Health education content management system for gynecological nursing

Abstract

The invention relates to the technical field of education management, in particular to a health education content management system for gynecological nursing, which is constructed by the following steps: S1, analyzing original medical content to generate a professional expression structure, and identifying a core medical entity and a logic relation chain between entities; s2, on the basis of the professional expression structure and the user cognition level label, outputting a cognition adaptation expression through a hierarchical conversion engine; and S3, binding the cognitive adaptation expression with a professional expression structure, and constructing a bidirectional version topological relation in a content storage library, so that a reversible mapping relation is formed between the professional expression and the adaptation expression. According to the method, not only is the insufficient adaptation of a general medical model in terms of gynecology ambiguity and multi-stage dependence overcome, but also the subsequent conversion process is ensured to keep consistent in terms, relationships and contexts, and an interpretable and traceable basic expression framework is provided for cognitive adaptation.

Description

Technical Field

[0001] The present invention relates to the technical field of education management, and in particular to a health education content management system for gynecological care. Background Art

[0002] With the continuous advancement of knowledge about gynecological diseases and the continuous strengthening of individual health awareness, the role of patient-oriented health education in the clinical nursing system is becoming increasingly prominent. In highly sensitive and specific scenarios, such as ectopic pregnancy risk management, gynecological health knowledge guidance, cervical cancer screening and prevention, and perioperative guidance for gynecological malignancies, the delivery of accurate, accessible, and credible health information is directly related to patient treatment compliance and the effectiveness of nursing interventions.

[0003] Most existing gynecological health education systems rely on general health knowledge bases or manually edited content, which has the following key technical problems:

[0004] Traditional health education content is presented in paragraph-style natural language, lacking structured annotations for medical entities (such as drugs, diseases, physiological indicators, etc.), and failing to establish semantic logical relationships such as causality, contraindications, and stage dependencies between entities, making it difficult to support subsequent automatic conversion or risk reasoning. Existing systems mostly use a set of static expressions for all users, without distinguishing between the ability of primary users to understand basic disease concepts and the needs of advanced users for information such as evidence-based evidence and risk identification. This can easily lead to a "double mismatch" of content that is too obscure or insufficient information. In the context of the dynamic evolution of medical knowledge (such as adjustments to the indications and contraindications of immunotherapy and targeted therapy drugs for gynecological malignancies, how to balance the relationship between disease treatment and fertility preservation in young patients with gynecological malignancies, and new discoveries in intervention methods to alleviate chemotherapy-related nausea and vomiting in patients with gynecological malignancies), the existing health content update mechanism relies on manual search and content replacement, which cannot accurately locate the affected content and is difficult to maintain synchronization and consistency between the professional version and the popular science version, easily leading to knowledge lags or invalid risk warnings. Summary of the Invention

[0005] The present invention provides a health education content management system for gynecological care, which is an intelligent content management system with medical structure modeling capabilities, cognitive layering adaptation capabilities, and two-way version linkage capabilities, so as to solve core problems such as information gaps between professional and popular expressions, update out-of-control, and risk imbalance.

[0006] A health education content management system for gynecological care, the construction of which includes:

[0007] S1: Parse original medical content to generate professional expression structures, identify core medical entities and the logical relationship chains between entities;

[0008] S2: Based on the professional expression structure and the user's cognitive level label, output the cognitive adaptation expression through the hierarchical conversion engine;

[0009] S3: Bind the cognitive adaptation expression with the professional expression structure, and construct a bidirectional version topology relationship in the content repository, so that the professional version expression and the adaptation version expression content form a reversible mapping relationship.

[0010] Optionally, the S1 specifically includes:

[0011] S11, using a gynecology dictionary and a medical named entity recognition model, annotating the core medical entities in the text, wherein the core medical entities include disease entities, drug entities, surgery entities, and physiological parameter entities;

[0012] S12, based on entity co-occurrence frequency analysis and grammatical dependency analysis, constructing a logical relationship chain between entities, wherein the logical relationship chain includes causal relationships, taboo relationships, and stage dependency relationships;

[0013] S13, uses the gynecological care rule library to strengthen the relationship chain weight and initiates mandatory relationship binding for the specified entity combination.

