CN112349367A - Method and device for generating simulation medical record, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses a method and a device for generating a simulation medical record, electronic equipment and a storage medium. The method comprises the following steps: obtaining a reference medical record; acquiring a diagnosis and treatment problem request, wherein the diagnosis and treatment problem request carries an identification of a target department and a target disease; acquiring a target diagnosis and treatment problem set corresponding to the target department and the target disease, and outputting the target diagnosis and treatment problem set, wherein each diagnosis and treatment problem in the diagnosis and treatment problem set is constructed with a mapping relation with a field in a structured medical record in advance; taking the selected diagnosis and treatment problem in the target diagnosis and treatment problem set as a target diagnosis and treatment problem; acquiring answers corresponding to the target diagnosis and treatment problems from a reference medical record based on the mapping relation between the target diagnosis and treatment problems and fields in the structured medical record; and writing the target diagnosis and treatment questions and the corresponding answers into corresponding fields in the template medical record to obtain the simulated medical record. Based on the scheme disclosed by the application, the simulation medical record meeting the teaching requirement can be quickly generated.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for generating a simulated medical record, an electronic device, and a storage medium.
Background
Medicine is a highly knowledgeable and professional discipline. How to improve the diagnosis and treatment level of teaching objects (including medical students and primary doctors) is an important part in teaching tasks. Currently, a teaching object is generally taught using a medical record. For example, a high-quality medical record is constructed for a certain disease in a certain department, and a question and answer simulation is performed on a teaching object according to diagnosis and treatment questions and corresponding answers in the medical record, so that the purpose of improving the diagnosis and treatment level of the teaching object is achieved.
Currently, medical records used for teaching are written by experienced doctors based on real historical medical records, and the written medical records are called simulated medical records or simulated medical records. However, the existing scheme for generating the simulation medical record has the defects of low efficiency and long time consumption.
Disclosure of Invention
In view of the above, an object of the present application is to provide a method, an apparatus, an electronic device and a storage medium for generating a simulated medical record, so as to quickly generate a simulated medical record meeting teaching requirements.
In order to achieve the above purpose, the present application provides the following technical solutions:
the application provides a method for generating a simulation medical record, which comprises the following steps:
obtaining a reference medical record;
acquiring a diagnosis and treatment problem request, wherein the diagnosis and treatment problem request carries an identification of a target department and a target disease;
obtaining a target diagnosis and treatment problem set corresponding to the target department and the target disease from diagnosis and treatment problem sets constructed in advance for the diseases in the departments, and outputting the target diagnosis and treatment problem set; each diagnosis and treatment problem in the diagnosis and treatment problem set is constructed with a mapping relation with a field in a structured medical record in advance;
taking the selected diagnosis and treatment problem in the target diagnosis and treatment problem set as a target diagnosis and treatment problem;
acquiring answers corresponding to the target diagnosis and treatment questions from the reference medical record based on the mapping relation between the target diagnosis and treatment questions and fields in the structured medical record;
and writing the target diagnosis and treatment question and the corresponding answer into corresponding fields in a template medical record to obtain the simulated medical record.
Optionally, in the method, a scheme for pre-constructing a diagnosis and treatment problem set includes:
acquiring historical diagnosis and treatment data;
cleaning the historical diagnosis and treatment data;
classifying the cleaned historical diagnosis and treatment data, wherein the types of the historical diagnosis and treatment data comprise inquiry and physical examination;
carrying out similarity normalization processing on the washed historical diagnosis and treatment data to obtain standardized historical diagnosis and treatment data;
determining a field corresponding to the standardized historical diagnosis and treatment data in the structured medical record, and establishing a mapping relation between diagnosis and treatment problems in the standardized historical diagnosis and treatment data and the field;
determining departments and diseases corresponding to diagnosis and treatment problems in the standardized historical diagnosis and treatment data;
and adding the diagnosis and treatment problems into a diagnosis and treatment problem set constructed by corresponding departments and diseases.
Optionally, in the above method, the cleaning the historical diagnosis and treatment data includes:
performing word segmentation processing on the historical diagnosis and treatment data to obtain a first word segmentation result;
if at least one word in the first word segmentation result belongs to a medical knowledge base, a synonym dictionary of the medical knowledge base or a preset high-frequency medical vocabulary, the historical diagnosis and treatment data are reserved, otherwise, the historical diagnosis and treatment data are deleted.
Optionally, in the above method, the classifying the washed historical diagnosis and treatment data includes:
if the washed historical diagnosis and treatment data comprises words or inquiry keywords corresponding to the symptom body, determining that the washed historical diagnosis and treatment data is of an inquiry type;
and if the washed historical diagnosis and treatment data comprises words corresponding to the part ontology, determining that the washed historical diagnosis and treatment data is of a physical examination type.
Optionally, in the method, the performing similarity normalization processing on the cleaned historical diagnosis and treatment data to obtain standardized historical diagnosis and treatment data includes:
determining the similarity between the historical diagnosis and treatment data belonging to the same type in the washed historical diagnosis and treatment data;
and normalizing the plurality of cleaned historical diagnosis and treatment data with the similarity reaching a preset similarity threshold value to generate a piece of standardized historical diagnosis and treatment data.
Optionally, in the method, the determining a corresponding field of the standardized historical clinical data in the structured medical record includes:
respectively determining the correlation weight between the standardized historical diagnosis and treatment data and each field in the structured medical record;
and determining the field with the highest association weight as the corresponding field of the standardized historical diagnosis and treatment data in the structured medical record.
Optionally, in the method, the determining departments and diseases corresponding to the diagnosis and treatment problems in the standardized historical diagnosis and treatment data includes:
determining departments corresponding to diagnosis and treatment problems in the standardized historical diagnosis and treatment data according to fields corresponding to the standardized historical diagnosis and treatment data in the structured medical records;
performing word segmentation on the standardized historical diagnosis and treatment data to obtain a second word segmentation result, determining the similarity between the second word segmentation result and the medical keywords of each disease in the department, and determining the disease with the highest similarity as the disease corresponding to the diagnosis and treatment problem in the standardized historical diagnosis and treatment data.
