Method and device for writing fully-structured electronic medical record based on standardized medical terms

Abstract

The invention discloses a method and a device for writing a fully-structured electronic medical record based on standardized medical terms. A server comprises the following steps: receiving a structured electronic medical record text to be analyzed; analyzing the electronic medical record text into medical term values of data elements with meanings based on a standardized medical term platform; intelligent matching of medical terms is carried out through the incidence relation between the minimum data elements; and sending the matched medical terms to a client. According to the method, the fully-structured electronic medical record content is generated based on the medical terms of the minimum data element unit, so that a doctor can conveniently input the medical record content, the efficiency of inputting the electronic medical record content by the doctor is greatly improved, and the medical data of a patient can be conveniently reused around the fully-structured electronic medical record and become data of teaching and scientific research, so that the medical data of the patient can be used for teaching and scientific research. The patient data can be conveniently analyzed and mined, so that a basis is provided for medical research.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic medical records, and in particular relates to a method and device for writing fully structured electronic medical records based on standardized medical terminology. Background Art

[0002] Previous medical record systems were all about writing text and modifying local word choices. The disadvantages of this writing method are that it is not standard, comprehensive, or standardized. Moreover, electronic medical records are semi-structured, and most of the content is pure long text like Word. The connotation data of the text content cannot be used in real time, and the small part of the structured content also relies on the program to embed points in advance, which lacks system flexibility. Summary of the invention

[0003] The purpose of the present invention is to overcome the above problems and provide a method and device for writing fully structured electronic medical records based on standardized medical terminology.

[0004] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0005] The method for writing fully structured electronic medical records based on standardized medical terminology includes the following steps on the server side:

[0006] Receive structured electronic medical record text to be parsed;

[0007] Based on a standardized medical terminology platform, the electronic medical record text is parsed into medical terminology values ​​of data elements with meanings; wherein, based on a standardized medical terminology platform, the electronic medical record text is parsed into medical terminology values ​​of data elements with meanings comprises: parsing into multiple data elements with meanings based on a standardized medical terminology platform; analyzing and comparing the sizes of multiple data elements, and selecting the smallest data element;

[0008] Intelligently matching medical terms through the association relationship between the minimum data elements;

[0009] The matched medical terms are sent to the client.

[0010] As a further improvement, the intelligent matching of medical terms through the association relationship between the minimum data elements includes the following steps:

[0011] Based on the minimum data elements, complex medical terms are matched and combined.

[0012] As a further improvement, the parsing into multiple data elements with meanings based on the standardized medical terminology platform includes the following steps:

[0013] Performing word segmentation analysis on the structured electronic medical record text to be analyzed by a word segmentation engine to obtain structured analyzed word segments;

[0014] The medical terms corresponding to the structured parsed word segmentation are searched from the medical term library in sequence through a matching algorithm to obtain a plurality of data elements with meanings.

[0015] As a further improvement, the analyzing and comparing the sizes of the plurality of data elements to select the smallest data element comprises the following steps:

[0016] Segment the electronic medical record text from back to front in sequence to obtain a plurality of characters;

[0017] Match the characters with the elements in the data set; wherein,

[0018] If the match is successful, take the next character and continue to match it with the elements in the data set;

[0019] If the match is unsuccessful, the character and the position of the character in the electronic medical record text are recorded.

[0020] As a further improvement, the elements in the data set are a set containing the minimum data element.

[0021] As a further improvement, the minimum data element at least includes:

[0022] At least one symptom, punctuation, symptom duration, and time unit.

[0023] As a further improvement, when matching and combining into complex medical terms based on the minimum data element, if the user inputs a numerical value, it is skipped.

[0024] As a further improvement, when the standardized medical terminology platform is parsed into multiple data elements with meanings, if no corresponding data element is matched in the data set during parsing, a null value is returned and the corresponding electronic medical record text is sent to the client.

[0025] On the other hand, a method for writing a fully structured electronic medical record based on standardized medical terminology is provided, and the client includes the following steps:

[0026] Send structured text to be parsed to the server;

[0027] The medical terms matched by the server are received, and the correct medical terms are screened out from the medical terms.

