CN117912625A - Electronic medical record generation method based on inquiry dialogue - Google Patents
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Abstract
The invention relates to the technical field of medical data processing, in particular to an electronic medical record generation method based on inquiry dialogue. Includes determining a target medical record; determining similar medical records and obtaining associated features, determining time difference, and predicting time and quantity of triggering of target medical records or similar medical records by user groups based on the time difference and the user groups which definitely have similar symptoms with the target medical records; generating a virtual medical record, inputting the virtual medical record to a doctor end, guiding a subsequent dialogue query between the doctor end and a user group, and judging whether to directly generate the virtual medical record into an electronic medical record or not based on the matching degree of the virtual medical record and keywords. The infection risk characteristics and the association characteristics are constructed through the generated target medical records, so that the electronic medical records in the medical management platform are analyzed, the occurrence and the number of the electronic medical records in the medical management platform at different times are predicted, the medical management platform and doctors make a decision in advance, and the medical condition treatment efficiency is improved.
Description
Technical Field
The invention relates to the technical field of medical data processing, in particular to an electronic medical record generation method based on inquiry dialogue.
Background
At present, a user can directly carry out dialogue communication with a doctor through an online medical platform, so that the user can more conveniently determine the disease condition of the user, and meanwhile, the electronic medical record can be directly generated for the user to refer to.
For example, CN112036154B discloses a method, a device, a computer device and a storage medium for generating an electronic medical record based on a consultation dialogue, where the method includes: acquiring condition text data of a doctor and patient dialogue in an online consultation based on the Internet; determining the content category of the electronic case to which each clause belongs in the illness state text data; identifying similar clauses in the electronic case content category; determining repeated clauses in the similar clauses according to the doctor-patient identification, and deleting the repeated clauses in the electronic case content category; according to the method, medical records are automatically filled according to the condition text data communicated by doctors and patients, the medical record processing efficiency is improved, and the situation that similar contents are repeatedly recorded in the electronic medical records can be avoided.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides an electronic medical record generation method based on inquiry dialogue, which can effectively solve the problems that in the prior art, the influence of the medical record on the city can be analyzed according to the electronic medical record, the medical platform, the communication between doctors and users are influenced, and the condition efficiency of the users is determined.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
The invention provides a method for generating an electronic medical record based on a consultation dialogue, which at least comprises the following steps:
S1, acquiring dialogue text information between a user side and a doctor side through a medical management platform;
S2, extracting dialogue text information, determining repeated dialogue texts in the dialogue text information through a similarity comparison algorithm, eliminating the repeated dialogue texts in the dialogue text information, and constructing a dialogue text set from the eliminated dialogue text information;
S3, combining the dialogue text set with the semantic analysis model to determine keywords in dialogue text information, filling the keywords into a medical record template based on the keywords, generating an electronic medical record, analyzing whether the medical record in the electronic medical record has infection risk characteristics, and marking the medical record as a target medical record when the infection risk characteristics exist;
S4, when the target medical record is generated, acquiring the electronic medical record recorded in the medical management platform, inputting the target medical record into the medical management platform according to a similarity comparison algorithm to determine similar electronic medical records, marking the similar electronic medical record as similar medical records, acquiring the association characteristics between the similar medical record and the target medical record, and analyzing whether a time difference exists between the similar medical record and the target medical record;
S5, when a time difference exists, determining affected user groups in the urban area based on the association characteristics, and predicting time and quantity of triggering of the target medical record or the similar medical record by the user groups based on the time difference and the user groups with similar symptoms with the target medical record;
S6, determining the time and the number of the user crowd triggering the target medical records or similar medical records, generating virtual medical records in the medical management platform, inputting the virtual medical records to the doctor end when the user crowd carries out a dialogue between the medical management platform and the doctor end so as to guide the follow-up dialogue inquiry between the doctor end and the user crowd, judging keywords replied by the user crowd by combining with the semantic analysis module, and judging whether the virtual medical records are directly generated into electronic medical records or not based on the matching degree of the virtual medical records and the keywords.
