CN116313118B - Knowledge graph construction method applied to medical data processing - Google Patents
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- 208000024891 symptom Diseases 0.000 claims abstract description 31
- 230000000875 corresponding effect Effects 0.000 claims description 35
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- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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Abstract
The invention discloses a knowledge graph construction method applied to medical data processing, which relates to the technical field of medical treatment, wherein the medical treatment data of a user are acquired and processed, so that attribute definition is carried out on entities in the medical treatment data, and according to the attribute definition, attribute definition of an entity target is obtained, and then, attribute definitions of disease types, symptoms and physical sign data are obtained, corresponding attribute graphs are respectively generated, different peer-layer disease types and corresponding medical data layers are obtained according to the obtained attribute graphs, the medical data layers are aligned and fused to obtain a medical knowledge graph, and then, a search port is arranged, and required graph content is quickly obtained from the medical knowledge graph through keyword search, so that the user has more specificity in the use process of the knowledge graph.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a knowledge graph construction method applied to medical data processing.
Background
The knowledge graph is a structured semantic knowledge base for describing concepts and interrelationships thereof in a physical world in a symbolic form, and the basic constituent units of the knowledge graph are entity-relation-entity triples and entity and related attribute-value pairs thereof, and the entities are mutually connected through the relation to form a net-shaped knowledge structure. At present, the application of the knowledge graph in the medical field is based on man-machine question answering of the medical knowledge graph, and related technologies mostly find out corresponding items of knowledge points by extracting keywords in questions of users as knowledge points and carrying out one-to-one entity mapping in a database, and then feed back the corresponding items to the users as answers;
the patent with publication number CN108986871A discloses a construction method of an intelligent medical knowledge graph, which comprises the following steps: A. acquiring medical record data and extracting medical entities identified in medical records; B. preprocessing the medical record data and the extracted medical entity to obtain a co-occurrence matrix of the patient and the medical entity; C. obtaining a confidence coefficient value IMPT of the relation between each pair of nodes in the co-occurrence matrix in the step B by adopting a naive Bayesian model calculation, or obtaining a confidence coefficient value IMPT of the relation between each pair of nodes in the co-occurrence matrix in the step B by adopting a NoisyOR model calculation; D. c, ranking all confidence values obtained in the step C according to the order from large to small, taking the relation that the previous n or confidence values are larger than a certain threshold value as edges, and taking all medical entities as nodes to construct an intelligent medical knowledge graph;
the patent with publication number of CN108492887A discloses a medical knowledge graph construction method and device, which can fully utilize data driving and knowledge driving to construct a knowledge graph and solve the technical problem of unsound connection between medical entities of the same type in clinic. The method comprises the following steps: extracting target entities from medical data through natural language processing technology word segmentation; determining a frequent item set of a specified class entity according to an Apriori algorithm to obtain an entity group of the specified class; taking the target entity and the entity group as nodes in the knowledge graph, and calculating intensity indexes among the nodes to obtain a medical knowledge graph; and storing the constructed medical knowledge graph in a Neo4j graph database.
In the prior art, the establishment and the use of the medical knowledge graph have important significance for medical development, but in the use process of the medical knowledge graph, because the medical data contained in the knowledge graph has huge knowledge and often has internal relations among different medical data, in the use process, a user cannot quickly obtain the content part which the user wants from the huge knowledge graph, and therefore, the knowledge graph construction method for medical data processing is provided.
Disclosure of Invention
The invention aims to provide a knowledge graph construction method applied to medical data processing.
The aim of the invention can be achieved by the following technical scheme: the knowledge graph construction method applied to medical data processing comprises the following steps:
step one: constructing a user medical data platform and acquiring medical treatment data of a user;
step two: processing the acquired medical treatment data of the user, and defining the attribute of the medical treatment data;
step three: generating an attribute map according to the attribute defined by the medical treatment data, and correlating different attribute maps;
step four: and generating different medical data layers according to the associated attribute maps, and carrying out alignment fusion on each medical data layer to obtain a medical knowledge map.
Further, the construction process of the user medical data platform comprises the following steps:
setting an information registration port, and enabling a user to perform personal information registration verification through the information registration port;
after the user enters the user medical data platform, a personal information base is established according to personal basic information of the user, all medical treatment data of the user are imported into the personal information base, and the medical treatment data are stored in the user medical data platform.
