CN114357951A - Method, device, equipment and storage medium for generating standard report - Google Patents
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Abstract
The application discloses a method, a device, equipment and a storage medium for generating a standard report, and relates to the technical field of artificial intelligence. One embodiment of the method comprises: acquiring a target report; inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; acquiring corresponding standard text information from a preset association relation library according to the text structure information; and generating a standard report according to the standard text information.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for generating a standard report.
Background
With the rapid development of the mobile internet, along with the continuous breakthrough of various technologies and the promotion of consumers to the service experience requirements under the condition of consumption upgrade, the traditional service industry is actively integrated into the internet era, and various platforms are communicated with an offline service mechanism through the butt joint of systems, data and services, so that more accurate and rapid and convenient offline services are provided for the consumers.
In the related art, report formats pushed to the platform by different suppliers for users to view are inconsistent, so that the platform is inconvenient to manage the different suppliers. The reading analysis cost of the user is increased under the condition, and meanwhile, the platform cannot effectively collect and analyze results of multiple reports for the user, and provides more targeted suggestions for the user.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a storage medium for generating a standard report.
In a first aspect, an embodiment of the present application provides a method for generating a standard report, the method including: acquiring a target report; inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; acquiring corresponding standard text information from a preset association relation library according to the text structure information; and generating a standard report according to the standard text information.
In some embodiments, the predetermined associative relationship library is determined based on the following steps: obtaining a plurality of initial reports; establishing an incidence relation between the plurality of initial reports and the standard text information in response to the similarity between the plurality of initial reports and the standard text information meeting a preset similarity threshold; and constructing a preset incidence relation library according to the incidence relation.
In some embodiments, in response to the similarity between the plurality of initial reports and the standard text information satisfying a preset similarity threshold, establishing an association between the plurality of initial reports and the standard text information includes: extracting keywords in a plurality of initial reports and standard text information respectively; and in response to the similarity between the keywords in the plurality of initial reports and the standard text information meeting a preset similarity threshold, establishing an association relationship between the plurality of initial reports and the standard text information.
In some embodiments, in response to the similarity between the keywords in the plurality of initial reports and the standard text information satisfying a preset similarity threshold, establishing an association between the plurality of initial reports and the standard text information includes: acquiring the priority of keywords in the initial report and the standard text information; and in response to the similarity between the keywords with the priority levels larger than the preset priority threshold value meeting the preset similarity threshold value, establishing an association relationship between the plurality of initial reports and the standard text information.
In some embodiments, the keywords in the standard textual information include at least one medical standard term.
In some embodiments, inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report, includes: and responding to the target report with the target format, inputting the target report into a pre-trained text recognition model, and obtaining text structure information corresponding to the target report.
In some embodiments, the method of generating a standard report further comprises: and in response to that the format of the target report is not the target format, converting the format of the target report into the target format by using a conversion mode corresponding to the format of the target report.
In a second aspect, an embodiment of the present application provides an apparatus for generating a standard report, the apparatus including: the report acquisition module is used for acquiring a target report; the information obtaining module is used for inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; the information acquisition module is used for acquiring corresponding standard text information from a preset association relation library according to the text structure information; and the report generating module is used for generating a standard report according to the standard text information.
In some embodiments, the means for generating a standard table further comprises: the report acquisition module is also used for acquiring a plurality of initial reports; the relation establishing module is used for responding that the similarity between the plurality of initial reports and the standard text information meets a preset similarity threshold value and establishing the incidence relation between the plurality of initial reports and the standard text information; and the relational database building module is used for building a preset relational database according to the incidence relation.
In some embodiments, the relationship establishing module comprises: the keyword extraction unit is used for respectively extracting keywords in the plurality of initial reports and the standard text information; and the relationship establishing unit is used for responding to the similarity between the plurality of initial reports and the keywords in the standard text information to meet a preset similarity threshold value, and establishing the association relationship between the plurality of initial reports and the standard text information.
In some embodiments, the relationship establishing unit is further configured to: acquiring the priority of keywords in the initial report and the standard text information; and in response to the similarity between the keywords with the priority levels larger than the preset priority threshold value meeting the preset similarity threshold value, establishing an association relationship between the plurality of initial reports and the standard text information.
In some embodiments, the keywords in the standard textual information include at least one medical standard term.
In some embodiments, the information obtaining module is further configured to: and responding to the target report with the target format, inputting the target report into a pre-trained text recognition model, and obtaining text structure information corresponding to the target report.