Further, the similarity comparison algorithm is based on a word frequency and inverse document frequency algorithm or a cosine similarity algorithm to determine repeated dialogue texts in the fixed dialogue text information, and a dialogue text set is constructed by eliminating the repeated dialogue texts.
Further, the infection risk is characterized in that the condition in the electronic medical records has an infectious disease, and when the condition is determined to be the infectious disease, the electronic medical records corresponding to the infectious disease are marked as target medical records.
Further, the method for determining the similar medical records is as follows:
acquiring a large number of electronic medical records recorded in a medical management platform;
Comparing the target medical record with the electronic medical record in the medical management platform, determining a similarity threshold of the target medical record, and determining the electronic medical record which is similar to the target medical record based on the similarity threshold;
And marking the similar electronic medical records as similar medical records.
Further, when the similar medical records are determined, whether the association features exist between the similar medical records and the target medical records or not is obtained, wherein the association features are as follows:
The similar medical record and the target medical record have infection risk characteristics, are in the same region and are in a contact cross infection state;
When the associated features exist, calculating whether a time difference exists between the similar medical records and the target medical records, when the time difference exists, determining the attack time of the similar medical records and the target medical records, marking the attack time early as an influence starting point condition, marking the attack time late as an influence delivery condition, respectively determining the affected user population in the urban area through the influence starting point condition and the influence delivery condition, and predicting the time and the quantity of the user population triggering the target medical records or the similar medical records according to the calculated time difference.
Further, when the number of the user groups triggering the target medical record or the similar medical record is determined:
determining the number of electronic medical records to be stored and recorded in a medical management platform based on the number of user groups;
acquiring an occupied memory value of each stored recorded electronic medical record file;
And calculating the total occupied storage value of the electronic medical record files stored in the medical management platform at different times according to the occupied memory value.
Further, whether to directly generate the virtual medical record as the electronic medical record is determined, an accurate threshold condition is established, and whether to directly generate the virtual medical record as the electronic medical record is determined according to the accurate threshold condition.
Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects:
the infection risk characteristics and the association characteristics are constructed through the generated target medical records, so that the electronic medical records in the medical management platform are analyzed, the occurrence and the number of the electronic medical records in the medical management platform at different times are predicted, the medical management platform and doctors make a decision in advance, and the medical condition treatment efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention 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 evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of the overall process of the present invention;
FIG. 2 is a schematic diagram of a method for determining similar medical records according to the present invention;
fig. 3 is a schematic diagram of a method for determining total occupied storage values of electronic medical records according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is further described below with reference to examples.
Example 1 (see fig. 1-3): an electronic medical record generating method based on inquiry dialogue at least comprises the following steps:
S1, dialogue text information between the user side and the doctor side through a medical management platform is acquired, so that the communication description between the user side and the doctor side can be determined based on the dialogue text information, and the following steps are conveniently executed.
S2, extracting dialogue text information, determining repeated dialogue texts in the dialogue text information through a similarity comparison algorithm, eliminating the repeated dialogue texts in the dialogue text information, and constructing a dialogue text set from the eliminated dialogue text information; specifically, the comparison text information meeting the repeatability characteristic is removed, so that one dialogue text information is reserved in a plurality of repeated comparison text information, a large amount of dialogue text information is prevented from being processed, the efficiency of data processing and analysis is improved, and a dialogue text set can be constructed on the basis, so that the generation efficiency of the electronic medical record of a user is accelerated, wherein the following needs to be described:
for the similarity comparison algorithm, the following steps are adopted:
similarity calculation based on word frequency and inverse document frequency, specifically, the similarity of sentences is evaluated by calculating the frequency of words in sentences and the importance of the words in the whole corpus;
The similarity of the two vectors can be evaluated by a cosine similarity algorithm, specifically by calculating the cosine value of the included angle between the two vectors, so that the similarity comparison algorithm adopted by the scheme is not limited, and dialog text information with certain similarity can be conveniently determined and accurately removed.