[0028] On the other hand, the present invention also provides a system for writing fully structured electronic medical records based on standardized medical terminology, the system comprising at least one processor and a memory storing instructions, which, when executed by at least one processor, implements the method in the above technical solution.

[0029] A computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the steps of the above technical solution.

[0030] A computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the method described in the steps of the above technical solution is implemented.

[0031] The beneficial effects of the present invention are:

[0032] The present invention generates fully structured electronic medical record content based on medical terms in the smallest data element unit, which not only facilitates doctors to enter medical record content, but also greatly improves the efficiency of doctors entering electronic medical record content. Focusing on the fully structured electronic medical record, it is convenient to reuse patient medical data and make it a material for teaching and scientific research, and to analyze and mine patient data, thereby providing a basis for medical research; giving full play to the value of massive data in electronic medical records, and assisting in clinical decision-making assistance, medical record quality management, medical quality management, and scientific research data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention has the following accompanying drawings:

[0034] Figure 1 is a flowchart of a method for writing a fully structured electronic medical record based on standardized medical terminology according to some specific embodiments of the present invention;

[0035] Figure 2 is a schematic diagram of a structure of an apparatus for writing a fully structured electronic medical record based on standardized medical terminology according to some specific embodiments of the present invention;

[0036] Figure 3 It is a flowchart of the client and server of the method for writing a fully structured electronic medical record based on standardized medical terminology of the present invention;

[0037] Figure 4 A schematic diagram of a system for writing fully structured electronic medical records based on standardized medical terminology in the present invention. DETAILED DESCRIPTION

[0038] The following examples further illustrate the content of the present invention, but should not be construed as limiting the present invention. Without departing from the spirit and substance of the present invention, modifications or substitutions made to the methods, steps or conditions of the present invention all fall within the scope of the present invention.

[0039] Embodiment 1:

[0040] like Figure 1 , 3 As shown, the present invention provides a method for writing a fully structured electronic medical record based on standardized medical terminology, and the server includes the following steps:

[0041] Step S11: receiving structured electronic medical record text to be parsed;

[0042] Step S12: parsing the electronic medical record text into medical terminology values ​​of data elements with meanings based on a standardized medical terminology platform; wherein the parsing of the electronic medical record text into medical terminology values ​​of data elements with meanings based on a standardized medical terminology platform comprises: parsing into multiple data elements with meanings based on a standardized medical terminology platform; analyzing and comparing the sizes of multiple data elements to select the smallest data element;

[0043] Step S13: Intelligently matching medical terms based on the association relationship between the minimum data elements;

[0044] Step S14: sending the matched medical terms to the client.

[0045] For example, text structuring relies on a preset combination of medical terms. The term combination is composed of multiple data elements, which are also the basic components of medical records. For example, "fever for 3 days" can be decomposed into a combination of three data elements: symptoms, duration, and time unit. The symptom data element is a selective data element, which is associated with a symptom dictionary (also known as medical terminology, and is still referred to as a dictionary below to avoid confusion). The duration is a numerical data element, and the time unit is a selective data element, which is associated with a time unit dictionary. The term combination is represented in the program as a combination of a drop-down option, a number box, and a text box.

[0046] Intelligently matching medical terms through the association relationship between the minimum data elements includes the following steps:

[0047] Based on the minimum data elements, complex medical terms are matched and combined.

[0048] In the present invention, the dictionary cannot be used directly for writing and must be bound to a selective data element. The same dictionary can be bound to different data elements to express different meanings. For example, the administrative division dictionary can be bound to the current address data element or to the work address data element to express different purposes.

[0049] Parsing into multiple data elements with meaning based on the standardized medical terminology platform includes the following steps:

[0050] Performing word segmentation analysis on the structured electronic medical record text to be analyzed by a word segmentation engine to obtain structured analyzed word segments;

[0051] The medical terms corresponding to the structured parsed word segmentation are searched from the medical term library in sequence through a matching algorithm to obtain a plurality of data elements with meanings.

[0052] For example, in the initialization phase of the program, all data elements that can be used for writing purposes 1 and have been referenced by term combinations are uniformly imported into the memory to form a set of elements (elements are essentially dictionary values ​​+ data elements). Text, numeric, date and time, and binary types will not be imported. For Boolean data elements, they are also imported as two element types, one for "with XXX" and the other for "without XXX". For dictionary data elements, multiple elements corresponding to the dictionary values ​​are imported. The text input by the user is decomposed by matching the elements in the set.