The present application further provides a device for generating a simulated medical record, comprising:
a reference medical record obtaining unit for obtaining a reference medical record;
the system comprises a request acquisition unit, a diagnosis and treatment problem analysis unit and a diagnosis and treatment problem analysis unit, wherein the request acquisition unit is used for acquiring a diagnosis and treatment problem request which carries an identification of a target department and a target disease;
a target diagnosis and treatment problem set acquisition unit, configured to acquire a target diagnosis and treatment problem set corresponding to a target department and a target disease from diagnosis and treatment problem sets pre-constructed for the diseases in the departments, and output the target diagnosis and treatment problem set; each diagnosis and treatment problem in the diagnosis and treatment problem set is constructed with a mapping relation with a field in a structured medical record in advance;
the target diagnosis and treatment problem acquisition unit is used for taking the selected diagnosis and treatment problem in the target diagnosis and treatment problem set as a target diagnosis and treatment problem;
the diagnosis and treatment answer obtaining unit is used for obtaining an answer corresponding to the target diagnosis and treatment question in the reference medical record based on the mapping relation between the target diagnosis and treatment question and the fields in the structured medical record;
and the data processing unit is used for writing the target diagnosis and treatment questions and the corresponding answers into corresponding fields in a template medical record to obtain the simulated medical record.
The application also provides an electronic device comprising a processor, a memory and a communication interface;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of any one of the above methods for generating a simulated medical record.
The present application also provides a readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the above methods of generating a simulated medical record. It can be seen from this that:
according to the method, the device, the electronic equipment and the storage medium for generating the simulation medical record, diagnosis and treatment problem sets are constructed aiming at all diseases in all departments in advance, and each diagnosis and treatment problem in the diagnosis and treatment problem sets is constructed with a mapping relation with a field in a structured medical record in advance; after a diagnosis and treatment problem request carrying identification of a target department and a target disease is obtained, a target diagnosis and treatment problem set corresponding to the target department and the target disease is obtained from a pre-constructed diagnosis and treatment problem set, the target diagnosis and treatment problem set is output, then selected diagnosis and treatment problems in the diagnosis and treatment problem set are used as target diagnosis and treatment problems, answers corresponding to the target diagnosis and treatment problems are obtained from a reference medical record based on the mapping relation between the target diagnosis and treatment problems and fields in a structured medical record, and the target diagnosis and treatment problems and the corresponding answers are written into corresponding fields in a template medical record to obtain a simulated medical record.
Based on the scheme disclosed by the application, the user only needs to determine the target department and the target disease according to the teaching requirement and select the target diagnosis and treatment problem from the target diagnosis and treatment problem set corresponding to the target department and the target disease, and the electronic equipment can quickly generate the simulation model meeting the teaching requirement based on the reference medical records and the mapping relation between the pre-constructed diagnosis and treatment problems and the fields in the structured medical records.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method of generating a simulated medical record as disclosed herein;
FIG. 2 is a flow chart of a method of pre-building a problem set as disclosed herein;
fig. 3 is a schematic diagram of a diagnosis and treatment problem set and a mapping relationship between each diagnosis and treatment problem in the diagnosis and treatment problem set and a field in a structured medical record disclosed in the present application;
FIG. 4-1 is a schematic illustration of historical clinical data as disclosed herein;
FIG. 4-2 is a diagram illustrating the segmentation result of the historical clinical data shown in FIG. 4-1;
FIG. 4-3 is a diagram illustrating the results of screening the historical clinical data of FIG. 4-1 based on the medical knowledge base and the synonym dictionary of the medical knowledge base;
fig. 4-4 is a diagram illustrating a result of screening the historical diagnosis and treatment data shown in fig. 4-1 based on a preset high-frequency medical vocabulary;
FIG. 4-5 is a schematic illustration of a result of cleaning the historical clinical data shown in FIG. 4-1;
FIG. 5 is a schematic view of a sample portion body disclosed herein;
FIG. 6 is a schematic view of a sample of a symptom entity of the present disclosure;
fig. 7 is a schematic diagram of a part of classification results obtained by classifying the washed historical clinical data shown in fig. 4-5;
FIG. 8 is a schematic illustration of the questions and answers of the interrogation after the wash and the results of the normalization process disclosed herein;
FIG. 9 is a graphical illustration of the results of the normalization process performed on the cleaned volume data disclosed herein;
FIG. 10 is a diagram illustrating the segmentation results of clinical questions in standardized historical clinical data and keywords in the Chinese name path of structured fields, as disclosed herein;
FIG. 11 is a schematic diagram of an apparatus for generating a simulated medical record according to the present disclosure;
fig. 12 is a schematic structural diagram of an electronic device disclosed in the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
First, technical terms appearing in the present application will be explained.
Medical classification word bank: the word stock commonly used in the medical field includes six categories of diseases, symptoms, operations, medicines, examinations, and the like. For example, the word banks for diseases include coronary atherosclerotic heart disease, hypertension, etc., the word banks for symptoms include fever, headache, etc., the word banks for operations include cardiac intervention operations, thoracoscopic operations, etc., the word banks for drugs include aspirin, atorvastatin calcium, etc., the word banks for examination include erythrocytes, leukocytes, etc., and the word banks for examination include electrocardiogram, ultrasound, etc.
Diagnosis and treatment data: in diagnosing a disease, the physician's questions and the patient's answers (i.e., the patient's answers) may be subdivided into inquiry questions and corresponding answers, and physical examination questions and corresponding answers.
And (3) natural language processing: natural Language Processing (NLP) is an important direction in the fields of computer science and artificial intelligence. It studies various theories and methods that enable efficient communication between humans and computers using natural language. Natural language processing is a science integrating linguistics, computer science and mathematics. That is, let the computer understand the language we use daily.
Structuring data: currently, data is divided into structured data, semi-structured data, and unstructured data. Structured data refers to data that can be represented and stored in a two-dimensional form using a relational database. Unstructured data is data that has no fixed structure, such as pictures, documents, etc. Semi-structured data is a form of structured data that does not conform to the structure of a data model in which relational databases or other forms of data tables are associated, but contains relevant tags to separate semantic elements and to stratify records and fields. It is therefore also referred to as a self-describing structure.