[0014] Optionally, the initiating of mandatory relationship binding for the specified entity combination includes: when the "pregnancy" and "radiological examination" entities appear at the same time, automatically adding a "taboo" relationship chain and marking the risk level.

[0015] Optionally, the entity co-occurrence frequency analysis includes scanning the processed gynecological medical text, counting whether the identified medical entity pairs in the text co-occur in the same sentence or paragraph, and the co-occurrence of each pair of entities is counted in a co-occurrence matrix, where each item in the matrix is ​​used to measure the semantic relevance between the two entities, the original co-occurrence counts are normalized, the co-occurrence frequency of the entity pairs is combined with their respective occurrence frequencies in the entire text to generate a symmetrical co-occurrence correlation index, and a co-occurrence threshold is set. When the co-occurrence correlation of a pair of entities is higher than the co-occurrence threshold, the two entities are determined to have a potential association relationship in clinical semantics and are marked as "relationship pairs to be analyzed."

[0016] Optionally, the dependency syntactic analysis includes analyzing the grammatical structure of the sentence containing the selected "relationship pairs to be analyzed" and extracting the dependency path between the two. The dependency path is a sequence of words connecting two entities and the relationship in the syntactic structure, revealing the semantic logic between the entities. Based on the keywords or dependency relationship types appearing in the path, it is determined whether there is a logical relationship between the two entities. Ultimately, all entity pairs that are determined to have a clear relationship are unified into a relationship graph. The relationship graph is used to represent the professional expression structure. The relationship graph includes:

[0017] Entity node set: represents all core medical entities that are identified and involved in relationship analysis;

[0018] Directed edge set: represents the specific logical relationship between any two entities.

[0019] Optionally, the S2 specifically includes:

[0020] S21, deconstructing the professional expression structure: extracting the core medical entities as anchor points and splitting the logical relationship chain into medical propositions;

[0021] S22, matching hierarchical rules: Activate the corresponding conversion rule set based on the user's cognitive level label:

[0022] S23, reorganized and adapted expression: Based on the output results of medical propositions, natural language expressions are generated according to the gynecological knowledge dissemination paradigm.

[0023] Optionally, activating a corresponding conversion rule set according to the user cognitive level label in S22 specifically includes:

[0024] Primary tags trigger term replacement rules: replace professional entities with pre-set general vocabulary mapping words;

[0025] Intermediate tag trigger logic simplification rules: node compression for complex relationship chains;

[0026] Advanced tags trigger evidence enhancement rules: an evidence level identifier is appended after the entity.

[0027] Optionally, S2 further includes embedding an interactive traceability interface pointing to the original professional expression structure, and embedding an interactive traceability link pointing to the original professional expression structure in each sentence.

[0028] Optionally, the S4 specifically includes:

[0029] S41, anchor point positioning: marking core medical entities as topological anchor points in the professional representation structure, and each anchor point is associated with a unique coordinate identifier;

[0030] S42, version binding: establishing a bidirectional pointer between the conversion element in the cognitive adaptation expression and the corresponding anchor point;

[0031] S43, Update Transmission: When the professional expression structure changes, the affected adaptive expression is located based on the anchor coordinates, triggering the automatic update of the gynecology rule verification:

[0032] If the change is non-conflicting, synchronize it directly to the adapted version;

[0033] If it is a conflicting change, freeze the adapted version and start the manual review process.

[0034] Optionally, the bidirectional pointer in the S42 version binding includes:

[0035] Professional version → Adaptive version pointer: record the term replacement path;

[0036] Adaptive version → Professional version pointer: Set the traceability hot zone.

[0037] Beneficial effects of the present invention:

[0038] The present invention, by combining a gynecology dictionary with a named entity recognition (NER) model, accurately identifies core medical entities such as diseases, drugs, and physiological parameters in the original content, and constructs a logical relationship chain between entities based on co-occurrence frequency and grammatical dependency analysis; further combined with gynecology-specific rules (such as taboo binding, tense modification, etc.), strengthens semantic logic and clinical adaptability, and generates a professional expression structure with a graph structure. This mechanism not only overcomes the lack of adaptation of general medical models in gynecological terminology ambiguity and multi-stage dependencies, but also ensures that the subsequent conversion process maintains consistency in the three dimensions of terminology, relationship, and context, providing an explainable and traceable basic expression framework for cognitive adaptation.