S3, combining the dialogue text set with the semantic analysis model to determine keywords in the dialogue text information, filling the keywords into a medical record template based on the determined keywords, and generating an electronic medical record.
It should be noted that, for the semantic analysis model, a Word embedding model (such as Word2Vec, gloVe or BERT) may be used to analyze the dialogue text information, a large amount of medical text information is selected to be preprocessed, and the preprocessed medical text information is trained to be embedded into the model, so that the semantics of the current dialogue text information can be analyzed through the Word embedding model, and the keywords of the dialogue text information can be extracted based on the semantics, so that the extracted keywords are correspondingly filled into the medical record template.
Next, when the electronic medical record is generated, analyzing whether the medical record in the electronic medical record has infection risk characteristics, and when the infection risk characteristics exist, marking the medical record as a target medical record; specifically, in the present application, the infection risk feature means that a condition in an electronic medical record has a certain infection risk, and such as an influenza virus or the like that is transmitted through air, and therefore, when the medical record is an infectious disease related to an influenza virus or the like that is transmitted through air, it is determined as an infection risk feature, and thus, it is marked as a target medical record, and the following steps are performed:
S4, when the target medical record is generated, acquiring the electronic medical record recorded in the medical management platform, inputting the target medical record into the medical management platform according to a similarity comparison algorithm to determine similar electronic medical records, marking the similar electronic medical record as similar medical records, acquiring the association characteristics between the similar medical record and the target medical record, and analyzing whether a time difference exists between the similar medical record and the target medical record; specifically, a large number of electronic medical records recorded in a medical management platform are compared with a target medical record to determine an electronic medical record which is similar to the target medical record, when the electronic medical record exists, the electronic medical record is marked as a similar medical record, and for the similar medical record, comparison and judgment are performed according to information filled in a medical record template, further, through defining a similarity threshold (for example, the similarity threshold can be predefined as 80% -90%), the electronic medical record which reaches the similarity threshold can be determined to be marked and judged after comparison through a similarity comparison algorithm, and after comparison of similarity, whether the similar medical record and the target medical record have relevant characteristics is determined, wherein the relevant characteristics are as follows:
The similar medical record and the target medical record have infection risk characteristics, are in the same region and are in a contact cross infection state, that is, the users corresponding to the similar medical record and the target medical record respectively are in a certain range in a certain time in a certain region and are mutually affected by infection;
Therefore, when the above-mentioned association features are clear, it is obtained whether there is a time difference between the similar medical record and the target medical record, that is, whether there is a time sequence between the similar medical record and the target medical record, and when there is a time difference, it determines the time difference, and performs the following steps:
S5, when a time difference exists, determining affected user groups in the urban area based on the association characteristics, and predicting time and quantity of triggering of the target medical record or the similar medical record by the user groups based on the time difference and the user groups with similar symptoms with the target medical record; specifically, the time difference is determined, the attack time of the similar medical record and the target medical record is determined, the early attack time is marked as the condition affecting the initial point, the late attack time is marked as the condition affecting the delivery direction, therefore, the affected user population in the urban area can be respectively determined by the condition affecting the initial point and the condition affecting the delivery direction (the infection risk characteristic in the associated characteristic), predicting the time and quantity of triggering the target medical record or the similar medical record by the user group according to the time difference between the similar medical record and the target medical record calculated before, for the predicted time and quantity of triggering the target medical record or the similar medical record by the user group, the user group needs to be affected by infection in a certain range under a certain time in a certain area according to the time defined in the above, that is to say, the user group needs to have a user existing distance threshold corresponding to the target medical record or the similar medical record, the distance threshold value is determined according to the influence of infection on each other in a certain range in a certain area, the collected facial features of users can be compared according to the monitoring system terminals in the city to determine the distance threshold value by combining a GPS positioning system, or the distance threshold value among a plurality of intelligent terminal devices can be determined by positioning the intelligent terminal devices carried by the users, thereby being convenient for determining the distance threshold value, accurately predicting the number of the user crowd triggering target medical records or similar medical records through the distance threshold value, and for the time of the user crowd triggering the target medical records or similar medical records, determining the time difference value between the similar medical records and the target medical records can be calculated according to the time of the user crowd triggering the target medical records, the method and the system can be used for determining the number of the user groups triggering the target medical record or similar medical records in different time periods, so that the medical management and control platform can make medical associated emergency decisions in advance.
