough, adults are unlikely to appear, and the univariate condition probability description such as pure P (milk cough|pneumonia) cannot embody the key constraint term of 'infants'. In view of this problem, based on a naive bayes algorithm, the multivariate condition is converted into the univariate condition calculation, for example, assuming that the "pneumonia" is independent of the "infant" condition, P (hemoptysis |pneumonia, infant) =p (hemoptysis) ×p (pneumonia|hemoptysis) ×p (infant|hemoptysis)/P (pneumonia, infant), but the assumption that the condition is independent does not satisfy the actual situation, and calculation errors are caused in many situations, so that the information expression in the current medical knowledge graph is not perfect enough, and the multivariate condition probability cannot be expressed yet.
In view of the problems in the related art, the present exemplary embodiment firstly provides a method for processing medical entity information, where the method for processing medical entity information may be executed on a server, or may be executed on a server cluster or a cloud server, or the like, and of course, a person skilled in the art may execute the method of the present disclosure on other platforms according to requirements, which is not limited in particular in the present exemplary embodiment. Referring to fig. 1, the method for processing medical entity information may include the steps of:
s110, acquiring target medical information according to preset conditions, wherein the target medical information comprises a first medical entity list, a second medical entity list and a third medical entity list;
s120, determining a plurality of pieces of condition entity information from the first medical entity list, the second medical entity list and/or the third medical entity list, determining one piece of associated entity information from the first medical entity list or the second medical entity list, and acquiring a high-order entity relation pair according to the plurality of condition entities and the associated entity; wherein the plurality of conditional entity information is different from the associated entity information;
And S130, determining the association relation between different medical entity information according to the high-order entity relation pair.
On the one hand, the method and the device can express the multi-element conditional probability by determining a plurality of pieces of conditional entity information and one piece of associated entity information from different medical entity lists and forming a high-order entity relation pair according to the plurality of pieces of conditional entity information and the associated entity information, and further can avoid the problem of calculation errors caused by independent assumption conditions when the association relation between the different pieces of medical entity information is determined according to the high-order entity relation pair; on the other hand, the determination of the higher-order entity relation pair can enable information in the medical knowledge graph to be more perfect and information expression to be more comprehensive.
Next, respective steps of the processing method of medical entity information of the present disclosure will be described according to the structure shown in fig. 2:
in step S110, target medical information is acquired according to a preset condition, where the target medical information includes a first medical entity list, a second medical entity list, and a third medical entity list.
In an exemplary embodiment of the present disclosure, medical information refers to data information in medical records generated during a patient's medical visit, and may specifically include clinical medical data stored in an electronic medical record database. In the process of medical treatment of the patient, all generated medical data may be stored in an electronic medical record database, which may be a data warehouse set in the terminal device 201 and used for storing medical data, or may be a storage server used for storing medical data, and the server 202 may acquire target medical data from the data warehouse of the terminal device 201 or may acquire target medical data from the storage server. Since the number of patients to be diagnosed is large and a plurality of examinations are required for each patient according to the difference of symptoms, and accordingly many examination data are generated, the number of medical data in the electronic medical record database is huge, and in order to improve the efficiency of acquiring target medical data, the target medical data can be acquired according to preset conditions. The preset condition may be a patient visit ID, where the visit ID is used to identify each visit of the patient, and the server 202 may screen from the electronic medical record database according to the visit ID, to obtain the target medical data corresponding to the visit ID. Further, the patient ID may be a patient ID, or may be new identification information generated based on the patient ID, and the patient ID may be ID information generated based on the birth date and the visit date of the patient, or may be ID information generated based on the birth date, the visit department number, and the visit date of the patient, or may be ID information generated based on other information, which is not particularly limited in the present disclosure.
