CN114550870A - Prescription auditing method, device, equipment and medium based on artificial intelligence - Google Patents
Prescription auditing method, device, equipment and medium based on artificial intelligence Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses a prescription auditing method based on artificial intelligence, which comprises the following steps: receiving input information of a user; if the input information contains prescription information, generating a prescription list according to the prescription information; if the input information does not contain prescription information, receiving prescription information returned by the user according to the prescription information collection request, and generating a prescription list according to the returned prescription information; extracting information of a person taking the medicine, information of illness state and medicine information according to a prescription sheet, and analyzing medical history according to the information of the person taking the medicine to obtain forbidden medicine information; constructing a target vector matrix according to the disease condition information, and calculating the target vector matrix by using a disease condition analysis model to obtain applicable medicine information; and auditing the prescription according to the medicine information, the forbidden medicine information and the applicable medicine information to obtain an auditing result. The invention also provides a prescription auditing device, equipment and a medium based on artificial intelligence. The invention can improve the auditing accuracy of the prescription list in medicine purchase.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a prescription auditing method and device based on artificial intelligence, electronic equipment and a computer-readable storage medium.
Background
Under the support of national and local policies, internet medical treatment is flourishing, people gradually accept the form of online medical treatment, and more consumers buy medicines on line. The internet medical platform is used for selling medicines, such as prescription medicines, and the corresponding medicines are sold according to prescription orders like an off-line hospital. However, if only the prescription order photo is used as the acceptance threshold of medicine selling, the user who does not have the prescription order is limited to purchase the medicine, and the convenience of internet medicine selling is low; if only doctors are used for on-line manual examination, the workload of manual work is large and the lag of medicine selling is high; if the prescription is checked by using the intelligent database, the checking and approving angle is single and the accuracy is low.
Disclosure of Invention
The invention provides a prescription auditing method and device based on artificial intelligence and a computer readable storage medium, and mainly aims to solve the problem of low accuracy of auditing a prescription in medicine purchase.
In order to achieve the above object, the present invention provides a prescription auditing method based on artificial intelligence, which comprises:
receiving input information of a user, and judging whether prescription information exists in the input information;
if the input information contains prescription information, generating a prescription list according to the prescription information;
if the input information does not contain prescription information, sending a prescription information collection request to the user;
receiving prescription information returned by a user according to the prescription information collection request, and generating a prescription list according to the returned prescription information;
extracting information of a person taking the medicine, information of illness state and medicine information according to the prescription, and analyzing medical history according to the information of the person taking the medicine to obtain forbidden medicine information;
constructing a target vector matrix according to the illness state information, and calculating the target vector matrix by using a pre-constructed illness state analysis model to obtain applicable medicine information;
and auditing the prescription according to the medicine information, the forbidden medicine information and the applicable medicine information, and outputting an auditing result.
Optionally, the determining whether there is prescription information in the input information includes:
extracting an information format of the input information, and judging whether the information format contains a preset format or not;
if the information format comprises a preset format, determining that prescription information exists in the input information;
and if the information format does not contain a preset format, judging that the input information does not contain prescription information.
Optionally, the generating a prescription list according to the returned prescription information includes:
performing semantic analysis on the returned prescription information to obtain a plurality of semantic paragraphs;
calculating the similarity according to the plurality of semantic paragraphs and template paragraphs of a preset prescription template;
and inputting the semantic paragraphs with the similarity calculation results larger than the threshold value into the corresponding template paragraphs to obtain the square list.
Optionally, the analyzing the medical history according to the information of the person taking the medicine to obtain information of the forbidden medicine comprises:
performing word segmentation processing on the information of the person using the medicine to obtain a plurality of information word segments;
utilizing a preset forbidden medicine information base to search the plurality of information word segments one by one;
and extracting the corresponding forbidden medicine from the forbidden medicine information base according to the selected and searched information participles to obtain forbidden medicine information.
Optionally, the constructing a target vector matrix according to the disease condition information includes:
extracting data to be masked from the disease condition information, and performing masking operation on the data to be masked to obtain masked data;
performing vector conversion on all data in the masked data to obtain a vector set, and performing position coding on the vector set to obtain a positioning vector set;
and converting the positioning vector set into a positioning vector matrix, and adjusting an iteration weight factor in a pre-constructed feedforward neural network by using the positioning vector matrix to obtain a target vector matrix.
Optionally, the calculating the target vector matrix by using the pre-constructed disease analysis model to obtain the applicable drug information includes:
performing convolution, pooling and full connection on the target vector matrix for preset times through the disease analysis model to obtain disease analysis information;
and calculating to obtain the applicable medicine information corresponding to the disease state analysis information through the activator.
Optionally, the auditing the prescription order according to the medicine information, the forbidden medicine information and the applicable medicine information, and outputting an auditing result include:
judging whether the medicine information does not belong to forbidden medicine information and belongs to applicable medicine information;
if the medicine information does not belong to forbidden medicine information and belongs to applicable medicine information, the prescription is judged to be approved;
if the medicine information does not belong to forbidden medicine information and does not belong to applicable medicine information, executing and judging that the prescription order is not approved;
if the medicine information belongs to forbidden medicine information and does not belong to applicable medicine information, executing and judging that the prescription order examination does not pass;
and if the medicine information belongs to forbidden medicine information and applicable medicine information, executing and judging that the prescription order is not approved.