Further, the process of personal information registration verification by the user through the information registration port comprises the following steps:
the user inputs personal basic information through the information registration port;
uploading personal basic information input by a user to a management center, checking the personal basic information by the management center, generating account information according to a mobile phone number input by the user after the personal basic information passes the checking, and authorizing the account information; the user enters the medical data platform of the user through the mobile phone number input information registration port.
Further, each personal information base is provided with a unique index port, and the index ports correspond to the unique index codes.
Further, the process of acquiring the medical treatment data of the user includes:
acquiring a medical treatment record of a user;
extracting basic information of a user in a medical treatment record, uploading the basic information of the user in the extracted medical treatment record into a user medical data platform, and searching all personal information libraries in the user medical data platform according to the basic information of the user to obtain a personal information library matched with the user; rubbing an index code of the personal information base, and binding and linking the index code with the medical treatment record; importing the medical treatment record into a personal information base through an index port corresponding to the index code; and reading the medical treatment data on the medical treatment record, generating a time section according to the importing time of the medical treatment record, and correlating the time section with the medical treatment data.
Further, the processing of the acquired medical treatment data includes:
marking the entity targets in the medical treatment data, wherein the entity targets in the medical treatment data comprise disease types, symptoms and sign data;
respectively taking the disease type, symptoms and sign data as knowledge nodes, and associating the knowledge nodes;
generating an index instruction according to each knowledge node, and linking with a corresponding knowledge base according to the index instruction;
obtaining corresponding primary index information and secondary index information according to the entity targets in the medical treatment data, and calling corresponding databases and disease types, symptoms and sign data in the database according to the obtained primary index information and secondary index information to finish attribute definition of the entity targets.
Further, the knowledge base is configured to provide a knowledge layer for different knowledge nodes, and the knowledge base building process includes:
establishing different databases corresponding to different types according to different disease types, and establishing a plurality of different database sub-databases in the database of each disease type;
importing symptoms associated with corresponding disease types into different data sub-libraries, and simultaneously inputting sign data of symptoms of different degrees associated with the symptoms;
generating primary index information and secondary index information respectively, wherein different primary index information corresponds to different databases, different secondary index information corresponds to different database sub-libraries, each primary index information is associated with at least one secondary index information, the primary index information corresponds to a disease type, and the secondary index information corresponds to symptoms and sign data of different degrees.
Further, after the definition of the attribute of the entity target in the medical treatment data is completed, generating a corresponding attribute map according to the attribute of the entity target in the medical treatment data, and associating the attribute map according to the medical treatment data includes:
after the attribute definition of the entity target is obtained, the attribute definition of the disease type, symptom and sign data is obtained, and corresponding attribute maps are respectively generated;
acquiring entity intensities between different disease types and corresponding symptoms;
setting different entity intensity range intervals, and matching the obtained entity intensities of different disease types with the entity intensity range intervals; the disease types in the different entity intensity range intervals are correlated and marked as the same-level disease type.
Compared with the prior art, the invention has the beneficial effects that: the medical treatment data of the user are acquired, the medical treatment data are processed, so that the attribute definition is carried out on the entities in the medical treatment data, the attribute definition of the entity target is obtained according to the attribute definition, the attribute definition of the disease type, symptom and sign data is obtained, the corresponding attribute maps are respectively generated, different peer-layer disease types and corresponding medical data layers are obtained according to the obtained attribute maps, the medical data layers are aligned and fused, the medical knowledge maps are obtained, and then the retrieval port is arranged, and the required map content is quickly obtained from the medical knowledge maps through keyword retrieval, so that the user has more specificity in the use process of the knowledge maps.
Drawings
Fig. 1 is a schematic diagram of the present invention.
Detailed Description
As shown in fig. 1, the knowledge graph construction method applied to medical data processing includes the following steps:
step one: constructing a user medical data platform and acquiring medical treatment data of a user;
step two: processing the acquired medical treatment data of the user, and defining the attribute of the medical treatment data;
step three: generating an attribute map according to the attribute defined by the medical treatment data, and correlating different attribute maps;
step four: and generating different medical data layers according to the associated attribute maps, and carrying out alignment fusion on each medical data layer to obtain a medical knowledge map.