In an exemplary embodiment of the present disclosure, different types of medical entity lists may be included in the target medical information, and in an embodiment of the present disclosure, the target medical information includes a first medical entity list, a second medical entity list, and a third medical entity list, and in particular, the medical entity lists may be classified into according to types: the first medical entity list in the embodiment of the disclosure is a diagnostic entity list, the second medical entity list is a non-diagnostic entity list, and the third medical entity list is a patient information entity list. The entity information in the diagnosis entity list may be specifically a diagnosis name and a corresponding ICD code, and the diagnosis entity list may be generated according to node information by extracting main diagnosis information corresponding to the diagnosis ID, searching relevant nodes of the main diagnosis information in ICD-10 disease naming standards. For example, if the primary diagnosis information of a patient is "stomach Dou Exing tumor" and the ICD is C16.301, then the diagnosis-related entity information of this visit includes C16.3-pylorus Dou Exing tumor and C16-stomach malignancy. The entity information in the non-diagnostic entity list may be entity information related to symptoms, examination, etc., for example, a medical department entity, a pharmaceutical combination entity, a surgical name entity, etc., and the method of acquiring the non-diagnostic entity list may be to extract a pharmaceutical combination and a pharmaceutical entity in a medical order prescribed by a doctor from a medical order table, extract a surgical name entity from a surgical table, extract a medical department entity of a patient from a medical records first page or a medical records table, and extract other related entity information from other data tables, and form a non-diagnostic entity list according to the acquired respective entities. The entity information in the patient information entity list may be specifically a patient ID, a patient gender, a patient age, a patient height, a patient weight, etc., and the patient information entity list may be formed by extracting patient information from a table of diagnosis or a top page of a medical record.
In an exemplary embodiment of the present disclosure, in order to ensure high availability of the target medical data, the target medical data may be subjected to a preprocessing operation after being acquired, for example, cleaning the target medical data to remove an abnormal value, a repeated value, a missing value, and the like therein; data integration is carried out on the target medical data, and the target medical data is integrated according to the type corresponding to the target medical data; of course, other pretreatment operations may be performed, and this disclosure is not repeated here.
In step S120, determining a plurality of conditional entity information from the first medical entity list, the second medical entity list and/or the third medical entity list, determining one associated entity information from the first medical entity list or the second medical entity list, and acquiring a higher-order entity relation pair according to the plurality of conditional entities and the associated entity; wherein the plurality of conditional entity information is different from the associated entity information.
In an exemplary embodiment of the present disclosure, after the first medical entity list, the second medical entity list, and the third medical entity list are acquired, entity relationship pairs may be formed according to correlations between entities in the respective lists, and the entity relationship pairs may include a first-order entity relationship pair and a higher-order entity relationship pair.
In the exemplary embodiments of the present disclosure, the occurrence of a certain symptom or a certain disease is not usually caused by one condition factor, but may be caused by the co-action of a plurality of condition factors, so that the relationship between the information of the medical entities is not fully represented by the first-order entity relationship, and thus, a high-order entity relationship pair needs to be commonly determined according to the plurality of condition factors and the corresponding symptom or disease to represent the relationship between different medical entities. The higher-order entity relationship pair may be a second-order entity relationship pair, a third-order entity relationship pair, or a higher-order entity relationship pair, and fig. 3 shows a flow chart of determining the higher-order entity relationship pair, as shown in fig. 3, and in step S301, a plurality of conditional entity information is determined from the first medical entity list, the second medical entity list, and/or the third medical entity list; the occurrence of a certain condition may be affected by different ages and sexes, by different diseases and ages, by different ages, sexes and diseases, etc., and similarly, the occurrence of a certain disease may be determined by a plurality of conditional factors, which may be of the same entity type, or may be of different entity types, and in order to correctly express the relationship of the medical entity information, a plurality of conditional entity information may be determined from the first medical entity list, the second medical entity list and/or the third medical entity list; in step S302, determining an associated entity information from the first medical entity list or the second medical entity list; since the conditional factors affect the generation of the result, and in the medical field, patient information such as patient age, patient sex, patient weight, etc. is generally the cause of the result generation, it is possible to determine one associated entity information from the diagnostic entity list or the non-diagnostic entity list; in step S303, a higher-order entity relationship pair is obtained according to the plurality of conditional entity information and the associated entity information; after the conditional entity information and the associated entity information are determined, a higher-order entity relation pair can be formed according to the conditional entity information and the associated entity information, and the specific expression form of the higher-order entity relation pair can be < (conditional 1+conditional 2+ … …) -associated entity >.
