CN111460173B - Method for constructing disease ontology model of thyroid cancer - Google Patents
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Abstract
The invention discloses a method for constructing a disease ontology model of thyroid cancer, which comprises the following steps: s100, collecting information; s200, preprocessing information, unifying entities with different names in the information from different sources, and performing semantic extraction on the information to obtain thyroid cancer data; s300, classifying thyroid cancer data, including entity data and entity relation data; s400, screening and filtering the entity data to obtain basic entity data; s500, constructing a thyroid cancer disease ontology model according to the entity relation data obtained in S300 based on the basic entity data; s600, mapping the thyroid cancer disease ontology model to MeSH. The invention systematically arranges and analyzes the disease knowledge of thyroid cancer, determines the disease core concept and the attribute, semantic relation and domain knowledge axiom thereof, establishes a Chinese-English corresponding ontology concept framework system model in the thyroid cancer disease field and creates a precondition for constructing a thyroid cancer disease knowledge base system.
Description
Technical Field
The invention relates to the field of disease ontology models, in particular to a method for constructing a disease ontology model of thyroid cancer.
Background
Compared with other types of knowledge, the medical knowledge has the characteristics of time sequence, concept diversity, high consistency requirement, short half life, huge complexity, knowledge diversity, ambiguity and the like. The traditional knowledge organization method limits the research on medical knowledge to a certain medical professional range, is in a matrix, splits the medical knowledge which is related to each other in essence, becomes an obstacle for people to acquire information comprehensively and accurately, and is not convenient for the comprehensive utilization of the medical knowledge. Therefore, how to define, analyze and identify the content features of the medical knowledge unit and the complex semantic relations thereof deeply; how to link, integrate and recombine knowledge units and fragments thereof with different carrier forms and structures so as to enable medical knowledge to reasonably meet the information requirements of people and serve clinical application, new drug research and development and the like, which are important problems faced by information organizers and need to be solved, and more important problems which are generally concerned about and need to be solved in the pharmaceutical industry.
The existing internet search engine generally realizes retrieval based on keywords and a retrieval mechanism based on a classified directory, the information retrieval mechanism of the search engine participates in matching the external form of characters instead of the concepts expressed by the characters, the accurate matching of contents cannot be ensured, the precision ratio and the recall ratio are not high, the capability of supporting semantic matching is poor, the semantic understanding is incomplete, a personalized service mechanism cannot be provided well according to the interests and preferences of users, and meanwhile, the retrieval result cannot be reused and shared by intelligent processing software. Therefore, the current information retrieval mechanism has serious shortcomings, and needs to be improved.
Disease Ontology (DO), which is a Disease knowledge base constructed from Disease terms of medical knowledge base, has been developed as a standardized Ontology of human diseases, aiming to provide consistent, reusable and sustainable descriptions of human Disease terms, disease phenotypic features and related medical vocabulary concepts in the biomedical field. The method aims to obtain the semantics which can be interpreted by a computer from the unstructured text through concept recognition and semantic relation definition. The disease ontology is mainly used for describing the concepts related to diseases and the relationship among the concepts, or the basic theory related to diseases, and is a bottom-layer framework for searching, intelligently answering and searching and classifying disease information. By taking the current state of research at home and abroad into consideration, the application research on the body of the medical field is still in a continuous development stage at present, and the foreign research focuses on constructing a standardized tool of the medical field and expressing the concept and semantic type of the medical term; research of ontology in domestic medical field is combined with knowledge base and medical information system closely, and it is tried to construct ontology-based medical knowledge base with reasoning function. Because the disease ontology relates to a wide scope, has strong specialization, needs multidisciplinary cooperation, has long construction period and great construction difficulty, the international existing disease ontology is mostly an upper-layer ontology, such as a degenerative disease ontology, a rare disease ontology, an infectious disease ontology, an oral disease ontology and the like, more abstract semantics in the specialty of the disease ontology are described, and the clinical practicability is not high. The existing terminal body only has a few disease bodies such as a Parkinson disease body, an Alzheimer disease body, chronic nephropathy and the like, and does not have a thyroid disease body.