It should be further noted that, in the implementation process, the user medical data platform is used for storing personal basic information and medical treatment data of a user, and the construction process of the user medical data platform includes:
setting an information registration port, and enabling a user to perform personal information registration verification through the information registration port;
it should be further noted that, in the implementation process, the process of personal information registration verification by the user through the information registration port includes:
the user inputs personal basic information through an information registration port, wherein the personal basic information comprises a name, an age, a sex and a mobile phone number authenticated by a real name;
uploading personal basic information input by a user to a management center, checking the personal basic information by the management center, generating account information according to a mobile phone number input by the user after the personal basic information passes the checking, and authorizing the account information;
the user enters the medical data platform of the user through the mobile phone number input information registration port;
after a user enters a user medical data platform, a personal information base is established according to personal basic information of the user, all medical treatment data of the user are imported into the personal information base, and the medical treatment data are stored in the user medical data platform;
it should be further noted that, in the implementation process, each personal information base is provided with a unique index port, and the index port corresponds to a unique index code.
It should be further noted that, in the specific implementation process, the specific process of acquiring the medical treatment data of the user includes:
acquiring a medical treatment record of a user; it should be further noted that, in the specific implementation process, the medical treatment record at least includes the basic information of the user, the medical record, the treatment plan information and the basic information of the corresponding doctor, and the basic information of the doctor includes the name, the sex, the department and the mobile phone number of real name authentication;
extracting basic information of a user in a medical treatment record, uploading the basic information of the user in the extracted medical treatment record into a user medical data platform, and searching all personal information libraries in the user medical data platform according to the basic information of the user to obtain a personal information library matched with the user;
rubbing an index code of the personal information base, and binding and linking the index code with the medical treatment record;
importing the medical treatment record into a personal information base through an index port corresponding to the index code;
and reading the medical treatment data on the medical treatment record, generating a time section according to the importing time of the medical treatment record, and correlating the time section with the medical treatment data.
It should be further noted that, in the implementation process, after the medical treatment data of the user is obtained, the obtained medical treatment data is processed, and the specific processing process includes:
marking an entity target in the medical treatment data; it should be further noted that, in the specific implementation process, the physical targets in the medical treatment data include disease type, symptoms and sign data;
respectively taking the disease type, symptoms and sign data as knowledge nodes, and associating the knowledge nodes;
generating an index instruction according to each knowledge node, and linking with a corresponding knowledge base according to the index instruction;
it should be further noted that, in the implementation process, the knowledge base is used for providing knowledge layers for different knowledge nodes, and the establishment process of the knowledge base includes:
establishing different databases corresponding to different types according to different disease types, and establishing a plurality of different database sub-databases in the database of each disease type;
importing symptoms associated with corresponding disease types into different data sub-libraries, and simultaneously inputting sign data of symptoms of different degrees associated with the symptoms;
generating primary index information and secondary index information respectively, wherein different primary index information corresponds to different databases, different secondary index information corresponds to different database sub-libraries, each primary index information can be associated with at least one secondary index information, the primary index information corresponds to disease types, and the secondary index information corresponds to symptoms and sign data of different degrees.
Obtaining corresponding primary index information and secondary index information according to the entity targets in the medical treatment data, and calling corresponding databases and disease types, symptoms and sign data in the database according to the obtained primary index information and secondary index information to finish attribute definition of the entity targets.
It should be further noted that, in the implementation process, after the definition of the attribute of the entity target in the medical treatment data is completed, a corresponding attribute map is generated according to the attribute of the entity target in the medical treatment data, and the attribute map is associated according to the medical treatment data, and the specific process includes:
after the attribute definition of the entity target is obtained, the attribute definition of the disease type, symptom and sign data is obtained, and corresponding attribute maps are respectively generated;
acquiring the number of disease types in medical treatment data, and marking the number of the disease types as n, and marking the number of the disease types as i, wherein i=1, 2, … …, n, n is an integer, and n is more than or equal to 1;
the entity intensity between the disease type labeled i and the corresponding symptom is labeled Q (i);
wherein Q (i) = (1/n) ×a×s i *(1-a)*(K i *B i +1)/(K i +B i )；
Wherein a is a constant and a is not equal to 0, K i Represents the number of diseases contained under the disease type denoted by i, B i Representing the number of symptoms corresponding to different diseases, S i An intensity index corresponding to a disease type denoted by i;
setting different entity intensity range intervals, and matching the obtained entity intensities Q (i) of different disease types with the entity intensity range intervals;
the disease types in the different entity intensity range intervals are correlated and marked as the same-level disease type.