[0039] The hierarchical conversion engine designed in this invention generates cognitive adaptation expressions from atomic medical propositions based on user cognitive labels (such as primary, intermediate, and advanced) and dynamically matches term replacement, relationship chain simplification, and evidence enhancement rule sets. The introduction of a "sensitive term retention mechanism" and "gynecology-specific compression rules" lowers the information threshold while retaining core logic and risk warnings, avoiding oversimplification or misleading expressions. The interactive traceability interface embedded in the adaptive expression supports users to trace back to the original professional structure at any time, ensuring the controllability and transparency of information conversion. It is particularly suitable for high-risk scenarios such as drug contraindications and pregnancy operations.

[0040] This invention introduces a "topological anchor + bidirectional pointer + update perception" mechanism into the medical health education system: each core medical entity is bound to a unique coordinate as an anchor point, a bidirectional mapping pointer is established between the professional representation structure and the cognitive adaptation representation, and a change perception mechanism distinguishes between non-conflicting and conflicting content adjustments. When professional content changes, the system automatically identifies the scope of impact and accurately transmits it to the adaptation level. It supports switching between "direct synchronization" and "freeze pending review" strategies, achieving dynamic consistency maintenance of content dissemination and authoritative medical information. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A schematic diagram of a system construction process according to an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of a professional structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art may also implement some known technologies in other alternative ways. The accompanying drawings are only for describing the embodiments in more detail and are not intended to limit the present invention in any specific way.

[0045] like Figure 1-Figure 2 As shown, a health education content management system for gynecological care, the construction of the system includes:

[0046] S1: Parse original medical content to generate professional expression structures, identify core medical entities and the logical relationship chains between entities;

[0047] S2: Based on the professional expression structure and the user's cognitive level label, output the cognitive adaptation expression through the hierarchical conversion engine;

[0048] S3: Bind the cognitive adaptation expression with the professional expression structure, and construct a bidirectional version topology relationship in the content repository, so that the professional version expression and the adaptation version expression content form a reversible mapping relationship.

[0049] Said S1 specifically includes:

[0050] S11, Entity Recognition Stage: Using the gynecology dictionary and the medical named entity recognition (NER) model, the core entities in the original medical text are annotated. The core medical entities in the annotation results include:

[0051] Disease entity (e.g., "ectopic pregnancy");

[0052] Drug entity (e.g., "mifepristone");

[0053] The entity of the procedure (e.g., "hysteroscopy");

[0054] Physiological parameter entities (e.g., "basal body temperature");

[0055] The above output entity labels provide a node basis for relationship construction in the next stage.

[0056] The named entity recognition model structure uses the BiLSTM-CRF structure for named entity recognition, which is expressed as

[0057] Input:X=(x1,x2,...,x n );

[0058] EmbeddingLayer:E=Embed(X);

[0059] BiLSTM Encoding:H=BiLSTM(E);

[0060]

[0061] Among them, x i Represents the input of the i-th character or word, E is the embedding vector, which can be combined with medical word vector + dictionary enhancement, H represents the bidirectional context representation generated by BiLSTM, represents a set of possible label sequences, including B-disease, I-drug, O, etc. P(y|H) represents the maximum possible labeling path obtained by decoding the conditional random field (CRF);

[0062] The entity label set finally output by entity type classification includes: e i =(s i ,t i ,c i ); where e i Represents the i-th identified core medical entity object, which is an element in the entity label set, s i is the entity’s starting position, t i is the end position of the entity, c i represents the category label of the i-th identified medical entity, c i ∈{Disease,Drug,Surgery,PhysParam}, and:

[0063] Disease: disease entity (e.g., "ectopic pregnancy", "uterine fibroids");

[0064] Drug: drug entity (e.g., "mifepristone," "birth control pill");

[0065] Surgery: surgical entity (e.g., "hysteroscopy," "curettage");

[0066] PhysParam: Physiological parameter entity (such as "basal body temperature", "estrogen level");

[0067] S12, logical relationship chain construction stage: Based on the entity recognition results, combined with the entity co-occurrence frequency and grammatical dependency analysis algorithm, the logical relationship chain between entities is inferred and generated.