Through the implementation of the scheme, after the number of user groups which are about to trigger target medical records or similar medical records in cities under different times is predicted, the number of the electronic medical records which are about to be stored in the medical management platform under different predicted times can be determined, so that the total occupied storage value of the total electronic medical records which are about to be stored in the medical management platform under different times can be calculated according to the occupied memory value of each previous electronic medical record file, whether the remaining storage space in the medical management platform can execute the corresponding storage of the electronic medical records with the total occupied storage value under different times or not can be determined in advance, the medical management platform can be prompted in advance to release the internal storage space of the medical management platform, the medical management platform can make corresponding emergency measures when the conditions appear later, the electronic medical records can be accurately stored in the medical management platform, the safe medical treatment of the electronic medical records can be realized, and therefore, the user can be ensured to use the accurate electronic medical records when going to a hospital later.
It is worth noting that when the time and the number of the user group triggering the target medical record or the similar medical record are determined, a virtual medical record is generated in the medical management platform, and the following steps are executed:
S6, inputting the virtual medical record to the doctor end when the user crowd carries out dialogue with the doctor end through the medical management platform so as to guide the subsequent dialogue inquiry between the doctor end and the user crowd, judging the key words replied by the user crowd by combining the semantic analysis module, and judging whether to directly generate the virtual medical record into the electronic medical record or not based on the matching degree of the virtual medical record and the key words; specifically, for a virtual medical record, a specific actual electronic medical record is not generated, when the virtual medical record can be communicated with a doctor terminal through a cloud system of a medical management platform (wherein, the identity of the predicted user population is determined through a monitoring system terminal verification, so that when the user population corresponding to the virtual medical record forms dialogue communication with the doctor terminal in the medical management platform, the virtual medical record corresponding to the user population identity is transmitted to the doctor terminal), therefore, the doctor terminal can query and suspicious for symptoms to the user population by means of the virtual medical record, so as to accelerate the efficiency and speed of dialogue communication between the user population and the doctor terminal, so that the whole medical management platform accelerates the determination efficiency of conditions for the user population predicted in different time periods, and greatly improves the working efficiency at the same time, and in the system, the semantic analysis module is combined to analyze dialogue text information replied by the user population and extract key words again, so as to determine the accuracy between the key words and the virtual medical record, and whether the key words are accurately matched with the doctor is required to be directly interpreted by constructing a threshold condition (degree of matching) or not, so that the efficiency and the medical record condition of the user population is required to be directly interpreted by the doctor, and the medical record are required to be determined:
The accurate threshold conditions are: based on the virtual medical record, when the doctor side continuously proposes the keywords of the query displayed on the virtual medical record through the virtual medical record and the answers of the user population are consistent with the keywords of the virtual medical record, an accurate threshold condition is achieved (the continuously proposed query keywords can be continuously 3 times, and concrete is not limited), the corresponding virtual medical record is directly generated into the electronic medical record, and therefore the treatment efficiency of the medical management platform for determining the condition in the face of massive user population dialogue communication can be greatly improved.