Table 1 shows a specific composition of target medical information, and as shown in Table 1, the diagnosis name in the diagnosis entity list is pneumonia; the symptoms contained in the non-diagnostic entity list are milk cough and dyspnea, the department of diagnosis is pediatric, the examination is chest X-ray examination, and the examination is blood routine examination and etiology examination; the patient information entity list contains patients of 3 months of age, male sex, 5Kg of weight and 50cm of height.
TABLE 1
Figure BDA0001927679020000091
Figure BDA0001927679020000101
From the target medical information shown in table 1, according to the relationship among the diagnostic entity, the non-diagnostic entity and the patient information entity, the diagnostic name and the patient age can be used as conditional entity information, the symptom can be used as associated entity information, and a second-order entity relationship pair < (pneumonia+3 months) -cough can be formed according to the conditional entity information and the associated entity information; the age, symptoms and examination of the patient can be used as conditional entities, the diagnosis name is used as an associated entity, and a third-order entity relation pair < (3 months+cough+chest X-ray) -pneumonia > is formed according to the conditional entities and the associated entity; of course, a higher-order entity relationship pair may be formed according to other relationships between entities, so as to obtain a higher-order entity relationship. The higher-order entity relationship pairs in the present disclosure include, but are not limited to, the second-order entity relationship pair and the third-order entity relationship pair described above, but may also be other higher-order entity relationship pairs, which are not specifically limited in this disclosure.
Further, for some specific entity relationships, a higher-order entity relationship pair may be directly formed according to the entity relationship, for example, some symptoms may only occur under a certain gender, a certain age group and some diseases, then for describing the relationship, a higher-order entity relationship pair may be formed according to symptoms, gender, age group and diseases, for example, a female may have a specific symptom S in a disease D occurring between 20-40 years, and then a third-order entity relationship pair may be directly constructed, which is specifically expressed as < (d+female+20-40 years) -S >.
In step S130, an association relationship between different medical entity information is determined according to the pair of higher-order entity relationships.
In an exemplary embodiment of the present disclosure, after the higher-order entity-relationship pair is acquired, an association relationship between different medical entity information may be determined according to the higher-order entity-relationship pair. The association relation can be specifically reflected by the conditional probability among different medical entity information, the conditional probability can be obtained by calculation according to a higher-order entity relation pair, and the calculation formula of the conditional probability is shown in a formula (1):
p (associated entity|condition 1, condition 2, … …, condition K) =n i /N (1)
Wherein K is the number of conditional entity information in the higher-order entity relation pair, N i The number of entity relation pairs which are the same as the number of the higher-order entity relation pairs in the electronic medical record database is N, and the number of entity relation pairs which are the same as the number of the conditional entity information in the higher-order entity relation pairs in the electronic medical record database is N.
Taking the conditional probability of a symptom R under K conditions as an example, the calculation formula is as follows: p (symptom=r|condition 1, condition 2, … …, condition K) =number of records satisfying K conditions in the K-order relation pair and the symptom is R/number of records satisfying all symptoms of K conditions in the K-order relation pair, where K is the number of conditional entity information in the higher-order entity relation pair. It should be noted that, the formula (1) may also be used to calculate the conditional probability of the first-order entity relationship pair, that is, the unary conditional probability calculated when K is 1.