Text similarity: a method of examining whether two texts are similar and determining a degree of similarity between the two texts.
The application discloses a method for generating a simulation medical record, which can be used for quickly generating diagnosis and treatment questions and corresponding answers according to teaching requirements, so that the simulation medical record meeting the teaching requirements can be quickly generated.
The method for generating the simulated medical record is applied to the electronic device, wherein the electronic device can be a terminal device, such as a mobile phone and a personal computer, and can also be a server or a server cluster providing a service for generating the simulated medical record.
Referring to fig. 1, fig. 1 is a flowchart of a method for generating a simulated medical record disclosed in the present application. The method comprises the following steps:
step S101: a reference medical record is obtained.
The reference medical record is a real historical medical record and is reference data used for generating the simulation medical record.
In implementation, after determining that a simulated medical record is to be generated for a disease in a department, one or more historical medical records of the disease in the department are obtained as reference medical records. For example, to generate a simulated medical record for hypertension in a cardiovascular department, one or more historical medical records for hypertension in the cardiovascular department are obtained as reference medical records.
Step S102: and obtaining a diagnosis and treatment problem request.
The diagnosis and treatment problem request carries the identification of a target department and a target disease.
In one possible implementation, the user directly inputs a diagnosis and treatment question request on a graphical interface, wherein the diagnosis and treatment question request carries the identification of a target department and a target disease. The graphical interface is a front-end page capable of human-machine interaction.
In another possible implementation manner, departments and diseases under each department are displayed on a graphical interface, a user selects the department and the disease under the department, and when the confirmation control is clicked, a diagnosis and treatment problem request is generated, wherein the diagnosis and treatment problem request carries the identification of the target department and the target disease.
Step S103: and obtaining a target diagnosis and treatment problem set corresponding to the target department and the target disease from diagnosis and treatment problem sets constructed in advance aiming at the diseases in the departments, and outputting the target diagnosis and treatment problem set. Each diagnosis and treatment problem in the diagnosis and treatment problem set is constructed with a mapping relation with a field in the structured medical record in advance.
Diagnosis and treatment problem sets are pre-constructed in advance aiming at various diseases in various departments, each diagnosis and treatment problem set generally comprises a plurality of diagnosis and treatment problems, and each diagnosis and treatment problem in the diagnosis and treatment problem sets is pre-constructed with a mapping relation with fields in a structured medical record.
Taking the cardiovascular medicine as an example, diagnosis and treatment problem sets are respectively constructed in advance aiming at hypertension and coronary atherosclerotic heart disease. Referring to fig. 3, fig. 3 shows a diagnosis and treatment problem set pre-constructed for a disease in a department, and a mapping relationship between each diagnosis and treatment problem in the diagnosis and treatment problem set and a field in a structured medical record.
After the diagnosis and treatment problem request is obtained, the identification of the target department and the target disease carried by the diagnosis and treatment problem request is determined, and a target diagnosis and treatment problem set corresponding to the target department and the target disease is obtained in a diagnosis and treatment problem set which is pre-constructed aiming at each disease in each department.
It should be noted that the diagnosis and treatment questions include an inquiry question and a physical examination question.
Step S104: and taking the selected diagnosis and treatment problems in the target diagnosis and treatment problem set as target diagnosis and treatment problems.
And after the target diagnosis and treatment problem set is obtained, displaying the target diagnosis and treatment problem set, selecting one or more diagnosis and treatment problems in the target diagnosis and treatment problem set by a user, and taking the selected diagnosis and treatment problems as target diagnosis and treatment problems. That is, the number of target diagnosis and treatment problems may be one or more.
Step S105: and acquiring answers corresponding to the target diagnosis and treatment problems from the reference medical records based on the mapping relation between the target diagnosis and treatment problems and the fields in the structured medical records.
Step S106: and writing the target diagnosis and treatment questions and the corresponding answers into corresponding fields in the template medical record to obtain the simulated medical record.
The mapping relation between the target diagnosis and treatment question and the field in the structured medical record is known, and the answer corresponding to the target diagnosis and treatment question is obtained in the reference medical record based on the mapping relation between the target diagnosis and treatment question and the field in the structured medical record. And then writing the target diagnosis and treatment questions and the corresponding answers into corresponding fields in the template medical record, thereby obtaining the simulated medical record.
Here, the diagnosis and treatment problem set shown in fig. 3 is taken as an example:
if a target issue is "whether another disease has been detected before", the target issue is mapped to the "admission record, past history, disease name" field in the structured case history. Therefore, data of fields of 'admission record, past history, disease and disease name' in the reference medical record are obtained, the data comprise diagnosis and treatment questions and corresponding answers, and the answers in the data are further obtained, namely the answers corresponding to the target diagnosis and treatment questions. The target diagnosis and treatment question 'whether other diseases exist before' and the corresponding answer are written into the 'admission record, the past history, the diseases and the disease names' fields in the template medical record.
If a plurality of target diagnosis and treatment questions exist, the answers corresponding to the target diagnosis and treatment questions are sequentially obtained from the reference medical record, and the target diagnosis and treatment questions and the corresponding answers are written into corresponding fields in the template medical record to obtain the simulated medical record.
It should be noted that the simulated medical record generated based on the method disclosed in the present application is mainly used for the teaching of the inquiry and physical examination of the teaching object, and therefore, the simulated medical record only includes diagnosis and treatment questions and corresponding answers.
In addition, considering that a real medical record often includes inquiry data, physical examination data, examination data and diagnosis data, optionally, on the basis of the obtained simulated medical record, the following steps may be further provided: one or more of the test data, the examination data, and the diagnostic data are obtained, and the obtained data are written into the simulated medical record.
In implementation, the inspection data, the examination data and the diagnosis data in the reference medical record can be acquired, and the acquired data can be written into the simulation medical record.