Disclosure of Invention
The invention aims to provide a method for constructing a thyroid cancer disease ontology model, which is used for carrying out system arrangement on thyroid cancer disease knowledge and reanalyzing a knowledge system structure, determining the attributes, semantic relations and domain knowledge axioms of a disease core concept and a core concept set, establishing a thyroid cancer disease Chinese-English corresponding ontology concept framework system model and creating a precondition for constructing a thyroid cancer disease knowledge base system.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
the invention discloses a method for constructing a disease ontology model of thyroid cancer, which comprises the following steps:
s100, thyroid cancer disease information collection;
s200, information preprocessing, namely unifying entities with different names in information from different sources, and performing semantic extraction on the information to obtain thyroid cancer data;
s300, classifying thyroid cancer data, including entity data and entity relation data;
s400, screening and filtering the entity data to obtain basic entity data;
s500, constructing a thyroid cancer disease ontology model according to the entity relation data obtained in the S300 based on the basic entity data;
s600, mapping the thyroid cancer disease ontology model to MeSH.
Preferably, in step S100, the information sources include thyroid anatomy knowledge, pathology knowledge, clinical diagnosis and treatment information, thyroid cancer-related guidelines, expert consensus, books, published documents, and periodicals.
Preferably, in step S200, the NCBI GENE symbol is used as a representative term of GENE/PROTEIN, and the code of the NCI disease vocabulary is used as a unique index of the disease vocabulary to unify the names.
Preferably, in step S200, semantic extraction is performed using machine learning.
Preferably, in step S300, the entity relationship class data includes an inheritance relationship and an instance relationship.
Preferably, the basic entity data obtained in step S400 includes definitions, codes, acronyms and synonyms, and network link addresses.
Preferably, the step S600 includes the steps of,
s610, mapping the vocabulary of the thyroid cancer disease ontology model to MeSH according to the reference attribute of the thyroid cancer disease ontology model;
s620, comparing synonyms in the thyroid cancer disease ontology model with synonyms of MeSH,
if there is a match then it is mapped onto MeSH,
and if not, mapping to the nearest parent node through the inheritance relationship.
Preferably, in step S500, the thyroid cancer disease ontology model has a tree-like hierarchical structure.
The invention has the beneficial effects that:
1. the invention is helpful for solving the problems that the same concept has different vocabulary expressions and the same vocabulary has different connotations in the thyroid cancer disease field, and solving the standardization problem of the concept in the thyroid cancer disease field;
2. the invention can improve the ability of reading and understanding relevant words and semantics such as thyroid cancer disease knowledge asked by a user by a computer program, help the user to retrieve and acquire relevant disease knowledge, achieve sharing between people and machines, and realize the rediscovery of knowledge and the reuse of thyroid cancer disease field knowledge;
3. the invention provides more direct, convenient and accurate thyroid cancer disease knowledge for clinicians, health management decision makers, medical education and the like, so that the thyroid cancer disease knowledge can play a greater economic value and social value.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of an extrusion hierarchy framework of an ontology model of thyroid cancer disease;
FIG. 3 is a schematic view of the overall structure of the thyroid cancer disease body;
FIG. 4 is a schematic representation of the descriptive information for differentiated thyroid cancer.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings.
As shown in fig. 1 and 2, the present invention includes the following steps:
s100, thyroid cancer disease information is collected, and information sources comprise thyroid anatomy knowledge, pathological knowledge, clinical diagnosis and treatment information, thyroid cancer related guidelines, expert consensus, books, published documents and periodicals;
s200, information preprocessing, namely unifying entities with different names in information from different sources, adopting NCBI GENE symbols as representing nouns of GENE/PROTEIN, adopting codes of NCI disease vocabularies as unique marks of the disease vocabularies for name unification,
the information is semantically extracted using machine learning,
obtaining thyroid cancer data;
s300, classifying the thyroid cancer data, including entity data and entity relation data,
the entity relationship class data comprises inheritance relationship and instance relationship;
s400, screening and filtering the entity data to obtain basic entity data,
the basic entity data comprises definitions, codes, abbreviations, synonyms and network link addresses;
s500, constructing a thyroid cancer disease ontology model according to the entity relation data obtained in the S300 based on the basic entity data;
s600, mapping the thyroid cancer disease ontology model to MeSH, and comprising the following steps:
s610, mapping the vocabulary of the thyroid cancer disease ontology model to MeSH according to the reference attribute of the thyroid cancer disease ontology model;
s620, comparing synonyms in the thyroid cancer disease ontology model with synonyms of MeSH,
if there is a match then the mapping is to MeSH,
and if not, mapping to the nearest parent node through the inheritance relationship.
When the utility model is used in practice,
and constructing a comprehensive thyroid cancer field ontology model by using an ontology construction tool protege, and continuously correcting and expanding the constructed thyroid cancer disease ontology concept class and attribute along with version updating iteration.