In an exemplary embodiment of the present disclosure, a first-order entity relationship between different medical entity information may be further acquired according to the first medical entity list, the second medical entity list, and the third medical entity list, fig. 4 shows a flow chart of acquiring a first-order entity relationship pair, and in step S401, diagnostic entity information in the first medical entity list is mapped with non-diagnostic entity information in the second medical entity list and patient information entity information in the third medical entity list, respectively, to acquire the first-order entity relationship pair, as shown in fig. 4; in step S402, mapping non-diagnostic entity information in the second medical entity list with patient information entity information in the third medical entity list to obtain a second first order entity relationship pair; in step S403, mapping the target non-diagnostic entity information in the second medical entity list with other non-diagnostic entity information to obtain a third-order entity relationship pair; in step S404, a first-order entity relationship pair is determined according to the first-order entity relationship pair, the second-order entity relationship pair, and the third-order entity relationship pair. Wherein the first list of entities may be a list of diagnostic entities, the second list of entities may be a list of non-diagnostic entities, and the third list of entities may be a list of patient information entities.
Further, when mapping the diagnostic entity information with the non-diagnostic entity information, mapping the diagnostic entity information with the patient information entity information, and mapping the non-diagnostic entity information with the patient information entity information, the diagnostic entity list and the non-diagnostic entity list may be respectively subjected to a cartesian product, the diagnostic entity list and the patient information entity list may be subjected to a cartesian product, and the non-diagnostic entity list and the patient information entity list may be subjected to a cartesian product, so as to obtain a first-order entity relationship pair between the entity information in each entity list. After the diagnostic entity list and the non-diagnostic entity list are subjected to Cartesian product, the relationship between the diagnostic name and non-diagnostic entity information such as medication, examination, inspection, operation, department of diagnosis and the like can be obtained, for example, medical entity relationship pairs such as < gastric malignancy-tigaom >, < gastric malignancy-gastroscopy >; after the diagnosis entity list and the patient information entity list are subjected to Cartesian product, the relationship between the diagnosis name and the entity information such as the patient age, the patient sex and the like can be obtained, for example, medical entity relationship pairs such as < gastric malignancy-61 years >, < gastric malignancy-male >; after the non-diagnostic entity list and the patient information entity list are subjected to Cartesian product, the relationship between non-diagnostic entity information such as medication, examination, inspection, operation, medical department and the like and entity information such as patient age, patient sex and the like, for example, a medical entity relationship pair such as < tigiow-male > and the like can be obtained. Meanwhile, the target non-diagnostic entity information in the second entity list may be mapped with other non-diagnostic entity information to form a medical entity relationship pair, and specifically, any one of non-diagnostic entities such as medication, examination, surgery, department of diagnosis, etc. may be mapped with other non-diagnostic entity information, for example, symptom "abdominal pain" is mapped with examination "gastroscopy", to form a < abdominal pain-gastroscopy > medical entity relationship pair.
In an exemplary embodiment of the present disclosure, after the first-order entity relationship pair and the higher-order entity relationship pair are acquired according to the relationship between the medical entity information, a medical knowledge graph may be formed according to the first-order entity relationship pair and the higher-order entity relationship pair. In embodiments of the present disclosure, medical knowledge maps may be constructed in the form of bayesian networks for recording relationships between different medical concepts. Fig. 5 shows a schematic flow chart of constructing a medical knowledge graph, as shown in fig. 5, in step S501, K-ary conditional probabilities between respective medical entity information are calculated, where k=1, 2,3, … …; specifically, the conditional probability between the individual medical entity information may be calculated according to formula (1); in step S502, determining whether there is a directed edge between nodes in the bayesian network according to the conditional probability; when the conditional probability of the information of two medical entities is 0, indicating that no edge exists between nodes in the Bayesian network formed by the information of the two medical entities; when the conditional probability of the information of two medical entities is not 0, indicating that edges exist between nodes in the Bayesian network formed by the information of the two medical entities, wherein the directions of the edges are the directions of the information of the conditional entities and the information of the related entities; in step S503, a conditional probability table is set corresponding to each medical entity information; the conditional probability table is used for representing the influence degree of the change of the conditional entity information on the associated entity information.