In order to solve the above problem, the present invention further provides an artificial intelligence based prescription auditing apparatus, comprising:
the prescription information judging module is used for receiving input information of a user and judging whether the input information contains prescription information or not;
the prescription generating module is used for generating a prescription according to the prescription information when the input information contains the prescription information; when the input information does not contain prescription information, sending a prescription information collection request to a user; receiving prescription information returned by a user according to the prescription information collection request, and generating a prescription list according to the returned prescription information;
the forbidden medicine information generating module is used for extracting information of a person taking the medicine, information of illness state and medicine information according to the prescription, and analyzing medical history according to the information of the person taking the medicine to obtain forbidden medicine information;
the applicable drug information generation module is used for constructing a target vector matrix according to the illness state information and calculating the target vector matrix by using a pre-constructed illness state analysis model to obtain applicable drug information;
and the prescription order auditing module is used for auditing the prescription order according to the medicine information, the forbidden medicine information and the applicable medicine information and outputting an auditing result.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based prescription review method described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, the at least one computer program being executed by a processor in an electronic device to implement the artificial intelligence based prescription auditing method described above.
The prescription list is generated by two modes of generating the prescription list according to the prescription information identified by the input information and generating the prescription list according to the prescription information returned by the user according to the prescription information collection request, so that the threshold of the user for purchasing the medicine on line is reduced, and the convenience of medicine purchase is improved; forbidden medicines are obtained by analyzing the medical history of the information of the person using the medicine in the prescription list; and then, a vector matrix is constructed according to the disease condition information in the prescription order, model calculation is carried out on the vector matrix by using the disease condition analysis model to obtain applicable medicine information, and then the medicine information is audited according to forbidden medicines and applicable medicines, so that the variety of audit angles of the prescription order is increased, and the audit accuracy of the prescription order is improved. Therefore, the prescription auditing method, the device, the electronic equipment and the computer readable storage medium based on artificial intelligence can solve the problem of low auditing accuracy of prescription in medicine purchase.
Drawings
FIG. 1 is a schematic flow chart illustrating a recipe review method based on artificial intelligence according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of recipe list generation for returned recipe information according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating a medical history analysis process according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of an artificial intelligence based prescription verification apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the artificial intelligence based prescription auditing method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a prescription auditing method based on artificial intelligence. The executing subject of the artificial intelligence based prescription auditing method includes, but is not limited to, at least one of the electronic devices of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the application. In other words, the artificial intelligence based recipe auditing method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Referring to fig. 1, a schematic flow chart of a recipe auditing method based on artificial intelligence according to an embodiment of the present invention is shown. In this embodiment, the recipe auditing method based on artificial intelligence includes:
s1, receiving input information of a user, and judging whether prescription information exists in the input information;
in the embodiment of the present invention, the input information of the user may be prescription list pictures provided by hospitals and clinics, tables containing prescription information and the like imported by other channels.
In an embodiment of the present invention, the determining whether there is prescription information in the input information includes:
extracting an information format of the input information, and judging whether the information format comprises a preset format or not;
if the information format comprises a preset format, determining that prescription information exists in the input information;
and if the information format does not contain a preset format, judging that the input information does not contain prescription information.
In detail, the information format may be JPG, BMP, PNG, EXCEL, or the like; if the preset information format is JPG, BMP, PNG, EXCEL, and when the information format is JPG, BMP, PNG, etc., existing in the input information, the information format includes the preset format, that is, the input information includes a prescription image, which is the prescription information.
If the input information contains prescription information, executing S2 and generating a prescription list according to the prescription information;
in the embodiment of the present invention, the prescription information may be a display form including prescription information, such as a prescription list image.
In an embodiment of the present invention, the generating a prescription list according to the prescription information includes:
performing text recognition on the prescription information to obtain a prescription text;
and generating a prescription list by using a preset prescription single template according to the prescription text.
Further, the text recognition of the prescription information to obtain a prescription text includes:
performing text type identification on the prescription information to obtain a text type corresponding to the prescription information;
selecting a corresponding text positioning model from a preset text positioning model library as a target text positioning model according to the text type;
performing text positioning on the prescription information by using the target text positioning model to obtain a text area;
and carrying out optical character text recognition on the text area to obtain a prescription text.
In the embodiment of the invention, the prescription information can be a prescription list image, and the text type comprises a print text type and a handwriting text type; text type recognition of the prescription information may be performed using an image classification model, which may be a deep learning model based on the ResNet18 algorithm; the image text type corresponding to the target text image can be the image text type corresponding to the text region to be identified of the target text image, or the image text type corresponding to the whole target text image
In the embodiment of the invention, the optical character text recognition model can be used for carrying out optical character text recognition on the text area. If the text type is a handwritten text type, inputting the text region to be recognized into a preset handwritten optical character text recognition model; and if the text type is a print form text type, inputting the text area to be recognized into a preset print form optical character text recognition model. The handwritten optical character text recognition model and the print optical character text recognition model can be convolution cyclic neural network (CRNN) models.