The method for generating the simulation medical record comprises the steps of constructing diagnosis and treatment problem sets aiming at all diseases in all departments in advance, wherein each diagnosis and treatment problem in the diagnosis and treatment problem sets is constructed with a mapping relation with a field in a structured medical record in advance; after a diagnosis and treatment problem request carrying identification of a target department and a target disease is obtained, a target diagnosis and treatment problem set corresponding to the target department and the target disease is obtained from a pre-constructed diagnosis and treatment problem set, the target diagnosis and treatment problem set is output, then selected diagnosis and treatment problems in the diagnosis and treatment problem set are used as target diagnosis and treatment problems, answers corresponding to the target diagnosis and treatment problems are obtained from a reference medical record based on the mapping relation between the target diagnosis and treatment problems and fields in a structured medical record, and the target diagnosis and treatment problems and the corresponding answers are written into corresponding fields in a template medical record to obtain a simulated medical record.
Based on the method disclosed by the application, the user only needs to determine the target department and the target disease according to the teaching requirement and select the target diagnosis and treatment problem from the target diagnosis and treatment problem set corresponding to the target department and the target disease, and the electronic equipment can quickly generate the simulation model meeting the teaching requirement based on the reference medical records and the mapping relation between the pre-constructed diagnosis and treatment problems and the fields in the structured medical records.
In the technical solution disclosed in the present application, diagnosis and treatment problem sets are constructed in advance for a plurality of diseases in a plurality of departments in a hospital. The following describes a scheme of constructing a diagnosis and treatment problem set in advance.
Referring to fig. 2, fig. 2 is a flow chart of a method of pre-building a problem set as disclosed herein. The method comprises the following steps:
step S201: and acquiring historical diagnosis and treatment data.
The diagnosis and treatment data (namely historical diagnosis and treatment data) generated by a doctor in the actual diagnosis and treatment process is obtained from the existing actual diagnosis and treatment cases. The diagnosis and treatment data comprises diagnosis and treatment questions and corresponding answers, and can be divided into an inquiry type and a physical examination type.
In implementation, the historical diagnosis and treatment data can be acquired by adopting a web crawler technology. Referring to fig. 4-1, fig. 4-1 illustrates a portion of historical clinical data.
Step S202: and cleaning historical diagnosis and treatment data.
In one possible implementation, the cleaning of historical clinical data includes:
a1: performing word segmentation processing on historical diagnosis and treatment data to obtain a first word segmentation result;
a2: if at least one word in the first word segmentation result belongs to the medical knowledge base, the synonym dictionary of the medical knowledge base or a preset high-frequency medical vocabulary, historical diagnosis and treatment data are reserved, and if not, the historical diagnosis and treatment data are deleted.
That is, all the historical clinical data are cleaned one by one. Performing word segmentation processing on a piece of historical diagnosis and treatment data to obtain a word segmentation result, and recording the word segmentation result as a first word segmentation result for convenience of description; and then, judging whether at least one participle in the first participle result belongs to a medical knowledge base, a synonym dictionary of the medical knowledge base or a preset high-frequency medical vocabulary, if one or more participles in the first participle result belong to the medical knowledge base, the synonym dictionary of the medical knowledge base or the preset high-frequency medical vocabulary, keeping the historical diagnosis and treatment data, and if none of the participles in the first participle result belongs to the medical knowledge base, the synonym dictionary of the medical knowledge base or the preset high-frequency medical vocabulary, deleting the historical diagnosis and treatment data.
This is explained in conjunction with fig. 4-1 to 4-5.
The acquired historical clinical data is shown in fig. 4-1. The word segmentation processing is performed on the historical diagnosis and treatment data shown in fig. 4-1, and the obtained word segmentation result is shown in fig. 4-2. Comparing the segmentation result of the historical diagnosis and treatment data shown in fig. 4-2 with the synonym dictionary of the medical knowledge base and the medical knowledge base, if any one or more segmentation words in the segmentation result of the historical diagnosis and treatment data belong to the medical knowledge base or the synonym dictionary of the medical knowledge base, the historical diagnosis and treatment data is retained, as shown in fig. 4-3. Comparing the word segmentation result of the historical diagnosis and treatment data shown in fig. 4-2 with the preset high-frequency medical vocabulary, if any one or more words in the word segmentation result of the historical diagnosis and treatment data belong to the preset high-frequency medical vocabulary, the historical diagnosis and treatment data is reserved. The result of cleaning the historical clinical data shown in fig. 4-1 is obtained by combining fig. 4-3 and fig. 4-4, as shown in fig. 4-5.
Of course, other schemes can be adopted to clean the historical diagnosis and treatment data, as long as the cleaned historical diagnosis and treatment data are directly related to the diagnosis and treatment process.
Step S203: and classifying the cleaned historical diagnosis and treatment data. The types of historical diagnosis and treatment data include inquiry and physical examination.
In one possible implementation, classifying the cleaned historical clinical data includes:
b1: if the washed historical diagnosis and treatment data comprise words or inquiry keywords corresponding to the symptom body, determining that the washed historical diagnosis and treatment data are of an inquiry type;
b2: and if the washed historical diagnosis and treatment data comprises words corresponding to the part ontology, determining the washed historical diagnosis and treatment data as the physical examination type.
Referring to fig. 5 and 6, fig. 5 shows a sample of a partial site body, and fig. 6 shows a sample of a partial symptom body. The inquiry keywords are words which are obtained by counting a large amount of inquiry data and are used frequently in the inquiry process. For example, the inquiry keywords include, but are not limited to: age, sex, etc. It should be noted that if the diagnosis question has no corresponding answer, the diagnosis question does not belong to the inquiry type. Fig. 7 shows a partial result of classifying the washed historical clinical data shown in fig. 4 to 5.
Step S204: and carrying out similarity normalization processing on the washed historical diagnosis and treatment data to obtain standardized historical diagnosis and treatment data.
Similar historical diagnosis and treatment data may exist in the washed historical diagnosis and treatment data, and if a plurality of similar historical diagnosis and treatment data are reserved, data redundancy can be caused, so that the similarity normalization processing is performed on the washed historical diagnosis and treatment data, and the aim is to process the similar historical diagnosis and treatment data into one piece of standardized historical diagnosis and treatment data.
It has been explained in the foregoing that the medical data includes an inquiry type and a physical type, and accordingly, the historical medical data after cleaning also includes an inquiry type and a physical type. Similarity normalization processing needs to be performed on data belonging to an inquiry type in the washed historical diagnosis and treatment data, and similarity normalization processing needs to be performed on data belonging to a physical examination type in the washed historical diagnosis and treatment data.