[0068] Typical relationship chain types include:

[0069] Causal relationship: such as HPV infection → cervical cancer;

[0070] Contraindications: e.g. estrogen drugs × history of thrombosis;

[0071] Estrogen medication × history of thrombosis;

[0072] Phase dependency: e.g. ovulation → basal body temperature rises;

[0073] This stage constructs a preliminary semantic map as the reinforcement input for the next stage.

[0074] S121, co-occurrence frequency analysis, statistics of entity co-occurrence relationships in medical texts, and construction of a symmetric co-occurrence matrix C:

[0075]

[0076] Among them, cofreq(e i ,e j ) represents entity e i With e j The number of times it appears in the same sentence / paragraph, freq(e i ) represents entity e i The frequency of occurrence, freq(e j ) represents entity e j The frequency of occurrence, C i,j ∈[0,1] represents the normalized co-occurrence correlation; set the co-occurrence threshold τ c , when C i,j >τ c When the entity is determined to have potential association, it is marked as a "relationship pair to be analyzed".

[0077] S122, dependency syntax analysis: Perform dependency syntax analysis on the sentence containing the identified entity and extract the relationship path between entities:

[0078] Among them, w k represents the kth word on the path, r k Indicates the type of dependency relationship between the kth word and its parent word, L is the total length of the path, DepPath(e i ,e j ) means from e i to e j The dependency syntactic path between

[0079] Construct a logical relationship chain, expressed as:

[0080]

[0081] Among them, R(e i ,e j ) represents entity e i and ej The logical relationship type between i ,e j Represents two identified medical entities. Causal indicates a causal relationship (e.g., "HPV infection causes cervical cancer"); Contraindication indicates a contraindication relationship (e.g., "a certain drug should not be used during breastfeeding"); Temporal / Phase indicates a time / phase relationship (e.g., "ovulation period → basal body temperature rises"); and None indicates that no clear semantic relationship is identified.

[0082] S123, preliminary semantic graph output: construct a relationship graph, that is, a professional expression structure: G = (V, E), where: V = {e1, ..., e n} is an entity node, E={(e i ,e j ,R(e i ,e j ))} is a directed edge that carries a logical relationship type.

[0083] S13, Gynecological nursing rule enhancement mechanism: Combine the gynecological nursing rule library to perform semantic enhancement on specific relationship chains, add professional semantic binding and risk modification, combine domain knowledge, and construct rule sets. The definition is as follows:

[0084] Enforce relationship binding rule expression:

[0085] Means: If entity e a Belongs to the set T a , entity e b Belongs to the set T b , and they appear in the same context (co-occurrence), then the logical relationship R(e) between them is automatically established. a , e b ), the relationship type is r k , and mark the risk level as lvl k , f k represents the kth mandatory rule in the field of gynecological care, e a ,e b Represents two identified medical entities, such as “pregnancy” and “radiology examination”, T a ,T b Represents a set of entity types, which represent the entity categories of interest to the rule, coexist(e a ,e b ) represents a conditional judgment, indicating whether the two entities co-occur in the same context (such as in a sentence or a paragraph), R(e a ,e b) represents the logical relationship between entities, such as "contraindication" or "causal" relationship. k Indicates the relationship type specified by the rule, lvl k is the risk level, which indicates the risk level of this relationship.

[0086] For example: T a ={Pregnancy}, indicating pregnancy-related entities;

[0087] T b ={RadiationExam}, indicating a radiological examination entity;

[0088] R(e a ,e b )=Contraindication,lvl k =High, meaning: If "pregnancy" and "CT examination" are detected in the same sentence:

[0089] Automatically add the relationship R(pregnancy, CT scan) = Contraindication

[0090] Set the risk level to High; after the rule is executed, insert or update edges into graph G:

[0091] E←E∪{(e a ,e b ,Contraindication, risk=High)}.

[0092] The risk level (High / Medium / Low) is preset in the mandatory relationship binding rules or obtained by dynamic evaluation:

[0093] Entity combination: pregnancy + radiological examination: High;

[0094] Entity combination: Lactation + Tetracycline: Medium;

[0095] Entity combination: Estrogen + History of thrombosis: High;

[0096] Entity combination: Cervical cancer screening + menstrual period: Low.