It should be further noted that, in the specific implementation process, the medical data layer is generated according to different disease types of the same layer, the attribute maps belonging to the same disease type of the same layer are imported into the corresponding medical data layer, and the different medical data layers are aligned and fused to obtain the medical knowledge map;
it should be further noted that, in the specific implementation process, the method can also extract the content of the specific map for the medical knowledge map, and the specific process includes:
setting a search port, and inputting at least one keyword by a user through the search port, wherein the keyword comprises disease types, disease names, symptoms or physical sign data and the like;
marking keywords input to a retrieval port by a user, and marking contents in a medical knowledge graph comprising the keywords;
extracting the marked medical knowledge graph to generate different unitized medical knowledge graphs;
it should be further noted that, in the implementation process, when there are multiple unit medical knowledge patterns, the medical data layers to which each unit medical knowledge pattern belongs are different, so that the content part required by the user can be quickly obtained from the huge medical knowledge patterns.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (1)
1. The knowledge graph construction method applied to medical data processing is characterized by comprising the following steps of:
step one: constructing a user medical data platform and acquiring medical treatment data of a user;
step two: processing the acquired medical treatment data of the user, and defining the attribute of the medical treatment data;
step three: generating an attribute map according to the attribute defined by the medical treatment data, and correlating different attribute maps;
step four: generating different medical data layers according to the associated attribute maps, and carrying out alignment fusion on each medical data layer to obtain a medical knowledge map;
the construction process of the user medical data platform comprises the following steps:
setting an information registration port, and enabling a user to perform personal information registration verification through the information registration port;
after a user enters a user medical data platform, a personal information base is established according to personal basic information of the user, all medical treatment data of the user are imported into the personal information base, and the medical treatment data are stored in the user medical data platform;
the process of personal information registration verification by the user through the information registration port comprises the following steps:
the user inputs personal basic information through the information registration port;
uploading personal basic information input by a user to a management center, checking the personal basic information by the management center, generating account information according to a mobile phone number input by the user after the personal basic information passes the checking, and authorizing the account information; the user enters the medical data platform of the user through the mobile phone number input information registration port;
each personal information base is provided with a unique index port, and the index ports correspond to unique index codes;
the acquisition process of the medical treatment data of the user comprises the following steps:
acquiring a medical treatment record of a user;
extracting basic information of a user in a medical treatment record, uploading the basic information of the user in the extracted medical treatment record into a user medical data platform, and searching all personal information libraries in the user medical data platform according to the basic information of the user to obtain a personal information library matched with the user; rubbing an index code of the personal information base, and binding and linking the index code with the medical treatment record; importing the medical treatment record into a personal information base through an index port corresponding to the index code; reading medical treatment data on the medical treatment record, generating a time section according to the importing time of the medical treatment record, and associating the time section with the medical treatment data;
the process of processing the acquired medical treatment data comprises the following steps:
marking the entity targets in the medical treatment data, wherein the entity targets in the medical treatment data comprise disease types, symptoms and sign data;
respectively taking the disease type, symptoms and sign data as knowledge nodes, and associating the knowledge nodes;
generating an index instruction according to each knowledge node, and linking with a corresponding knowledge base according to the index instruction;