The thyroid disease ontology concept and relationship sources comprise thyroid cancer clinical guidelines, thyroid cancer expert consensus, thyroid cancer related published articles and thyroid cancer books at home and abroad. Knowledge sources are collected, analyzed, discussed and summarized by tissue thyroid surgery experts, pathologists and thyroid cancer basic researchers.
By combining the prior knowledge of thyroid gland anatomy, pathology, clinical diagnosis and treatment and the like, the names of different entities in the information of different data sources in the same thyroid cancer field are unified based on relevant guidelines, expert consensus, books and published articles of thyroid cancer at home and abroad.
The NCBI GENE symbols are used as the representative nouns of GENE/PROTECTIN, and the codes of NCI disease vocabularies are used as the unique marks of the disease vocabularies for name unification.
Semantic relation extraction: relevant guidelines, expert consensus, books and published article contents of thyroid cancer at home and abroad are extracted, dependency syntactic analysis is carried out, the relation among specific entities in all sentences is obtained, and then semantic relation is obtained.
Thyroid cancer disease ontology data is divided into entity type and entity relationship type
Entity: including definitions (definition), codes (NCI _ code), acronyms and synonyms (synonyms), links (URLs, data description reference sources for data with reference elements), etc.
The thyroid cancer disease ontology establishes semantic relations and a tree-like hierarchical structure, establishes inheritance relations such as is _ a and subiclass _ of, and establishes instance relations such as instance _ of relation.
According to clinical guidelines for thyroid cancer, consensus among thyroid cancer experts, published articles related to thyroid cancer, books for thyroid cancer, and discussion results of thyroid surgery experts, pathologists and thyroid cancer basic researchers, a basic hierarchical framework of a thyroid cancer disease book shown in fig. 2 is obtained.
And adding the rest contents on the basic frame of the body according to the semantic relation among the entities obtained in the previous step, thereby obtaining the body in the thyroid cancer disease field.
Mapping the thyroid cancer disease body to Medical Subject reading (MeSH), specifically comprising: mapping the words to MeSH through cross-reference attribute of thyroid cancer disease body; mapping onto MeSH if there is a match by comparing synonyms in the thyroid cancer disease ontology with synonyms of MeSH; mapping to the nearest parent node through the inheritance relationship.
The following examples describe the specific definition and content of the thyroid cancer disease entity and one of its entities, but it should be understood by those skilled in the art that this is by way of illustration only and that the scope of the invention is defined by the appended claims, and those skilled in the art can make various changes or modifications to these embodiments without departing from the principle and spirit of the invention as it is established in the thyroid field and entity, and such changes and modifications fall within the scope of the invention.
As shown in fig. 3, through research and reading of 14 english guidelines for thyroid cancer, 26 chinese guidelines and expert consensus, thyroid cancer english-chinese book and pubmed database thyroid cancer-related literature, and periodic conference discussion of thyroid surgery experts, pathologists, thyroid cancer basic researchers, 578 thyroid cancer information metadata are extracted, gene/Protein symbols and disease vocabulary codes are searched through https:// www.ncbi.nlm.nih.gov/and https:// bioport.bionomics.org/website, and synonyms and abbreviations are merged to unify names.
Through the extraction of the semantic relation of the document data and the semantic analysis of experts, the tree hierarchy establishes the inheritance relation (is _ a, subclass _ of relation) and the instance relation (instance _ of relation). The body has a maximum depth of 8 and a width of 29.
As shown in fig. 4, taking "Differentiated Thyroid Carcinoma (Differentiated Thyroid Carcinoma) as an example, code C7153 was found at https:// biooral. (ii) An adjacent specific presenting from the resonant oil and developing extended event of a refractory cell differential. Recording to the nuclear defects of the refractory cells, an is classified heat as pallet, or a refractory cell; synonyms are: differentiated Thyroid Cancer; differentiated thyreoid cardamoma; differentiated Thyroid Gland Cancer; thyroid Gland Differentiated Carcinoma; the URL is: https:// ncit.nci.nih.gov/ncitbrowser/conceptreport.jsp? dictionary = NCI _ theracurus & n s = exit & code = C7153.
The root of Thyroid Cancer disease body is Thyroid Cancer (Thyroid Cancer), and the semantic relationship of Differentiated Thyroid Cancer (Differentiated Thyroid Cancer) is as follows: the subclasses of "Differentiated Thyroid cancer (Differentiated Thyroid Gland cancer) include" Thyroid Papillary Carcinoma "(Thyroid Gland Papillary Carcinoma)," Thyroid Follicular Carcinoma "(Thyroid Gland cancer) and" Thyroid Gland Hurthle Cell Carcinoma "(Thyroid Gland cancer and Hurthle Cell Carcinoma); the father of "Differentiated Thyroid cancer (Differentiated Thyroid cancer) is" Pathological Classification ", and the father of" Pathological Classification "is" Pathology ", and is located under the root.