In some embodiments, the means for generating a standard table further comprises: and the format conversion module is used for converting the format of the target report into the target format by utilizing a conversion mode corresponding to the format of the target report in response to that the format of the target report is not the target format.
In a third aspect, embodiments of the present application provide an electronic device comprising at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in the first aspect.
In a fourth aspect, embodiments of the present application provide a non-transitory computer readable storage medium having stored thereon computer instructions, wherein the computer instructions are configured to cause a computer to perform the method as described in the first aspect.
According to the method, the device, the equipment and the storage medium for generating the standard report, firstly, a target report is obtained; then inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; then according to the text structure information, acquiring corresponding standard text information from a preset association relation library; and then generating a standard report according to the standard text information. The text structure information corresponding to the target report can be obtained by utilizing a pre-trained text recognition model; and then, acquiring corresponding standard text information from a preset incidence relation library according to the text structure information to generate a standard report, thereby realizing the standardization of the non-standard report.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram for one embodiment of a method of generating a standard report according to the present application;
FIG. 3 is a flow diagram for one embodiment of a method of generating a standard report according to the present application;
FIG. 4 is a block diagram of an embodiment of an apparatus for generating a standard report according to the present application;
FIG. 5 is a schematic block diagram of an electronic device suitable for use in implementing embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the method of generating a standard report or the apparatus for generating a standard report of the present application may be applied.
As shown in fig. 1, system architecture 100 may include servers 101 and 102, a network 103, and an electronic device 104. Network 103 is the medium used to provide communication links between servers 101 and 102 and electronic device 104. Network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The users may use the servers 101 and 102 to interact with the electronic device 104, such as target reporting, over the network 103.
The servers 101 and 102 may be hardware or software. When the servers 101 and 102 are hardware, they may be implemented as a distributed server cluster composed of multiple servers, or may be implemented as a single server. When servers 101 and 102 are software, they may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The electronic device 104 may provide various services. For example, the electronic device 104 may obtain a target report; inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; acquiring corresponding standard text information from a preset association relation library according to the text structure information; and generating a standard report according to the standard text information.
The electronic device 104 may be hardware or software. When the electronic device 104 is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server; or the electronic device 104 may be an electronic product that performs man-machine interaction with a user through one or more modes of a keyboard, a touch pad, a touch screen, a remote controller, a voice interaction device, or a handwriting device, for example, a PC (Personal Computer), a mobile phone, a smart phone, a PDA (Personal Digital Assistant), a wearable device, a PPC (Pocket PC, palmtop), a tablet Computer, a smart car machine, a smart television, a smart sound box, a tablet Computer, a laptop Computer, a desktop Computer, and the like. When the electronic device 104 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the method for generating a standard report provided in the embodiment of the present application is generally performed by the electronic device 104, and accordingly, the apparatus for generating a standard report is generally disposed in the electronic device 104.
It should be understood that the number of electronic devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of electronic devices, networks, and servers, as desired for implementation.
With continuing reference to FIG. 2, a flow diagram 200 of one embodiment of a method of generating a standard report according to the present application is shown that may include the steps of:
In the present embodiment, an execution subject (e.g., the electronic device 104 shown in fig. 1) of the method of generating a standard report may acquire a target report of a terminal device (e.g., the servers 101 and 102 shown in fig. 1) through a network (e.g., the network 103 shown in fig. 1). The target report may be a report formed by medical-related data provided by different suppliers, such as a physical examination report.
In this embodiment, the executing entity may input the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report. The text recognition model is used for obtaining text structure information corresponding to the target report, and the text structure information can be stored in a key-value pair mode.
In this embodiment, the text recognition model may be determined based on the following steps: acquiring text structure information and a corresponding behavior text label; and then, training by using the text structure information and the corresponding text labels to obtain a text recognition model. During training, the execution subject may use the text structure information as an input of the text recognition model, and use a text label corresponding to the text structure information as an expected output, to obtain the text recognition model. The machine learning model may be a probability model, a classification model, or other classifier in the prior art or future development technology, for example, the machine learning model may include any one of the following: decision tree model (XGBoost), logistic regression model (LR), deep neural network model (DNN).
In this embodiment, the execution main body may obtain the corresponding standard text information from a preset association relation library according to the text structure information. The preset association relation library may be configured to store a correspondence between the standard text information and the text structure information.
In this embodiment, the execution subject may obtain a plurality of initial reports and standard reports, and construct an association relationship between the plurality of initial reports and the standard reports; and then, establishing an association relation library in advance according to the association relation.