It is worth to say that, when the virtual medical record is generated into the electronic medical record duration, the dialogue communication between the user group and the doctor end is ended immediately, and meanwhile, the virtual medical record is deleted in the cloud system in the medical management platform, so that the storage capacity of the cloud system is improved.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; these modifications or substitutions do not depart from the essence of the corresponding technical solutions from the protection scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. An electronic medical record generating method based on inquiry dialogue is characterized by at least comprising the following steps:
S1, acquiring dialogue text information between a user side and a doctor side through a medical management platform;
S2, extracting dialogue text information, determining repeated dialogue texts in the dialogue text information through a similarity comparison algorithm, eliminating the repeated dialogue texts in the dialogue text information, and constructing a dialogue text set from the eliminated dialogue text information;
S3, combining the dialogue text set with the semantic analysis model to determine keywords in dialogue text information, filling the keywords into a medical record template based on the keywords, generating an electronic medical record, analyzing whether the medical record in the electronic medical record has infection risk characteristics, and marking the medical record as a target medical record when the infection risk characteristics exist;
S4, when the target medical record is generated, acquiring the electronic medical record recorded in the medical management platform, inputting the target medical record into the medical management platform according to a similarity comparison algorithm to determine similar electronic medical records, marking the similar electronic medical record as similar medical records, acquiring the association characteristics between the similar medical record and the target medical record, and analyzing whether a time difference exists between the similar medical record and the target medical record;
S5, when a time difference exists, determining affected user groups in the urban area based on the association characteristics, and predicting time and quantity of triggering of the target medical record or the similar medical record by the user groups based on the time difference and the user groups with similar symptoms with the target medical record;
S6, determining the time and the number of the user crowd triggering the target medical records or similar medical records, generating virtual medical records in the medical management platform, inputting the virtual medical records to the doctor end when the user crowd carries out a dialogue between the medical management platform and the doctor end so as to guide the follow-up dialogue inquiry between the doctor end and the user crowd, judging keywords replied by the user crowd by combining with the semantic analysis module, and judging whether the virtual medical records are directly generated into electronic medical records or not based on the matching degree of the virtual medical records and the keywords.
2. The method for generating an electronic medical record based on a consultation dialogue according to claim 1, wherein the similarity comparison algorithm is based on a word frequency and inverse document frequency algorithm or a cosine similarity algorithm to determine repeated dialogue texts in dialogue text information, and constructs a dialogue text set by eliminating the repeated dialogue texts.
3. The method for generating an electronic medical record based on a consultation dialogue according to claim 1, wherein the infection risk is characterized in that a disease state in the electronic medical record is an infectious disease, and when the disease state is determined to be the infectious disease, the electronic medical record corresponding to the infectious disease is marked as a target medical record.
4. The method for generating an electronic medical record based on a consultation dialogue according to claim 1, wherein the method for determining the similar medical record is as follows:
acquiring a large number of electronic medical records recorded in a medical management platform;
Comparing the target medical record with the electronic medical record in the medical management platform, determining a similarity threshold of the target medical record, and determining the electronic medical record which is similar to the target medical record based on the similarity threshold;
And marking the similar electronic medical records as similar medical records.
5. The method for generating an electronic medical record based on a consultation dialogue according to claim 4, wherein when the similar medical record is determined, whether an association feature exists between the similar medical record and the target medical record is obtained, wherein the association feature is:
The similar medical record and the target medical record have infection risk characteristics, are in the same region and are in a contact cross infection state;
When the associated features exist, calculating whether a time difference exists between the similar medical records and the target medical records, when the time difference exists, determining the attack time of the similar medical records and the target medical records, marking the attack time early as an influence starting point condition, marking the attack time late as an influence delivery condition, respectively determining the affected user population in the urban area through the influence starting point condition and the influence delivery condition, and predicting the time and the quantity of the user population triggering the target medical records or the similar medical records according to the calculated time difference.
6. The method for generating an electronic medical record based on a consultation dialogue according to claim 5, wherein when the number of user groups triggering a target medical record or similar medical record is determined:
determining the number of electronic medical records to be stored and recorded in a medical management platform based on the number of user groups;
acquiring an occupied memory value of each stored recorded electronic medical record file;
And calculating the total occupied storage value of the electronic medical record files stored in the medical management platform at different times according to the occupied memory value.
7. The method for generating an electronic medical record based on a consultation session according to claim 1, wherein it is determined whether to directly generate a virtual medical record as an electronic medical record, an accurate threshold condition is constructed, and it is determined whether to directly generate the virtual medical record as the electronic medical record according to the accurate threshold condition.
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