The present invention is capable of other embodiments, and various changes and modifications can be made by one skilled in the art without departing from the spirit and scope of the invention.
Claims (6)
1. A method for constructing a disease ontology model of thyroid cancer is characterized by comprising the following steps:
s100, thyroid cancer disease information collection;
s200, information preprocessing, namely unifying entities with different names in information from different sources, and performing semantic extraction on the information to obtain thyroid cancer data;
s300, classifying thyroid cancer data, including entity data and entity relation data;
s400, screening and filtering the entity data to obtain basic entity data;
s500, constructing a thyroid cancer disease ontology model according to the entity relation data obtained in the S300 based on the basic entity data;
s600, mapping the thyroid cancer disease ontology model to MeSH;
in step S500, the thyroid cancer disease ontology model is of a tree-like hierarchical structure;
step S600 includes the following steps:
s610, mapping the vocabulary of the thyroid cancer disease ontology model to MeSH according to the reference attribute of the thyroid cancer disease ontology model;
s620, comparing synonyms in the thyroid cancer disease ontology model with synonyms of MeSH,
if there is a match then the mapping is to MeSH,
and if not, mapping to the nearest parent node through the inheritance relationship.
2. The construction method according to claim 1, characterized in that: in step S100, the information sources include thyroid anatomy knowledge, pathological knowledge, clinical diagnosis and treatment information, thyroid cancer-related guidelines, expert consensus, books, published documents, and periodicals.
3. The construction method according to claim 2, wherein: in step S200, the NCBI GENE symbol is used as a representative noun of GENE/PROTEIN, and the code of NCI disease vocabulary is used as a unique mark of the disease vocabulary for name unification.
4. The construction method according to claim 3, wherein: in step S200, semantic extraction is performed using machine learning.
5. The construction method according to claim 1, characterized in that: in step S300, the entity relationship class data includes an inheritance relationship and an instance relationship.
6. The construction method according to claim 1, characterized in that: the basic entity data obtained in step S400 includes definitions, codes, abbreviations and synonyms, and network link addresses.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011143361A2 (en) * | 2010-05-11 | 2011-11-17 | Veracyte, Inc. | Methods and compositions for diagnosing conditions |
| CN106780448A (en) * | 2016-12-05 | 2017-05-31 | 清华大学 | A kind of pernicious sorting technique of ultrasonic Benign Thyroid Nodules based on transfer learning Yu Fusion Features |
| CN108428478A (en) * | 2018-02-27 | 2018-08-21 | 东北师范大学 | The thyroid cancer Risk Forecast Method excavated based on heterogeneous medical data |
| CN110069639A (en) * | 2019-04-01 | 2019-07-30 | 东华大学 | A method of building Thyroid ultrasound domain body |
| CN110335668A (en) * | 2019-05-22 | 2019-10-15 | 台州市中心医院（台州学院附属医院） | Thyroid cancer cell pathological map auxiliary analysis method and system based on deep learning |
Family Cites Families (1)
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011143361A2 (en) * | 2010-05-11 | 2011-11-17 | Veracyte, Inc. | Methods and compositions for diagnosing conditions |
| CN106780448A (en) * | 2016-12-05 | 2017-05-31 | 清华大学 | A kind of pernicious sorting technique of ultrasonic Benign Thyroid Nodules based on transfer learning Yu Fusion Features |
| CN108428478A (en) * | 2018-02-27 | 2018-08-21 | 东北师范大学 | The thyroid cancer Risk Forecast Method excavated based on heterogeneous medical data |
| CN110069639A (en) * | 2019-04-01 | 2019-07-30 | 东华大学 | A method of building Thyroid ultrasound domain body |
| CN110335668A (en) * | 2019-05-22 | 2019-10-15 | 台州市中心医院（台州学院附属医院） | Thyroid cancer cell pathological map auxiliary analysis method and system based on deep learning |
Non-Patent Citations (3)
| Title |
|---|
| E. Hoseinian-Azghadi ; .Development of a 9-months pregnant hybrid phantom and its internal dosimetry for thyroid agents.《Journal of Radiation Research》.2014, * |
| 人类肝脏疾病本体的构建及其应用;杨春媛;《医药卫生科技辑》;20141115;全文 * |
| 甲状腺癌动物模型建立研究的现状与展望;胡以国；罗晗；朱精强;《中国普外基础与临床杂志》;20181025;全文 * |
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