In one example, the target report may include a table, and the association relationship may further include: the field category to which the text in the cell belongs, namely the field category to which the text structure information belongs. The field categories may be used to characterize the categories of textual structure information stored by the cell, e.g., department, project, location, etc.
And step 204, generating a standard report according to the standard text information.
In this embodiment, the execution subject may integrate the standard text information to generate a standard report.
In one example, according to the position information of the text structure information in the target report, the standard text information corresponding to the text structure information is displayed at the position of the text structure information to form the standard report. The standard report may be medical-related data as opposed to a target report, and the standard report may be used to measure whether the data in the target report meets relevant medical standards.
It should be noted that the standard report includes standard text information corresponding to the text structure information in the target report, as compared with the target report.
The data (e.g., target report, physical examination report, standard report, etc.) referred to in this embodiment are obtained with the consent of the user or by compliance means.
In this embodiment, after generating the standard report, the method for generating the standard report may further include: reminder information is sent to servers (e.g., servers 101 and 102 shown in fig. 1) to remind of viewing the standard report. The reminding information can be reminded in the modes of short messages, interface highlighting and the like.
In this embodiment, after the standard report is generated, the standard report may also be classified for the supplier of the target report.
The method for generating the standard report, provided by the embodiment of the application, comprises the steps of firstly obtaining a target report; then inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; then according to the text structure information, acquiring corresponding standard text information from a preset association relation library; and then generating a standard report according to the standard text information. The text structure information corresponding to the target report can be obtained by utilizing a pre-trained text recognition model; and then, acquiring corresponding standard text information from a preset incidence relation library according to the text structure information to generate a standard report, thereby realizing the standardization of the non-standard report.
Referring to FIG. 3, a flow diagram 300 of one embodiment of a method of generating a standard report according to the present application is shown that may include the steps of:
In the present embodiment, an execution subject (e.g., the electronic device 104 shown in fig. 1) of the method of generating a standard report may acquire a target report of a terminal device (e.g., the servers 101 and 102 shown in fig. 1) through a network (e.g., the network 103 shown in fig. 1). The target report may be a report formed by medical-related data provided by different suppliers.
In this embodiment, the executing entity may input the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report. The text recognition model is used for obtaining text structure information corresponding to the target report, and the text structure information can be stored in a key-value pair mode.
In this embodiment, the text recognition model may be determined based on the following steps: acquiring text structure information and a corresponding behavior text label; and then, training by using the text structure information and the corresponding text labels to obtain a text recognition model. During training, the execution subject may use the text structure information as an input of the text recognition model, and use a text label corresponding to the text structure information as an expected output, to obtain the text recognition model. The machine learning model may be a probability model, a classification model, or other classifier in the prior art or future development technology, for example, the machine learning model may include any one of the following: decision tree model (XGBoost), logistic regression model (LR), deep neural network model (DNN).
In this embodiment, the execution subject described above may acquire a plurality of initial reports on the terminal devices (e.g., servers 101 and 102 shown in fig. 1) through a network (e.g., network 103 shown in fig. 1). The initial reports may be reports formed from medical-related data provided by different suppliers, and the plurality of initial reports may include target reports.
And step 304, in response to the similarity between the plurality of initial reports and the standard text information meeting a preset similarity threshold, establishing an association relationship between the plurality of initial reports and the standard text information.
In this embodiment, the executing entity may establish an association relationship between a plurality of initial reports and the standard text information when the similarity between the plurality of initial reports and the standard text information satisfies a preset similarity threshold.
In one example, the similarity between the initial report a, the initial report B, the initial report C and the standard text information satisfies a preset similarity threshold, and the association between the initial report a and the standard text information, the association between the initial report B and the standard text information, and the association between the initial report C and the standard text information may be established.
It should be noted that the preset similarity threshold may be determined according to the requirement of the user on the standard text information or the standard precision of the standard text information.
And 305, constructing a preset association relation library according to the association relation.
In this embodiment, the execution subject may construct a preset association library according to the association generated in step 305, where the preset association library may be used to obtain standard text information corresponding to the text structure information through indexing.
It should be noted that the plurality of initial reports may be initial reports provided by different suppliers. The association may include an association between the vendor's initial report and the standard report.
It should be noted that the execution sequence among the steps 301 to 305 can be arbitrarily combined, for example, the steps 303 to 305 can be executed before the step 302, for example, the steps 303 to 305 are executed before the step 301 or the steps 303 to 305 are executed between the steps 301 and 302. Step 303 may also be performed simultaneously with step 301 or step 303 may be performed simultaneously with step 302.