In one possible implementation manner, the similarity normalization processing is performed on the cleaned historical diagnosis and treatment data, and includes:
c1: and determining the similarity between the historical diagnosis and treatment data of the same type in the washed historical diagnosis and treatment data.
In implementation, word segmentation processing is respectively performed on a plurality of pieces of historical diagnosis and treatment data belonging to the same type in the washed historical diagnosis and treatment data to obtain word segmentation results of each piece of historical diagnosis and treatment data, similarity characteristic values of words in each word segmentation result are determined, and then similarity between the pieces of historical diagnosis and treatment data is determined by using the similarity characteristic values of the words in each word segmentation result based on a cosine similarity principle.
Optionally, the similarity feature value of the participle is a word frequency (TF). The TF of a word is: the number of times the word appears in the document is divided by the total number of words in the document. Alternatively, the TF for a word is: the number of times the word appears in the document is divided by the number of times the word appears most frequently in the document.
Optionally, the similarity feature value of the word segmentation is TF minus IDF. A word appears in N documents, and the larger the value of N is, the smaller the weight of the word is, and correspondingly, the smaller the value of N is, the larger the weight of the word is. The Inverse Document Frequency (IDF) of a word is log (total number of documents in the corpus/total number of documents containing the word + 1).
C2: and normalizing the plurality of cleaned historical diagnosis and treatment data with the similarity reaching a preset similarity threshold value to generate a piece of standardized historical diagnosis and treatment data.
If the similarity between the plurality of cleaned historical diagnosis and treatment data reaches a preset similarity threshold (for example, 70%), the plurality of pieces of historical diagnosis and treatment data need to be normalized to generate one piece of standardized historical diagnosis and treatment data.
In implementation, if the plurality of pieces of cleaned historical diagnosis and treatment data to be subjected to the normalization processing are of the inquiry type, performing word segmentation on the historical diagnosis and treatment data, performing synonym conversion on a segmentation result, and performing conversion by using a adverb dictionary and a synonym dictionary to obtain standardized historical diagnosis and treatment data. Referring to fig. 8, fig. 8 shows the questions and answers of the inquiry and the results of the normalization process after washing.
And if the plurality of cleaned historical diagnosis and treatment data to be subjected to normalization processing are of a physical examination type, converting the historical diagnosis and treatment data according to the synonym dictionary of the body part to obtain standardized historical diagnosis and treatment data. Referring to fig. 9, fig. 9 shows the result of the volume data normalization process after washing.
Step S205: determining the corresponding field of the standardized historical diagnosis and treatment data in the structured medical record, and establishing a mapping relation between the diagnosis and treatment problems in the standardized historical diagnosis and treatment data and the field.
In one possible implementation, determining corresponding fields of the standardized historical clinical data in the structured medical record includes:
d1: and respectively determining the associated weight between the standardized historical diagnosis and treatment data and each field in the structured medical record.
D2: and determining the field with the highest association weight as the corresponding field of the standardized historical diagnosis and treatment data in the structured medical record.
The correlation weight between the standardized historical diagnosis and treatment data and each field in the structured medical record is characterized as follows: the relevance between the standardized historical clinical data and the fields in the structured medical record is high or low. And determining the association weight between the standardized historical diagnosis and treatment data and each field in the structured medical record aiming at each standardized historical diagnosis and treatment data, and then determining the field with the highest association weight as the corresponding field of the standardized historical diagnosis and treatment data in the structured medical record.
It should be noted that the structured medical record includes structured fields and unstructured fields.
Optionally, determining an association weight between the standardized historical diagnosis and treatment data and a structured field in a structured medical record, and adopting the following scheme:
extracting key words in the Chinese name path of the structured field; performing word segmentation on diagnosis and treatment problems in the standardized historical diagnosis and treatment data to obtain word segmentation results; and matching the keywords with the word segmentation results to obtain matching results, and generating association weights according to the matching results.
And the number of the matched words in the keywords and the word segmentation results and the value of the association weight form a positive correlation relationship. That is, the more words in the keyword and the word segmentation result that match, the greater the weight of association between the normalized historical clinical data and the structured field.
Referring to fig. 10, fig. 10 shows the segmentation results of the diagnosis and treatment questions in the standardized historical diagnosis and treatment data and the keywords in the chinese name path of the structured fields.
Optionally, if the standardized historical diagnosis and treatment data is an inquiry type, determining an association weight between the standardized historical diagnosis and treatment data and a structured field in a structured medical record, and adopting the following scheme:
extracting key words in the Chinese name path of the structured field; performing word segmentation on diagnosis and treatment problems (specifically inquiry problems) in the standardized historical diagnosis and treatment data to obtain word segmentation results; matching the keywords with the word segmentation results to obtain a first matching result, and generating a first association weight according to the first matching result;
counting the value range of the structured field, matching the distribution condition of the structured field with the word distribution condition of the word segmentation result of the diagnosis and treatment problem in the standardized historical diagnosis and treatment data to obtain a second matching result, and generating a second association weight according to the second matching result;
and taking the sum of the first association weight and the second association weight as an association weight between the standardized historical clinical data and a structured field in the structured medical record.
And the matching degree of the second matching result and the value of the second association weight form a positive correlation relationship. That is to say, the higher the matching degree between the value range distribution of the structured field and the word distribution of the word segmentation result of the diagnosis and treatment problem is, the larger the value of the second association weight is.
Optionally, determining an association weight between the standardized historical diagnosis and treatment data and an unstructured field in a structured medical record, and adopting the following scheme:
performing word segmentation on five history of first complaints in admission records of historical medical records, performing normalization on word segmentation results, and counting distribution conditions of high-frequency words in the word segmentation results after the normalization; performing word segmentation processing on the standardized historical diagnosis and treatment data, and counting word distribution conditions of word segmentation results; and matching the two distribution conditions to obtain a matching result, and generating the association weight according to the matching result. And the matching degree of the distribution condition of the high-frequency words and the distribution condition of the words of the word segmentation result and the association weight form a positive correlation.
Step S206: and determining departments and diseases corresponding to diagnosis and treatment problems in the standardized historical diagnosis and treatment data.