[0097] The S2 specifically includes:

[0098] S21, from the professional expression structure diagram G = (V, E) output from S1:

[0099] Take entity node e i ∈V is the anchor point; each logical edge (e i ,R(ei ,e j ),e j )∈E is disassembled into independent medical propositions for extraction, as a medical proposition p k , and join the collection The output medical proposition set is: p k =(e i ,R i,j ,e j ); that is, the most basic medical semantic unit extracted from the "professional expression structure" and available for subsequent conversion;

[0100] Among them, p k is a medical proposition (triple structure), e i ,e j is an entity pair, e i Indicates the starting entity (head entity), R i,j is the logical relationship between entities, e j Indicates the terminal entity (tail entity).

[0101] S22, matching hierarchical rules: Activate the cognitive adaptation rule set, set the user's cognitive level as label L u ∈{L basic ,L inter ,L adv}, respectively represent: elementary, intermediate, advanced, according to L u Activate a different rule set

[0102] S221, Primary Label (L u =L basic ):Term replacement rules have a mapping table M lex , defined as:

[0103] M lex :e i →e i '; the professional term e i Replaced with the easily understood expression e′ i ;

[0104] For example: M lex (“Endometriosis”) = “Abnormal menstrual tissue.”

[0105] S222, Intermediate Label (L u =L inter ):Logical chain simplification rules Let the relationship chain be:

[0106]

[0107] Execute the simplified function: Output a simplified chain that is approximately true (compressing the number of nodes but retaining the risk), as shown below:

[0108] Original chain: HPV infection → persistent infection → CIN lesions → cervical cancer;

[0109] After simplification:

[0110] S223, Advanced Label (L u =L adv ): Evidence enhancement rules add an evidence-based level identifier to each entity:

[0111] For example: Hormone replacement therapy → Hormone replacement therapy [Level I evidence].

[0112] S23, reorganized cognitive adaptation expression: according to the set of transformation propositions output by S22 Use the professional language reconstruction module to generate natural language sentences, namely cognitive adaptation expressions, and embed interactive traceability links pointing to the original structure diagram G in each sentence. In this way, users can view professional source information with one click while seeing simplified sentences.

[0113] For example:

[0114] If the professional proposition entered is: "Breastfeeding" - (taboo) - > "contraceptive pills";

[0115] After S2’s rule processing and terminology adaptation, the final output of cognitive adaptation is expressed as:

[0116] "Contraceptive pills are prohibited during breastfeeding (may harm the baby's liver)", and comes with a clickable "Contraceptive Pill Risks" link pointing to the corresponding data and medical evidence in the professional statement structure.

[0117] The S3 specifically includes:

[0118] S31, anchor point positioning: In the professional expression structure diagram G = (V, E), mark the core medical entity e i ∈V is a topological anchor point, each anchor point is bound to a unique three-dimensional coordinate: Coord(e i )=[C1,C2,C3[; where C1 is the medical category dimension, C2 is the clinical field dimension, and C3 is the application scenario dimension.

[0119] S32, version binding: define a bidirectional pointer structure, set the entity in the professional version representation to be e i , the conversion element in the adapted version is e′ i , then the binding relationship is:

[0120]

[0121] S33, dynamic update transmission: continuously monitor the updates of the professional structure diagram G, including the following events:

[0122] Additions and deletions to entity set V;

[0123] Addition and modification of relation set E;

[0124] Automatically identify the affected adaptation representation: Assume an anchor point e i Update, all binding pointers satisfy:

[0125] Pointer adapt→spec (e i ′)=Coord(e i ); that is, identify the elements containing the anchor mapping in all adapted versions of the content and make an update strategy decision.

[0126] Update rule logic:

[0127]

[0128] SYNC mode: Directly transfer the updated content to the adapted version;

[0129] FREEZE mode: locks the adaptation statement, marks it for review, and triggers manual verification.

[0130] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0131] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A health education content management system for gynecological care, characterized in that: The construction of the system includes: S1: Parse original medical content to generate professional expression structures, identify core medical entities and the logical relationship chains between entities; S2: Based on the professional expression structure and the user's cognitive level label, output the cognitive adaptation expression through the hierarchical conversion engine; S3: Bind the cognitive adaptation expression with the professional expression structure, and construct a bidirectional version topology relationship in the content repository, so that the professional version expression and the adaptation version expression content form a reversible mapping relationship.