In the exemplary embodiment of the present disclosure, since the medical knowledge graph includes a pair of higher-order entity relationships, not just a pair of first-order entity relationships, when reasoning is performed according to the medical knowledge graph in the present disclosure, the higher-order entity relationships can be converted into multiple condition probabilities without performing a condition independent assumption between the conditions, for example, for a second-order entity relationship pair < (pneumonia+3 months) -milk cough >, the corresponding binary condition probability is P (milk cough|pneumonia, 3 months), and when calculating the binary condition probability, it is not necessary to assume that the conditions between the pneumonia and 3 months are independent; similarly, for the third-order entity relation pair < (3 months+milk cough+chest X-ray) -pneumonia >, the corresponding ternary condition probability is P (pneumonia|3 months, milk cough and chest X-ray), and when the ternary condition probability is calculated, the condition independence between 3 months, milk cough and chest X-ray is not required to be assumed.
The processing method of the medical entity information can obtain the high-order entity relation pair, so that when reasoning is carried out according to the medical knowledge graph, the high-order entity relation can be converted into the multi-element condition probability without assuming mutual independence among multi-element conditions, and calculation errors caused by the fact that independent assumption of the conditions does not meet the actual conditions are avoided.
The disclosure also provides a medical entity information processing device. Fig. 6 shows a schematic structural diagram of a processing apparatus for medical entity information, which may include a target medical data acquisition module 610, a higher-order entity-relationship pair acquisition module 620, and an entity-relationship determination module 630, as shown in fig. 6. Wherein:
a target medical data obtaining module 610, configured to obtain target medical information according to a preset condition, where the target medical information includes a first medical entity list, a second medical entity list, and a third medical entity list;
a higher-order entity relationship pair obtaining module 620, configured to determine a plurality of condition entity information from the first medical entity list, the second medical entity list, and/or the third medical entity list, determine one associated entity information from the first medical entity list or the second medical entity list, and obtain a higher-order entity relationship pair according to the plurality of condition entities and the associated entity; wherein the plurality of conditional entity information is different from the associated entity information;
and an entity association relationship determining module 630, configured to determine association relationships between different medical entity information according to the pair of higher-order entity relationships.
The specific details of each module in the above-mentioned processing device careful by the medical entity have been described in detail in the corresponding processing method of the medical entity information, so that the details are not repeated here.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 700 according to such an embodiment of the present disclosure is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 7, the electronic device 700 is embodied in the form of a general purpose computing device. Components of electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, and a bus 730 connecting the different system components, including the memory unit 720 and the processing unit 710.
Wherein the storage unit stores program code that is executable by the processing unit 710 such that the processing unit 710 performs steps according to various exemplary embodiments of the present disclosure described in the above-described "exemplary methods" section of the present specification. For example, the processing unit 710 may perform step S110 as shown in fig. 1: acquiring target medical information according to preset conditions, wherein the target medical information comprises a first medical entity list, a second medical entity list and a third medical entity list; step S120: determining a plurality of pieces of condition entity information from the first medical entity list, the second medical entity list and/or the third medical entity list, determining one piece of associated entity information from the first medical entity list or the second medical entity list, and acquiring a high-order entity relation pair according to the plurality of pieces of condition entity information and the associated entity information; wherein the plurality of conditional entity information is different from the associated entity information; step S130: and determining the association relation between different medical entity information according to the high-order entity relation pair.
The memory unit 720 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 7201 and/or cache memory 7202, and may further include Read Only Memory (ROM) 7203.
The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 730 may be a bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 700 may also communicate with one or more external devices 1100 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 700, and/or any device (e.g., router, modem, etc.) that enables the electronic device 700 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 550. Also, electronic device 700 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 760. As shown, network adapter 760 communicates with other modules of electronic device 700 over bus 730. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 700, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible implementations, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.