[0053] Analyzing and comparing the sizes of the plurality of data elements to select the smallest data element comprises the following steps:

[0054] Segment the electronic medical record text from back to front in sequence to obtain a plurality of characters;

[0055] Match the characters with the elements in the data set; wherein,

[0056] If the match is successful, take the next character and continue to match it with the elements in the data set;

[0057] If the match is unsuccessful, the character and the position of the character in the electronic medical record text are recorded.

[0058] For example, after receiving the content, the backend segments the text content from back to front, taking one more character each time and matching it with the elements in the set. If no match is found, another character is taken for matching. If a match is found, the element is recorded, and the startOffset and endOffset of the element in the text content are recorded, and the record type is 1.

[0059] The date format of yyyy-MM-dd entered by the user (regular match) will also be recorded as an element with type 2.

[0060] For numeric types (regular matching) entered by the user, skip directly.

[0061] If the decomposition fails and the resulting set is empty, the empty set is returned directly and the front end processes it directly as a text data element.

[0062] After completing all the decompositions, you will get a set of decomposed elements (M) with a large number of redundant ones. Use a recursive method, combined with the recorded startOffset and endOffset. Combine the sets into multiple reasonably spliced ​​sets (m). When the text content is too long, the number of combinations may be large. At this time, considering that the actual elements of the term combination are not many, only 1,000 are taken first, and only sets with less than or equal to 8 internal elements are considered (there are almost no term combinations that can match them if there are more than 8). Then, the elements in the set m are spliced ​​and reversed, which means that the content closest to the user input is placed at the front.

[0063] In some embodiments of the present invention, the elements in the data set are a set including the minimum data element.

[0064] In some embodiments of the present invention, the minimum data element includes at least:

[0065] At least one symptom, punctuation, symptom duration, and time unit.

[0066] In some embodiments of the present invention, when matching and combining into complex medical terms based on the minimum data elements, if the user inputs a numerical value, it is skipped.

[0067] For example, text concatenation and inverted sorting mean that the content closest to the user input is placed at the front. If there is no such collection, an empty collection is returned directly, and the front end directly processes it as a text data element. In the actual implementation process, it was found that 1,000 collections put a lot of pressure on the server, so only the first 8 collections are still retained for subsequent processing. For each collection m, it is traversed, and the dictionary value set Ai associated with each element is extracted. If it is a date type element, the database is queried to obtain all date type data elements that can be used for writing purposes as a collection. For example, a doctor enters a sentence such as: dry mouth, polydipsia for 4 years, and the system automatically parses it into fully structured content: dry mouth [symptoms], [punctuation] polydipsia [symptoms] 4 [symptom duration] years [time unit].

[0068] In some embodiments of the present invention, when the standardized medical terminology platform is parsed into multiple data elements with meanings, if no corresponding data element is matched in the data set during parsing, a null value is returned and the corresponding electronic medical record text is sent to the client.

[0069] On the other hand, a method for writing a fully structured electronic medical record based on standardized medical terminology is provided, and the client includes the following steps:

[0070] Send structured text to be parsed to the server;

[0071] The medical terms matched by the server are received, and the correct medical terms are screened out from the medical terms.

[0072] Embodiment 2:

[0073] like Figure 2 As shown, a device for writing fully structured electronic medical records based on standardized medical terminology, the server side includes:

[0074] A first receiving unit is used to receive a structured electronic medical record text to be parsed;

[0075] The parsing unit is used to parse the electronic medical record text into medical terminology values ​​of data elements with meanings based on a standardized medical terminology platform; wherein the parsing of the electronic medical record text into medical terminology values ​​of data elements with meanings based on a standardized medical terminology platform comprises: parsing into multiple data elements with meanings based on a standardized medical terminology platform; analyzing and comparing the sizes of multiple data elements to select the smallest data element;

[0076] A matching unit, used for intelligently matching medical terms based on the association relationship between the minimum data elements;

[0077] The sending unit is used to send the matched medical terms to the client.