And step 306, acquiring corresponding standard text information from a preset association relation library according to the text structure information.
In this embodiment, the execution main body may obtain the corresponding standard text information from a preset association relation library according to the text structure information. The preset association relation library may be configured to store a correspondence between the standard text information and the text structure information.
In this embodiment, the execution subject may integrate the standard text information to generate a standard report.
In this embodiment, the specific operations of steps 301, 302, 306, and 307 are substantially the same as the operations of steps 201, 202, 203, and 204 in the embodiment shown in fig. 2, and are not repeated herein.
As can be seen from fig. 3, compared with the embodiment corresponding to fig. 2, in the embodiment, the method 300 for generating a standard report obtains a plurality of initial reports, and when the similarity between the plurality of initial reports and the standard text information satisfies a preset similarity threshold, establishes an association relationship between the plurality of initial reports and the standard text information to construct an association relationship library, so that text structure information corresponding to a target report can be obtained by using a pre-trained text recognition model; and then, acquiring corresponding standard text information from a preset incidence relation library according to the text structure information to generate a standard report, thereby realizing the standardization of the non-standard report.
In some optional implementations of this embodiment, the preset association relation library is determined based on the following steps: obtaining a plurality of initial reports; establishing an incidence relation between the plurality of initial reports and the standard text information in response to the similarity between the plurality of initial reports and the standard text information meeting a preset similarity threshold; and constructing a preset incidence relation library according to the incidence relation.
In this implementation, the execution body may obtain a plurality of initial reports; when the similarity between the text structure information and the standard text information in the initial reports meets a preset similarity threshold, establishing an incidence relation between the initial reports and the standard text information; and then, constructing a preset incidence relation library according to the incidence relation. The initial report may be a report before conversion into a standard report, and may include a plurality of text structure information, as opposed to the standard report, and the plurality of initial reports may include the target report. The above-described associations may be used to characterize the similarity between the initial report and the standard textual information.
The similarity threshold may be set according to a conversion degree of the initial report into the standard report.
In this implementation manner, an association relationship between the initial report and the standard text information may be established according to the similarity between the text structure information in the plurality of initial reports and the standard text information, so as to construct an association relationship library.
In some optional implementations of this embodiment, in response to that the similarity between the multiple initial reports and the standard text information satisfies a preset similarity threshold, establishing an association relationship between the multiple initial reports and the standard text information includes: extracting keywords in a plurality of initial reports and standard text information respectively; and in response to the similarity between the keywords in the plurality of initial reports and the standard text information meeting a preset similarity threshold, establishing an association relationship between the plurality of initial reports and the standard text information.
In this implementation, the execution body may extract keywords in a plurality of initial reports and standard text messages; and then, establishing an association relation between the plurality of initial reports and the standard text information when the similarity between the keywords in the plurality of initial reports and the standard text information meets a preset similarity threshold.
Here, extracting keywords from the plurality of initial reports and the standard text information, respectively, may include: keywords in the plurality of initial reports and the standard text information can be respectively extracted through TF-IDF (term frequency-inverse document frequency); or extracting keywords in a plurality of initial reports and standard text information through a text extraction model. The keyword may be a word capable of standardizing the contents of the initial report.
In one example, the keyword may be a keyword with a priority ranking of 3 among the plurality of keywords.
It should be noted that the conversion degree of the initial report into the standard report may be set or randomly set by the user.
In this implementation manner, the execution subject may establish an association relationship between the initial report and the standard text information according to the similarity between the text structure information in the plurality of initial reports and the keywords in the standard text information.
In some optional implementations of this embodiment, in response to a similarity between keywords in the plurality of initial reports and the standard text information satisfying a preset similarity threshold, establishing an association relationship between the plurality of initial reports and the standard text information includes: acquiring the priority of keywords in the initial report and the standard text information; and in response to the similarity between the keywords with the priority levels larger than the preset priority threshold value meeting the preset similarity threshold value, establishing an association relationship between the plurality of initial reports and the standard text information.
In this implementation manner, the execution subject may obtain priorities of the initial report and the keywords in the standard text message; and establishing an association relation between a plurality of initial reports and standard text information when the similarity between the keywords with the priority levels larger than the preset priority threshold value meets the preset similarity threshold value.
In one example, the keyword may be a keyword with a priority ranking of 3 among the plurality of keywords.