Referring to fig. 8, a program product 800 for implementing the above-described method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a 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.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is 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 (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with 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 readable signal medium may also be any readable medium that is not a 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 readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like 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 computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Furthermore, the above-described figures are only schematic illustrations of processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims (8)

1. A method of processing medical entity information, comprising:
acquiring target medical information according to preset conditions, wherein the target medical information comprises a first medical entity list, a second medical entity list and a third medical entity list; the first medical entity list is a diagnosis entity list, the second medical entity list is a non-diagnosis entity list, and the third medical entity list is a patient information entity list;
Determining a plurality of pieces of condition entity information from the first medical entity list, the second medical entity list and/or the third medical entity list, determining one piece of associated entity information from the first medical entity list or the second medical entity list, and acquiring a high-order entity relation pair according to the plurality of pieces of condition entity information and the associated entity information; wherein the plurality of conditional entity information is different from the associated entity information;
calculating conditional probabilities among different medical entity information according to the high-order entity relation pairs; and determining the association relation between the different medical entity information according to the conditional probability.
2. The method for processing medical entity information according to claim 1, wherein acquiring target medical information according to a preset condition, the target medical information including a first medical entity list, a second medical entity list, and a third medical entity list, comprises:
acquiring medical information corresponding to the visit ID from an electronic medical record database according to the visit ID;
and preprocessing the medical information to acquire the target medical information, wherein the target medical data comprises the first medical entity list, the second medical entity list and the third medical entity list.
3. The method of processing medical entity information according to claim 1, wherein calculating conditional probabilities between the different medical entity information from the pair of higher-order entity relationships comprises:
acquiring a first number of entity relation pairs which are the same as the high-order entity relation pairs in an electronic medical record database;
acquiring a second number of entity relation pairs with the same conditional entity information as the higher-order entity relation pair conditional entity information in the electronic medical record database;
the first number is compared to the second number to obtain the conditional probability.
4. The method of processing medical entity information according to claim 1, wherein the method further comprises:
mapping the diagnosis entity information in the first medical entity list with the non-diagnosis entity information in the second medical entity list and the patient information entity information in the third medical entity list respectively to obtain a first-order entity relation pair;
mapping non-diagnostic entity information in the second medical entity list with patient information entity information in the third medical entity list to obtain a second-order entity relationship pair;
Mapping the target non-diagnostic entity information in the second medical entity list with other non-diagnostic entity information to obtain a third-order entity relation pair;
and determining a first-order entity relation pair according to the first-order entity relation pair, the second first-order entity relation pair and the third first-order entity relation pair.
5. The method of processing medical entity information according to claim 1, wherein the method further comprises:
and respectively carrying out Cartesian product on the diagnosis entity list and the non-diagnosis entity list, the diagnosis entity list and the patient information entity list, and the non-diagnosis entity list and the patient information entity list, and forming entity relation pairs according to target non-diagnosis entity information and other non-diagnosis entity information in the non-diagnosis entity list so as to obtain first-order entity relation pairs.
6. A medical entity information processing apparatus, comprising:
the target medical data acquisition module is used for acquiring target medical information according to preset conditions, wherein the target medical information comprises a first medical entity list, a second medical entity list and a third medical entity list; the first medical entity list is a diagnosis entity list, the second medical entity list is a non-diagnosis entity list, and the third medical entity list is a patient information entity list;
A higher-order entity relation pair obtaining module, configured to determine a plurality of condition entity information from the first medical entity list, the second medical entity list and/or the third medical entity list, determine one associated entity information from the first medical entity list or the second medical entity list, and obtain a higher-order entity relation pair according to the plurality of condition entities and the associated entity; wherein the plurality of conditional entity information is different from the associated entity information;
the entity association relation determining module is used for calculating the conditional probability among different medical entity information according to the high-order entity relation pair; and determining the association relation between the different medical entity information according to the conditional probability.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of processing medical entity information according to any one of claims 1-5.
8. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of processing medical entity information of any of claims 1-5 via execution of the executable instructions.
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