[0078] For example, in one embodiment, the following technology is used to parse and split the structured electronic medical record text into the smallest data elements, and then synthesize the smallest data elements into complex medical terms and send them to the front end: The task of text structuring is to decompose the continuous natural language input by the doctor into a combination of terms. When writing medical records, the doctor will not directly write the name of the data element, but only the dictionary value behind the data element. To achieve this goal, the following steps will be taken:

[0079] In the initialization phase of the program, all data elements that can be used for writing purposes 1 and have been referenced by term combinations are uniformly imported into the memory to form a set of elements (elements are essentially dictionary values ​​+ data elements). Text, numeric, date and time, and binary types will not be imported. For Boolean data elements, they are also imported as two element types, one for "with XXX" and the other for "without XXX". For dictionary data elements, multiple elements corresponding to the dictionary values ​​are imported. The text entered by the user is decomposed by matching the elements in the set.

[0080] After the doctor enters the content and presses Enter to confirm, the front end splits the content entered by the user according to common commas, periods or exclamation marks. Each text segment is treated as a whole and calls the back end interface for decomposition (in parallel).

[0081] After receiving the content, the backend segments the text content from back to front, taking one more character each time and matching it with the elements in the set. If no match is found, another character is taken for matching. If a match is found, the element is recorded, and the startOffset and endOffset of the element in the text content are recorded, and the record type is 1.

[0082] The date format of yyyy-MM-dd entered by the user (regular match) will also be recorded as an element with type 2.

[0083] For numeric types (regular matching) entered by the user, skip directly.

[0084] If the decomposition fails and the resulting set is empty, the empty set is returned directly and the front end processes it directly as a text data element.

[0085] After completing all the decompositions, you will get a set of decomposed elements (M) with a large number of redundant ones. Use a recursive method, combined with the recorded startOffset and endOffset. Combine the sets into multiple reasonably spliced ​​sets (m). When the text content is too long, the number of combinations may be large. At this time, considering that the actual elements of the term combination are not many, only 1,000 are taken first, and only sets with less than or equal to 8 internal elements are considered (there are almost no term combinations that can match them if there are more than 8). Then, the elements in the set m are spliced ​​and reversed, which means that the content closest to the user input is placed at the front.

[0086] If there is no such collection, an empty collection is returned directly and the front end processes it directly as a text data element.

[0087] In the actual implementation process, it was found that 1000 sets put a lot of pressure on the server, so only the first 8 sets were retained for subsequent processing.

[0088] Each set m is traversed, and the dictionary value set Ai associated with each element is extracted. If it is a date type element, the database is queried to obtain all date type data elements that can be used for writing purposes as a set.

[0089] For a sequence composed of multiple sets, find the Cartesian product (Ai*Ai+1). In the actual operation, in order to prevent the number of explosions, the total amount will be recorded in each layer of product. When the total amount exceeds 500, the product is interrupted and the matching is no longer continued. Finally, a set of sets X is obtained, and the elements inside X are also a set x (x∈X). The number of elements inside x is n, x={a1,a2,a3,....an}, where ai∈A.

[0090] Using Cartesian product to solve all sets X, take each x (x = {a1, a2, a3, ....an}) and match the set Y of all term combinations written by Director Min (the numerical data elements in the term combinations will also be ignored during the query), and sort them in reverse order by similarity.

[0091] If no matching term combination is found, an empty set is returned directly and the processing of this set ends.

[0092] For the queried set Y, y∈Y, yi is a term combination, and a matching degree is recorded inside yi as the basic matching score score (maximum 100). The data elements in yi are matched one by one according to the elements ai of the x set (selective, Boolean, and more standardized date data sources participate in the matching, while others do not participate). For the matched yi, the value of ai is filled in the valueCode and valueName attributes to form the basic elements of the medical record.

[0093] During the matching process, the score will be reduced according to the matching situation. The experience value 10 / n will be taken as the increase or decrease weight avgWeight. Every time a match fails, avgWeight will be reduced once. If yi already has a preset value and it is inconsistent with ai, the score will be reduced by 1.

[0094] Finally, a complete term combination (with the user-entered value) is pieced together. If the text concatenation value of the combination is exactly the same as the value used for input (ignoring the numerical data), add 10 to the original score.