It should be noted that the conversion degree of the initial report into the standard report may be set or randomly set by the user.
In this implementation manner, the execution subject may establish an association relationship between the initial report and the standard text information according to a similarity between the text structure information in the plurality of initial reports and the keyword in the standard text information, where the priority is greater than a preset priority threshold.
In some alternative implementations of the present embodiment, the keywords in the standard textual information include at least one medical standard term.
In this implementation, the keywords in the standard textual information may include at least one medical standard term. The medical standard term may be a term described in a medical book.
In one example, the medical standard term may include: the examination item name and the examination part name.
It should be noted that standard text information may also be constructed according to departments.
In one example, standard text information corresponding to at least one of the detection item, the examination site, and the department may be displayed on a screen of an electronic device (e.g., the electronic device 104 shown in fig. 1) for a user to browse corresponding data.
In an example, the execution subject may further cluster the standard reports, for example, cluster the standard reports of the same supplier, cluster the same detection site, and the like; or clustering the same detection part in the standard report of the same supplier to realize multi-dimensional clustering.
It should be noted that the multidimensional clustering can be a plurality of different dimensions, such as suppliers, sites, detection items, and the like.
In some optional implementation manners of this embodiment, inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report, where the text structure information includes: and responding to the target report with the target format, inputting the target report into a pre-trained text recognition model, and obtaining text structure information corresponding to the target report.
In this implementation manner, when the format of the target report is the target format, the execution subject inputs the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report. The above formats are presentation formats of target reports, for example, pdf (Portable Document Format), jpg (joint Photographic Experts group), image file storage Format (PNG), image file Format (Bitmap, BMP), text Document (txt), and word.
In one example, if the format of the initial report is a PDF format, the format of the initial report is converted to a string format using a PDF parser to conform the format of the standard report to the format of the converted initial report.
It should be noted that the format may not only be limited to the above format, but also include a display text format, such as font size, font color, font spacing, and the like.
In some optional implementations of this embodiment, the method of generating a standard report further includes: and in response to that the format of the target report is not the target format, converting the format of the target report into the target format by using a conversion mode corresponding to the format of the target report.
In this implementation, the execution body may convert the format of the target report into the target format by using a conversion method corresponding to the format of the target report when the format of the target report is not the target format.
In one example, if the format of the initial report is a PDF format, the format of the initial report is converted to a string format using a PDF parser to conform the format of the standard report to the format of the converted initial report.
With further reference to fig. 4, as an implementation of the method shown in the above figures, the present application discloses an embodiment of an apparatus for generating a standard report, which corresponds to the embodiment of the method shown in fig. 2, and which can be applied in various electronic devices.
As shown in fig. 4, an embodiment of the present application provides an apparatus 400 for generating a standard report, the apparatus 400 including: a report acquisition module 401, an information acquisition module 402, an information acquisition module 403, and a report generation module 404. The report acquiring module 401 is configured to acquire a target report; an information obtaining module 402, configured to input the target report into a pre-trained text recognition model, and obtain text structure information corresponding to the target report; an information obtaining module 403, configured to obtain corresponding standard text information from a preset association relation library according to the text structure information; and a report generating module 404, configured to generate a standard report according to the standard text information.
In this embodiment, in the apparatus 400 for generating a standard report, specific processes of the report obtaining module 401, the information obtaining module 402, the information obtaining module 403, and the report generating module 404 and technical effects thereof may refer to steps 201 to 204 in the corresponding embodiment of fig. 2, respectively.
In some embodiments, the means for generating a standard report further comprises: a report obtaining module 401, further configured to obtain a plurality of initial reports; the relation establishing module is used for responding that the similarity between the plurality of initial reports and the standard text information meets a preset similarity threshold value and establishing the incidence relation between the plurality of initial reports and the standard text information; and the relational database building module is used for building a preset relational database according to the incidence relation.
In some embodiments, the relationship establishing module comprises: the keyword extraction unit is used for respectively extracting keywords in the plurality of initial reports and the standard text information; and the relationship establishing unit is used for responding to the similarity between the plurality of initial reports and the keywords in the standard text information to meet a preset similarity threshold value, and establishing the association relationship between the plurality of initial reports and the standard text information.
In some embodiments, the relationship establishing unit is further configured to: acquiring the priority of keywords in the initial report and the standard text information; and in response to the similarity between the keywords with the priority levels larger than the preset priority threshold value meeting the preset similarity threshold value, establishing an association relationship between the plurality of initial reports and the standard text information.