In one possible implementation, determining departments and diseases corresponding to diagnosis and treatment problems in standardized historical diagnosis and treatment data includes:
e1: and determining departments corresponding to diagnosis and treatment problems in the standardized historical diagnosis and treatment data according to corresponding fields of the standardized historical diagnosis and treatment data in the structured medical records.
The corresponding relation between the fields in the structured medical record and departments is determined, and the departments corresponding to diagnosis and treatment problems in the standardized historical diagnosis and treatment data can be determined according to the corresponding fields in the structured medical record of the standardized historical diagnosis and treatment data and the corresponding relation.
It should be noted that some fields in the structured medical record are set as common fields, such as fields related to age and gender. If the standardized historical clinical data corresponds to a common field in the structured medical records, the standardized historical clinical data is common data of all departments, that is, the clinical problems in the standardized historical clinical data correspond to all departments.
E2: performing word segmentation on the standardized historical diagnosis and treatment data to obtain a second word segmentation result, determining the similarity between the second word segmentation result and the medical keywords of each disease in the department, and determining the disease with the highest similarity as the disease corresponding to the diagnosis and treatment problem in the standardized historical diagnosis and treatment data.
A plurality of diseases are set under each department, and after the department corresponding to the diagnosis and treatment problem in the standardized historical diagnosis and treatment data is determined, the disease corresponding to the diagnosis and treatment problem needs to be further determined. Each disease has respective characteristics, and medical keywords of each disease are different, so that according to the similarity between the word segmentation result of the standardized historical diagnosis and treatment data and the medical keywords of a plurality of diseases in the department, which disease in the department the diagnosis and treatment problem in the standardized historical diagnosis and treatment data corresponds to can be determined.
Step S207: and adding the diagnosis and treatment problems into a diagnosis and treatment problem set constructed by corresponding departments and diseases.
After the department and the disease corresponding to the diagnosis and treatment problem are determined, the diagnosis and treatment problem is added into a diagnosis and treatment problem set corresponding to the department and the disease.
According to the scheme shown in fig. 2, the electronic equipment acquires historical diagnosis and treatment data and then cleans the historical diagnosis and treatment data, classifying the cleaned historical diagnosis and treatment data, carrying out similarity normalization processing on the cleaned historical diagnosis and treatment data, thereby processing similar historical diagnosis and treatment data in the same category into standardized historical diagnosis and treatment data, then determining the corresponding field of the standardized historical diagnosis and treatment data in the structured medical record, and establishes a mapping relation between diagnosis and treatment problems in the standardized historical diagnosis and treatment data and the field, determines departments and diseases corresponding to the diagnosis and treatment problems in the standardized historical diagnosis and treatment data, adds the diagnosis and treatment problems into a diagnosis and treatment problem set constructed by the corresponding departments and diseases, therefore, the construction of diagnosis and treatment problem sets corresponding to various diseases in various departments is completed, and each diagnosis and treatment problem in the diagnosis and treatment problem sets is constructed with a mapping relation with fields in the structured medical records in advance.
The application discloses a method for generating a simulated medical record, and correspondingly, the application also discloses a device for generating the simulated medical record. The descriptions of the two in the specification can be mutually referred.
Referring to fig. 11, fig. 11 is a schematic structural diagram of an apparatus for generating a simulated medical record disclosed in the present application. The device includes:
a reference medical record obtaining unit 1101, configured to obtain a reference medical record.
The request obtaining unit 1102 is configured to obtain a diagnosis and treatment problem request, where the diagnosis and treatment problem request carries an identifier of a target department and a target disease.
A target diagnosis and treatment problem set obtaining unit 1103, configured to obtain a target diagnosis and treatment problem set corresponding to a target department and a target disease from diagnosis and treatment problem sets pre-constructed for the respective diseases in the respective departments, and output the target diagnosis and treatment problem set. Each diagnosis and treatment problem in the diagnosis and treatment problem set is constructed with a mapping relation with a field in the structured medical record in advance.
And a target diagnosis and treatment problem acquisition unit 1104, configured to take the selected diagnosis and treatment problem in the target diagnosis and treatment problem set as the target diagnosis and treatment problem.
The diagnosis and treatment answer obtaining unit 1105 is configured to obtain an answer corresponding to the target diagnosis and treatment question in the reference medical record based on a mapping relationship between the target diagnosis and treatment question and a field in the structured medical record.
And the data processing unit 1106 is configured to write the target diagnosis and treatment question and the corresponding answer into corresponding fields in the template medical record to obtain a simulated medical record.
The device for generating the simulated medical record is characterized in that a diagnosis and treatment problem set is constructed in advance for each disease in each department, and a mapping relation with fields in a structured medical record is constructed in advance for each diagnosis and treatment problem in the diagnosis and treatment problem set; after a diagnosis and treatment problem request carrying identification of a target department and a target disease is obtained, a target diagnosis and treatment problem set corresponding to the target department and the target disease is obtained from a pre-constructed diagnosis and treatment problem set, the target diagnosis and treatment problem set is output, then selected diagnosis and treatment problems in the diagnosis and treatment problem set are used as target diagnosis and treatment problems, answers corresponding to the target diagnosis and treatment problems are obtained from a reference medical record based on the mapping relation between the target diagnosis and treatment problems and fields in a structured medical record, and the target diagnosis and treatment problems and the corresponding answers are written into corresponding fields in a template medical record to obtain a simulated medical record.
It can be seen that based on the device for generating the simulated medical record disclosed by the application, a user only needs to determine a target department and a target disease according to teaching requirements and select a target diagnosis and treatment problem from a target diagnosis and treatment problem set corresponding to the target department and the target disease, and the device can quickly generate a simulation model meeting the teaching requirements based on a reference medical record and a mapping relation between a pre-constructed diagnosis and treatment problem and a field in a structured medical record.
In another embodiment, on the basis of the apparatus shown in fig. 11, a preprocessing unit is further provided, and the preprocessing unit is used for pre-constructing diagnosis and treatment problem sets for respective diseases in respective departments.