[0095] Finally, the set Y obtained by each m in the M set is merged again and sorted in reverse order according to the score, with the one with the highest score at the front and returned to the front end.

[0096] In some embodiments of the present invention, the matching unit includes:

[0097] The first matching unit is used for matching and combining the minimum data elements into complex medical terms.

[0098] In some embodiments of the present invention, the parsing into multiple data elements with meanings based on the standardized medical terminology platform includes:

[0099] Performing word segmentation analysis on the structured electronic medical record text to be analyzed by a word segmentation engine to obtain structured analyzed word segments;

[0100] The medical terms corresponding to the structured parsed word segmentation are searched from the medical term library in sequence through a matching algorithm to obtain a plurality of data elements with meanings.

[0101] In some embodiments of the present invention, the analyzing and comparing the sizes of the plurality of data elements to select the smallest data element comprises:

[0102] Segment the electronic medical record text from back to front in sequence to obtain a plurality of characters;

[0103] Match the characters with the elements in the data set; wherein,

[0104] If the match is successful, take the next character and continue to match it with the elements in the data set;

[0105] If the match is unsuccessful, the character and the position of the character in the electronic medical record text are recorded.

[0106] In some embodiments of the present invention, the elements in the data set are a set including the minimum data element.

[0107] In some embodiments of the present invention, the minimum data element includes at least:

[0108] At least one symptom, punctuation, symptom duration, and time unit.

[0109] In some embodiments of the present invention, when matching and combining into complex medical terms based on the minimum data elements, if the user inputs a numerical value, it is skipped.

[0110] In some embodiments of the present invention, when the standardized medical terminology platform is parsed into multiple data elements with meanings, if no corresponding data element is matched in the data set during parsing, a null value is returned and the corresponding electronic medical record text is sent to the client.

[0111] On the other hand, it provides a method for writing fully structured electronic medical records based on standardized medical terminology. The client includes:

[0112] The second sending unit is used to send the structured text to be parsed to the server;

[0113] The second receiving unit is used to receive the medical terms matched by the server and select the correct medical terms from the medical terms.

[0114] Parsing the structured parsed text based on standardized medical terminology to obtain structured content includes the following steps:

[0115] For example, if a doctor inputs a sentence such as: dry mouth, polydipsia for 4 years, the system will automatically parse it into fully structured content: dry mouth [symptoms], [punctuation] polydipsia [symptoms] 4 [symptom duration] years [time unit].

[0116] The present invention focuses on fully structured electronic medical records, fully exploits the value of massive data in electronic medical records, and assists in clinical decision-making, medical record quality management, medical quality management, and scientific research data analysis.

[0117] In some specific embodiments, the text input by the doctor can be parsed into medical terminology values ​​with meaningful data elements based on the standardized medical terminology platform. The medical terms in the system are based on the minimum data elements, and complex medical terms are composed of different data elements. The system intelligently matches the association between data elements and recommends the correct medical terms to the doctor to choose. The content of the electronic medical record input by the doctor will be stored in a structured manner.

[0118] Parsing the structured parsed text based on standardized medical terminology to obtain structured content;

[0119] The structured content is sent to the client.

[0120] The present invention generates fully structured electronic medical record content based on medical terms in the smallest data element unit, which not only facilitates doctors to enter medical record content, but also greatly improves the efficiency of doctors entering electronic medical record content. Focusing on the fully structured electronic medical record, it is convenient to reuse patient medical data and make it a material for teaching and scientific research, and to analyze and mine patient data, thereby providing a basis for medical research; giving full play to the value of massive data in electronic medical records, and assisting in clinical decision-making assistance, medical record quality management, medical quality management, and scientific research data analysis.

[0121] In some specific embodiments, Figure 4 As shown, a system for writing fully structured electronic medical records based on standardized medical terminology includes at least one processor and a memory storing instructions to implement any method for writing fully structured electronic medical records based on standardized medical terminology in the above technical solutions.

[0122] In some specific embodiments, a computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps in the above technical solution.

[0123] In some specific embodiments, a computer program product includes a computer program, and when the computer program is executed by a processor, the method of implementing the steps in the above technical solution is implemented.