obtaining corresponding primary index information and secondary index information according to the entity targets in the medical treatment data, and calling corresponding databases and disease types, symptoms and sign data in the database according to the obtained primary index information and secondary index information to finish attribute definition of the entity targets;
the knowledge base is used for providing knowledge layers for different knowledge nodes, and the establishment process of the knowledge base comprises the following steps:
establishing different databases corresponding to different types according to different disease types, and establishing a plurality of different database sub-databases in the database of each disease type;
importing symptoms associated with corresponding disease types into different data sub-libraries, and simultaneously inputting sign data of symptoms of different degrees associated with the symptoms;
generating primary index information and secondary index information respectively, wherein different primary index information corresponds to different databases, different secondary index information corresponds to different database sub-libraries, each primary index information is associated with at least one secondary index information, the primary index information corresponds to a disease type, and the secondary index information corresponds to symptoms and sign data of different degrees;
after the definition of the attribute of the entity target in the medical treatment data is completed, generating a corresponding attribute map according to the attribute of the entity target in the medical treatment data, and associating the attribute map according to the medical treatment data comprises the following steps:
after the attribute definition of the entity target is obtained, the attribute definition of the disease type, symptom and sign data is obtained, and corresponding attribute maps are respectively generated;
acquiring entity intensities between different disease types and corresponding symptoms;
setting different entity intensity range intervals, and matching the obtained entity intensities of different disease types with the entity intensity range intervals; the disease types in the different entity intensity range intervals are correlated and marked as the same-level disease type.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106874695A (en) * | 2017-03-22 | 2017-06-20 | 北京大数医达科技有限公司 | The construction method and device of medical knowledge collection of illustrative plates |
| CN107145744A (en) * | 2017-05-08 | 2017-09-08 | 合肥工业大学 | Construction method, device and the aided diagnosis method of medical knowledge collection of illustrative plates |
| CN108492887A (en) * | 2018-04-13 | 2018-09-04 | 合肥工业大学 | medical knowledge map construction method and device |
| CN109271530A (en) * | 2018-10-17 | 2019-01-25 | 长沙瀚云信息科技有限公司 | A kind of disease knowledge map construction method and plateform system, equipment, storage medium |
| CN109766445A (en) * | 2018-12-13 | 2019-05-17 | 平安科技（深圳）有限公司 | A kind of knowledge mapping construction method and data processing equipment |
| CN110990584A (en) * | 2019-11-26 | 2020-04-10 | 口口相传(北京)网络技术有限公司 | Knowledge graph generation method and device |
| KR20210000952A (en) * | 2019-06-26 | 2021-01-06 | 주식회사 카카오 | Apparatus and method for knowledge graph indexing |
| CN114064818A (en) * | 2021-11-23 | 2022-02-18 | 北京百度网讯科技有限公司 | Medical entity relationship determination method, training method, device, equipment and medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3677179A4 (en) * | 2017-08-29 | 2020-08-12 | FUJIFILM Corporation | Medical information display device, method, and program |
-
2022
- 2022-06-07 CN CN202210639132.3A patent/CN116313118B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106874695A (en) * | 2017-03-22 | 2017-06-20 | 北京大数医达科技有限公司 | The construction method and device of medical knowledge collection of illustrative plates |
| CN107145744A (en) * | 2017-05-08 | 2017-09-08 | 合肥工业大学 | Construction method, device and the aided diagnosis method of medical knowledge collection of illustrative plates |
| CN108492887A (en) * | 2018-04-13 | 2018-09-04 | 合肥工业大学 | medical knowledge map construction method and device |
| CN109271530A (en) * | 2018-10-17 | 2019-01-25 | 长沙瀚云信息科技有限公司 | A kind of disease knowledge map construction method and plateform system, equipment, storage medium |
| CN109766445A (en) * | 2018-12-13 | 2019-05-17 | 平安科技（深圳）有限公司 | A kind of knowledge mapping construction method and data processing equipment |
| KR20210000952A (en) * | 2019-06-26 | 2021-01-06 | 주식회사 카카오 | Apparatus and method for knowledge graph indexing |
| CN110990584A (en) * | 2019-11-26 | 2020-04-10 | 口口相传(北京)网络技术有限公司 | Knowledge graph generation method and device |
| CN114064818A (en) * | 2021-11-23 | 2022-02-18 | 北京百度网讯科技有限公司 | Medical entity relationship determination method, training method, device, equipment and medium |
Non-Patent Citations (2)
| Title |
|---|
| 基于百科知识的医疗数据知识图谱构建;魏自强;郑伟伟;许永康;;网络安全技术与应用(第10期);全文 * |
| 电网企业运营指标知识图谱构建及其应用;李智星;万磊;周鸿雁;王广洁;邓琳;;农村电气化(第02期);全文 * |
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