2. A health education content management system for gynecological care according to claim 1, characterized in that: Said S1 specifically includes: S11, using a gynecology dictionary and a medical named entity recognition model, annotating the core medical entities in the text, wherein the core medical entities include disease entities, drug entities, surgery entities, and physiological parameter entities; S12, based on entity co-occurrence frequency analysis and grammatical dependency analysis, constructing a logical relationship chain between entities, wherein the logical relationship chain includes causal relationships, taboo relationships, and stage dependency relationships; S13, uses the gynecological care rule library to strengthen the relationship chain weight and initiates mandatory relationship binding for the specified entity combination.

3. A health education content management system for gynecological care according to claim 2, characterized in that: The initiation of mandatory relationship binding for the specified entity combination includes: when the "pregnancy" and "radiological examination" entities appear at the same time, automatically adding a "taboo" relationship chain and marking the risk level.

4. A health education content management system for gynecological care according to claim 2, characterized in that: The entity co-occurrence frequency analysis includes scanning the processed gynecological medical text, counting whether the identified medical entity pairs in the text co-occur in the same sentence or paragraph, and counting the co-occurrence of each pair of entities into a co-occurrence matrix, where each item in the matrix is ​​used to measure the semantic relevance between the two entities. The original co-occurrence count is normalized, and the co-occurrence frequency of the entity pairs is combined with their respective occurrence frequencies in the entire text to generate a symmetrical co-occurrence correlation index. A co-occurrence threshold is set. When the co-occurrence correlation of a pair of entities is higher than the co-occurrence threshold, the two entities are determined to have a potential association relationship in clinical semantics and are marked as a "relationship pair to be analyzed." 5. A health education content management system for gynecological care according to claim 4, characterized in that: The dependency syntactic analysis involves analyzing the grammatical structure of the sentences containing the selected "relationship pairs to be analyzed" and extracting the dependency path between the two. The dependency path is the sequence of words connecting two entities and the relationship in the syntactic structure, revealing the semantic logic between the entities. Based on the keywords or dependency relationship types appearing in the path, it is determined whether there is a logical relationship between the two entities. Ultimately, all entity pairs determined to have a clear relationship are unified into a relationship graph. The relationship graph is used to represent the professional expression structure. The relationship graph includes: Entity node set: represents all core medical entities that are identified and involved in relationship analysis; Directed edge set: represents the specific logical relationship between any two entities.

6. A health education content management system for gynecological care according to claim 1, characterized in that: The S2 specifically includes: S21, deconstructing the professional expression structure: extracting the core medical entities as anchor points and splitting the logical relationship chain into medical propositions; S22, matching hierarchical rules: Activate the corresponding conversion rule set based on the user's cognitive level label: S23, reorganized and adapted expression: Based on the output results of medical propositions, natural language expressions are generated according to the gynecological knowledge dissemination paradigm.

7. A health education content management system for gynecological care according to claim 6, characterized in that: The activation of the corresponding conversion rule set according to the user's cognitive level label in S22 specifically includes: Primary tags trigger term replacement rules: replace professional entities with pre-set general vocabulary mapping words; Intermediate tag trigger logic simplification rules: node compression for complex relationship chains; Advanced tags trigger evidence enhancement rules: an evidence level identifier is appended after the entity.

8. A health education content management system for gynecological care according to claim 7, characterized in that: The S2 also includes embedding an interactive traceability interface pointing to the original professional expression structure, and embedding an interactive traceability link pointing to the original professional expression structure in each sentence.

9. A health education content management system for gynecological care according to claim 1, characterized in that: The S4 specifically includes: S41, anchor point positioning: marking core medical entities as topological anchor points in the professional representation structure, and each anchor point is associated with a unique coordinate identifier; S42, version binding: establishing a bidirectional pointer between the conversion element in the cognitive adaptation expression and the corresponding anchor point; S43, Update Transmission: When the professional expression structure changes, the affected adaptive expression is located based on the anchor coordinates, triggering the automatic update of the gynecology rule verification: If the change is non-conflicting, synchronize it directly to the adapted version; If it is a conflicting change, freeze the adapted version and start the manual review process.

10. A health education content management system for gynecological care according to claim 9, characterized in that: The bidirectional pointers in the S42 version binding include: Professional version → Adaptive version pointer: record the term replacement path; Adaptive version → Professional version pointer: Set the traceability hot zone.

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