In some embodiments, the keywords in the standard textual information include at least one medical standard term.
In some embodiments, the information obtaining module 402 is further configured to: and responding to the target report with the target format, inputting the target report into a pre-trained text recognition model, and obtaining text structure information corresponding to the target report.
In some embodiments, the means for generating a standard report further comprises: and the format conversion module is used for converting the format of the target report into the target format by utilizing a conversion mode corresponding to the format of the target report in response to that the format of the target report is not the target format.
As shown in fig. 5, it is a block diagram of an electronic device according to the method of generating a standard report of the embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 5, the electronic apparatus includes: one or more processors 501, memory 502, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 5, one processor 501 is taken as an example.
The memory 502, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method of generating a standard report in the embodiments of the present application (e.g., the report acquisition module 401, the information obtainment module 402, the information acquisition module 403, and the report generation module 404 shown in fig. 4). The processor 501 executes various functional applications of the server and data processing, i.e., a method of generating a standard report in the above-described method embodiment, by executing the non-transitory software programs, instructions, and modules stored in the memory 502.
The memory 502 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the information processing electronic device based on the block chain, and the like. Further, the memory 502 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 502 optionally includes memory located remotely from processor 501, which may be connected to blockchain based information processing electronics over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the method of generating a standard report may further comprise: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the blockchain-based information processing electronic apparatus, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or other input devices. The output devices 504 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (12)
1. A method of generating a standard report, comprising:
acquiring a target report;
inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report;
acquiring corresponding standard text information from a preset association relation library according to the text structure information;
and generating a standard report according to the standard text information.
2. The method of claim 1, wherein the predetermined associative relation library is determined based on the steps of:
obtaining a plurality of initial reports;
in response to the similarity between the plurality of initial reports and the standard text information meeting a preset similarity threshold, establishing an association relationship between the plurality of initial reports and the standard text information;
and constructing the preset incidence relation library according to the incidence relation.
3. The method of claim 2, wherein said establishing an association between the plurality of initial reports and the standard textual information in response to the similarity between the plurality of initial reports and the standard textual information satisfying a preset similarity threshold comprises:
extracting keywords in the plurality of initial reports and the standard text information respectively;
and in response to the similarity between the keywords in the plurality of initial reports and the standard text information meeting a preset similarity threshold, establishing an association relationship between the plurality of initial reports and the standard text information.
4. The method of claim 3, wherein said establishing an association between the plurality of initial reports and standard textual information in response to a similarity between keywords in the plurality of initial reports and the standard textual information satisfying a preset similarity threshold comprises:
acquiring the priority of the keywords in the initial report and the standard text information;
and in response to the similarity between the keywords with the priority levels larger than the preset priority threshold value meeting the preset similarity threshold value, establishing the association relationship between the plurality of initial reports and the standard text information.
5. The method according to any one of claims 1-4, wherein the keywords in the standard textual information include at least one medical standard term.
6. The method according to any one of claims 1-4, wherein the inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report comprises:
and responding to the target report with a target format, inputting the target report into a pre-trained text recognition model, and obtaining text structure information corresponding to the target report.
7. The method of claim 6, further comprising:
and in response to that the format of the target report is not the target format, converting the format of the target report into the target format by using a conversion mode corresponding to the format of the target report.
8. An apparatus for generating a standard report, comprising:
the report acquisition module is used for acquiring a target report;
the information obtaining module is used for inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report;
the information acquisition module is used for acquiring corresponding standard text information from a preset association relation library according to the text structure information;
and the report generating module is used for generating a standard report according to the standard text information.
9. The apparatus of claim 8, the apparatus further comprising:
the report acquisition module is further used for acquiring a plurality of initial reports;
the relation establishing module is used for responding to the similarity between the plurality of initial reports and the standard text information and meeting a preset similarity threshold value, and establishing the incidence relation between the plurality of initial reports and the standard text information;
and the relational database construction module is used for constructing the preset relational database according to the incidence relation.
10. The apparatus of claim 9, wherein the relationship establishing module comprises:
a keyword extraction unit, configured to extract keywords in the plurality of initial reports and the standard text information, respectively;
and the relationship establishing unit is used for establishing the association relationship between the plurality of initial reports and the standard text information in response to the similarity between the keywords in the plurality of initial reports and the standard text information meeting a preset similarity threshold.
11. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
12. A non-transitory computer readable storage medium having stored thereon a computer program of instructions, wherein the program when executed by a processor implements the method of any of claims 1-7.
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