Optionally, the preprocessing unit includes:
the historical diagnosis and treatment data acquisition subunit is used for acquiring historical diagnosis and treatment data;
the data cleaning subunit is used for cleaning the historical diagnosis and treatment data;
the diagnosis and treatment data classification subunit is used for classifying the cleaned historical diagnosis and treatment data, and the types of the historical diagnosis and treatment data comprise inquiry and physical examination;
the normalization processing subunit is used for carrying out similarity normalization processing on the washed historical diagnosis and treatment data to obtain standardized historical diagnosis and treatment data;
the mapping relation processing subunit is used for determining a corresponding field of the standardized historical diagnosis and treatment data in the structured medical record and establishing a mapping relation between diagnosis and treatment problems in the standardized historical diagnosis and treatment data and the field;
the department and disease determining subunit is used for determining departments and diseases corresponding to diagnosis and treatment problems in the standardized historical diagnosis and treatment data;
and the diagnosis and treatment problem processing subunit is used for adding the diagnosis and treatment problems into a diagnosis and treatment problem set constructed by corresponding departments and diseases.
Optionally, the data cleaning subunit cleans the historical diagnosis and treatment data, specifically:
performing word segmentation processing on historical diagnosis and treatment data to obtain a first word segmentation result;
if at least one word in the first word segmentation result belongs to the medical knowledge base, the synonym dictionary of the medical knowledge base or a preset high-frequency medical vocabulary, historical diagnosis and treatment data are reserved, and if not, the historical diagnosis and treatment data are deleted.
Optionally, the diagnosis and treatment data classification subunit classifies the washed historical diagnosis and treatment data, specifically:
if the washed historical diagnosis and treatment data comprise words or inquiry keywords corresponding to the symptom body, determining that the washed historical diagnosis and treatment data are of an inquiry type;
and if the washed historical diagnosis and treatment data comprises words corresponding to the part ontology, determining the washed historical diagnosis and treatment data as the physical examination type.
Optionally, the normalization processing subunit performs similarity normalization processing on the washed historical diagnosis and treatment data to obtain standardized historical diagnosis and treatment data, and specifically includes:
determining the similarity between the historical diagnosis and treatment data belonging to the same type in the washed historical diagnosis and treatment data;
and normalizing the plurality of cleaned historical diagnosis and treatment data with the similarity reaching a preset similarity threshold value to generate a piece of standardized historical diagnosis and treatment data.
Optionally, the mapping relationship processing subunit determines a corresponding field of the standardized historical diagnosis and treatment data in the structured medical record, specifically:
respectively determining the association weight between the standardized historical diagnosis and treatment data and each field in the structured medical record;
and determining the field with the highest association weight as the corresponding field of the standardized historical diagnosis and treatment data in the structured medical record.
Optionally, the department and disease determining subunit determines departments and diseases corresponding to diagnosis and treatment problems in the standardized historical diagnosis and treatment data, specifically:
determining departments corresponding to diagnosis and treatment problems in the standardized historical diagnosis and treatment data according to corresponding fields of the standardized historical diagnosis and treatment data in the structured medical records;
performing word segmentation on the standardized historical diagnosis and treatment data to obtain a second word segmentation result, determining the similarity between the second word segmentation result and the medical keywords of each disease in the department, and determining the disease with the highest similarity as the disease corresponding to the diagnosis and treatment problem in the standardized historical diagnosis and treatment data.
The application also provides an electronic device. Referring to fig. 12, fig. 12 illustrates a hardware structure of an electronic device, which may include: at least one processor 1201, at least one communication interface 1202, at least one memory 1203 and at least one communication bus 1204.
In this embodiment, the number of the processor 1201, the communication interface 1202, the memory 1203 and the communication bus 1204 is at least one, and the processor 1201, the communication interface 1202 and the memory 1203 complete communication with each other through the communication bus 1204.
The processor 1201 may be a central processing unit CPU, or an application Specific Integrated circuit asic, or one or more Integrated circuits configured to implement embodiments of the present invention, etc.
The memory 1203 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory) or the like, such as at least one disk memory.
Wherein the memory stores a program and the processor can call the program stored in the memory, the program for:
obtaining a reference medical record;
acquiring a diagnosis and treatment problem request, wherein the diagnosis and treatment problem request carries an identification of a target department and a target disease;
obtaining a target diagnosis and treatment problem set corresponding to the target department and the target disease from diagnosis and treatment problem sets constructed in advance for the diseases in the departments, and outputting the target diagnosis and treatment problem set; each diagnosis and treatment problem in the diagnosis and treatment problem set is constructed with a mapping relation with a field in a structured medical record in advance;
taking the selected diagnosis and treatment problem in the target diagnosis and treatment problem set as a target diagnosis and treatment problem;
acquiring answers corresponding to the target diagnosis and treatment questions from the reference medical record based on the mapping relation between the target diagnosis and treatment questions and fields in the structured medical record;
and writing the target diagnosis and treatment question and the corresponding answer into corresponding fields in a template medical record to obtain the simulated medical record.
Alternatively, the detailed function and the extended function of the program may be as described above.
The present application also provides a readable storage medium having stored thereon a program adapted to be executed by a processor, the program for:
obtaining a reference medical record;
acquiring a diagnosis and treatment problem request, wherein the diagnosis and treatment problem request carries an identification of a target department and a target disease;
obtaining a target diagnosis and treatment problem set corresponding to the target department and the target disease from diagnosis and treatment problem sets constructed in advance for the diseases in the departments, and outputting the target diagnosis and treatment problem set; each diagnosis and treatment problem in the diagnosis and treatment problem set is constructed with a mapping relation with a field in a structured medical record in advance;
taking the selected diagnosis and treatment problem in the target diagnosis and treatment problem set as a target diagnosis and treatment problem;
acquiring answers corresponding to the target diagnosis and treatment questions from the reference medical record based on the mapping relation between the target diagnosis and treatment questions and fields in the structured medical record;
and writing the target diagnosis and treatment question and the corresponding answer into corresponding fields in a template medical record to obtain the simulated medical record.