[0124] The embodiments and functional operations of the subject matter described in this specification may be implemented in: digital electronic circuitry, tangibly implemented computer software or firmware, computer hardware, including the structures disclosed in this specification and their structural equivalents, or a combination of more than one of the above. The embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on one or more tangible non-transitory program carriers, for execution by a data processing device or to control the operation of a data processing device.

[0125] Alternatively or additionally, the program instructions may be encoded on an artificially generated propagation signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information for transmission to an appropriate receiver device for execution by a data processing device. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of the above devices.

[0126] A computer program (which may also be referred to or described as a program, software, software application, module, software module, script or code) may be written in any form of programming language, including compiled or interpreted languages ​​or declarative or procedural languages, and the computer program may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program may be stored in a portion of a file that stores other programs or data, for example, one or more scripts stored in the following: in a markup language document; in a single file dedicated to a related program; or in multiple collaborative files, for example, files storing one or more modules, subroutines or code portions. A computer program may be deployed to execute on one or more computers, the computers being located in one place or distributed to multiple locations and interconnected by a communication network.

[0127] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0128] Although the present invention has been described in detail above by general description, specific implementation methods and experiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for writing fully structured electronic medical records based on standardized medical terminology, characterized in that: The server includes the following steps: Receive structured electronic medical record text to be parsed; Based on the standardized medical terminology platform, the electronic medical record text is parsed into medical terminology values ​​of data elements with meanings; wherein, based on the standardized medical terminology platform, the electronic medical record text is parsed into medical terminology values ​​of data elements with meanings comprises: parsing into multiple data elements with meanings based on the standardized medical terminology platform; analyzing and comparing the sizes of multiple data elements, and selecting the smallest data element; Intelligently matching medical terms through the association relationship between the minimum data elements; The matched medical terms are sent to the client.

2. The method for writing a fully structured electronic medical record based on standardized medical terminology according to claim 1, characterized in that: The intelligent matching of medical terms through the association relationship between the minimum data elements comprises the following steps: Based on the minimum data elements, complex medical terms are matched and combined.

3. The method for writing a fully structured electronic medical record based on standardized medical terminology according to claim 1, characterized in that: The method of parsing the data into multiple data elements with meanings based on the standardized medical terminology platform includes the following steps: Performing word segmentation analysis on the structured electronic medical record text to be analyzed by a word segmentation engine to obtain structured analyzed word segments; The medical terms corresponding to the structured parsed word segmentation are searched from the medical term library in sequence through a matching algorithm to obtain a plurality of data elements with meanings.

4. The method for writing a fully structured electronic medical record based on standardized medical terminology according to claim 1, characterized in that: The analyzing and comparing the sizes of the plurality of data elements to select the smallest data element comprises the following steps: Segment the electronic medical record text from back to front in sequence to obtain a plurality of characters; Match the characters with the elements in the data set; wherein, If the match is successful, take the next character and continue to match it with the elements in the data set; If the match is unsuccessful, the character and the position of the character in the electronic medical record text are recorded.

5. The method for writing a fully structured electronic medical record based on standardized medical terminology according to claim 4, characterized in that: The elements in the data set are a set containing the minimum data element.

6. The method for writing a fully structured electronic medical record based on standardized medical terminology according to claim 1, characterized in that: The minimum data element at least includes: At least one symptom, punctuation, symptom duration, and time unit.

7. The method for writing a fully structured electronic medical record based on standardized medical terminology according to claim 2, characterized in that: When matching and combining into complex medical terms based on the minimum data element, if the user inputs a numerical value, it is skipped.

8. The method for writing a fully structured electronic medical record based on standardized medical terminology according to claim 4, characterized in that: When the standardized medical terminology platform is parsed into multiple data elements with meanings, if no corresponding data element is matched in the data set during parsing, a null value is returned and the corresponding electronic medical record text is sent to the client.

9. A method for writing fully structured electronic medical records based on standardized medical terminology, characterized in that: The client includes the following steps: Send structured text to be parsed to the server; The medical terms matched by the server are received, and the correct medical terms are screened out from the medical terms.

10. A system for writing fully structured electronic medical records based on standardized medical terminology, the system comprising at least one processor and a memory storing instructions, which, when executed by at least one processor, implements a method for writing fully structured electronic medical records based on standardized medical terminology as described in any one of claims 1 to 9.

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