Alternatively, the detailed function and the extended function of the program may be as described above.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device, the electronic device, the server and the storage medium disclosed by the embodiment correspond to the method disclosed by the embodiment, so that the description is relatively simple, and the relevant points can be referred to the description of the method part.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A method of generating a simulated medical record, comprising:
obtaining a reference medical record;
acquiring a diagnosis and treatment problem request, wherein the diagnosis and treatment problem request carries an identification of a target department and a target disease;
obtaining a target diagnosis and treatment problem set corresponding to the target department and the target disease from diagnosis and treatment problem sets constructed in advance for the diseases in the departments, and outputting the target diagnosis and treatment problem set; each diagnosis and treatment problem in the diagnosis and treatment problem set is constructed with a mapping relation with a field in a structured medical record in advance;
taking the selected diagnosis and treatment problem in the target diagnosis and treatment problem set as a target diagnosis and treatment problem;
acquiring answers corresponding to the target diagnosis and treatment questions from the reference medical record based on the mapping relation between the target diagnosis and treatment questions and fields in the structured medical record;
and writing the target diagnosis and treatment question and the corresponding answer into corresponding fields in a template medical record to obtain the simulated medical record.
2. The method of claim 1, wherein pre-constructing a solution for a set of clinical questions comprises:
acquiring historical diagnosis and treatment data;
cleaning the historical diagnosis and treatment data;
classifying the cleaned historical diagnosis and treatment data, wherein the types of the historical diagnosis and treatment data comprise inquiry and physical examination;
carrying out similarity normalization processing on the washed historical diagnosis and treatment data to obtain standardized historical diagnosis and treatment data;
determining a field corresponding to the standardized historical diagnosis and treatment data in the structured medical record, and establishing a mapping relation between diagnosis and treatment problems in the standardized historical diagnosis and treatment data and the field;
determining departments and diseases corresponding to diagnosis and treatment problems in the standardized historical diagnosis and treatment data;
and adding the diagnosis and treatment problems into a diagnosis and treatment problem set constructed by corresponding departments and diseases.
3. The method of claim 2, wherein said cleansing of said historical clinical data comprises:
performing word segmentation processing on the historical diagnosis and treatment data to obtain a first word segmentation result;
if at least one word in the first word segmentation result belongs to a medical knowledge base, a synonym dictionary of the medical knowledge base or a preset high-frequency medical vocabulary, the historical diagnosis and treatment data are reserved, otherwise, the historical diagnosis and treatment data are deleted.
4. The method of claim 2, wherein the classifying the cleaned historical clinical data comprises:
if the washed historical diagnosis and treatment data comprises words or inquiry keywords corresponding to the symptom body, determining that the washed historical diagnosis and treatment data is of an inquiry type;
and if the washed historical diagnosis and treatment data comprises words corresponding to the part ontology, determining that the washed historical diagnosis and treatment data is of a physical examination type.
5. The method according to claim 2, wherein the similarity normalization processing is performed on the cleaned historical clinical data to obtain standardized historical clinical data, and comprises:
determining the similarity between the historical diagnosis and treatment data belonging to the same type in the washed historical diagnosis and treatment data;
and normalizing the plurality of cleaned historical diagnosis and treatment data with the similarity reaching a preset similarity threshold value to generate a piece of standardized historical diagnosis and treatment data.
6. The method of claim 2, wherein the determining the corresponding field of the standardized historical clinical data in the structured medical record comprises:
respectively determining the correlation weight between the standardized historical diagnosis and treatment data and each field in the structured medical record;
and determining the field with the highest association weight as the corresponding field of the standardized historical diagnosis and treatment data in the structured medical record.
7. The method of claim 2, wherein the determining departments and diseases corresponding to the diagnosis and treatment questions in the standardized historical diagnosis and treatment data comprises:
determining departments corresponding to diagnosis and treatment problems in the standardized historical diagnosis and treatment data according to fields corresponding to the standardized historical diagnosis and treatment data in the structured medical records;
performing word segmentation on the standardized historical diagnosis and treatment data to obtain a second word segmentation result, determining the similarity between the second word segmentation result and the medical keywords of each disease in the department, and determining the disease with the highest similarity as the disease corresponding to the diagnosis and treatment problem in the standardized historical diagnosis and treatment data.
8. An apparatus for generating a simulated medical record, comprising:
a reference medical record obtaining unit for obtaining a reference medical record;
the system comprises a request acquisition unit, a diagnosis and treatment problem analysis unit and a diagnosis and treatment problem analysis unit, wherein the request acquisition unit is used for acquiring a diagnosis and treatment problem request which carries an identification of a target department and a target disease;
a target diagnosis and treatment problem set acquisition unit, configured to acquire a target diagnosis and treatment problem set corresponding to a target department and a target disease from diagnosis and treatment problem sets pre-constructed for the diseases in the departments, and output the target diagnosis and treatment problem set; each diagnosis and treatment problem in the diagnosis and treatment problem set is constructed with a mapping relation with a field in a structured medical record in advance;
the target diagnosis and treatment problem acquisition unit is used for taking the selected diagnosis and treatment problem in the target diagnosis and treatment problem set as a target diagnosis and treatment problem;
the diagnosis and treatment answer obtaining unit is used for obtaining an answer corresponding to the target diagnosis and treatment question in the reference medical record based on the mapping relation between the target diagnosis and treatment question and the fields in the structured medical record;
and the data processing unit is used for writing the target diagnosis and treatment questions and the corresponding answers into corresponding fields in a template medical record to obtain the simulated medical record.
9. An electronic device comprising a processor, a memory, and a communication interface;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of the method for generating a simulated medical record according to any one of claims 1 to 7.
10. A readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, performs the steps of the method for generating a simulated medical record as claimed in any of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113571167A (en) * | 2021-07-28 | 2021-10-29 | 重庆橡树信息科技有限公司 | Rapid triage system based on configured scoring knowledge model |
| CN113571167B (en) * | 2021-07-28 | 2024-04-19 | 重庆橡树信息科技有限公司 | Rapid triage system based on configuration type grading knowledge model |
| CN113792129A (en) * | 2021-09-16 | 2021-12-14 | 平安普惠企业管理有限公司 | Intelligent conversation method, device, computer equipment and medium |
| CN113792129B (en) * | 2021-09-16 | 2024-06-14 | 联通在线信息科技有限公司 | Intelligent session method, device, computer equipment and medium |
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