If the input information does not contain prescription information, executing S3 and sending a prescription information collection request to the user;
in the embodiment of the present invention, if there is no prescription information in the input information, the user needs to actively input to generate a prescription order, so that a prescription information collection request needs to be sent to the user to remind the user to actively input.
S4, receiving prescription information returned by the user according to the prescription information collection request, and generating a prescription list according to the returned prescription information;
in the embodiment of the present invention, the prescription information returned by the user according to the prescription information collection request may be prescription list pictures provided by supplementary hospitals and clinics, a form containing prescription information introduced from other channels, or a personal medical record filled in by the user according to a template attached to the prescription information collection request.
In the embodiment of the present invention, please refer to fig. 2, the generating a prescription order according to the returned prescription information includes:
s41, performing semantic analysis on the returned prescription information to obtain a plurality of semantic paragraphs;
s42, calculating the similarity according to the plurality of semantic paragraphs and template paragraphs of a preset prescription single template;
s43, inputting the semantic paragraphs with the similarity calculation results larger than the threshold value into the corresponding template paragraphs to obtain a square list.
In the embodiment of the present invention, a deep learning text classification model (e.g., fastText model, TextCNN, TextRNN + Attention, TextRCNN, etc.) in natural language learning (NPL) may be used to perform semantic analysis on the returned prescription information.
In the embodiment of the invention, the similarity between a plurality of semantic paragraphs and template paragraphs of a preset prescription single template can be calculated by utilizing cosine similarity, Pearson correlation coefficient, Euclidean distance and the like.
In one practical application scenario of the invention, a prescription filling template can be attached when a prescription information collection request is sent to a user, the user can directly input information according to the prescription single template, the input information is prescription information, and after the user returns the prescription information, the prescription single template can be used for directly generating a prescription.
S5, extracting information of a person taking the medicine, information of illness state and information of medicine according to the prescription, and analyzing medical history according to the information of the person taking the medicine to obtain information of forbidden medicine;
in the embodiment of the present invention, the information of the person using the medicine may include personal information authenticated by a real name, past medical history, medicine allergy history, family genetic disease, and the like, the information of the disease condition may be description of the disease condition of the user, and the information of the medicine may be a specific medicine name or the like that the user wants to purchase.
In the embodiment of the invention, the prescription can be a document, a table and the like with a fixed format template, and corresponding information of a medicine user, illness state information or medicine information can be obtained by extracting the content of a fixed position.
In the embodiment of the present invention, the prohibited medicine information is a medicine prohibited or carelessly taken or used by a user through past medical history, personal information, and the like of the user.
In the embodiment of the present invention, please refer to fig. 3, the analyzing the medical history according to the information of the person taking the medicine to obtain the information of the forbidden medicine includes:
s51, performing word segmentation processing on the information of the person using the medicine to obtain a plurality of information word segments;
s52, searching the plurality of information participles one by using a preset forbidden medicine information base;
and S53, extracting the corresponding forbidden medicine from the forbidden medicine information base according to the selected and searched information participles to obtain forbidden medicine information.
In the embodiment of the invention, the forbidden medicine information base comprises different medical histories, corresponding forbidden medicines, forbidden medicines corresponding to different age groups and the like.
For example, if there is an information word "diabetes", which can be retrieved in the forbidden drug information base, and the corresponding forbidden drug includes prednisone, dexamethasone, betamethasone, etc., then the forbidden drug information includes prednisone, dexamethasone, betamethasone; if the information participle of '5 years old' exists, the participle can be searched in the forbidden drug information base, and the corresponding forbidden drug comprises hydroxychloroquine, imipramine, ranitidine and the like, the forbidden drug information comprises hydroxychloroquine, imipramine, ranitidine.
S6, constructing a target vector matrix according to the illness state information, and calculating the target vector matrix by using a pre-constructed illness state analysis model to obtain applicable medicine information;
in an embodiment of the present invention, the applicable drug information is drug information to be used, which is obtained according to the disease condition information.
In an embodiment of the present invention, the constructing a target vector matrix according to the disease condition information includes:
extracting data to be masked from the disease condition information, and performing masking operation on the data to be masked to obtain masked data;
performing vector conversion on all data in the masked data to obtain a vector set, and performing position coding on the vector set to obtain a positioning vector set;
and converting the positioning vector set into a positioning vector matrix, and adjusting an iteration weight factor in a pre-constructed feedforward neural network by using the positioning vector matrix to obtain a target vector matrix.
In the embodiment of the invention, keywords can be extracted from the data to be masked according to a preset mask probability, and mask operation is performed on the keywords to obtain masked words; and in the data to be masked, replacing the key words with the masked words to obtain the masked data.
In the embodiment of the invention, a Word2vec algorithm can be adopted to perform vector conversion on all data in the masked data.
In an embodiment of the present invention, before the calculating the target vector matrix by using the pre-constructed disease analysis model to obtain the applicable drug information, the method further includes:
acquiring training illness state information and real adaptive medicine information corresponding to the training illness state information;
constructing a vector matrix of the training disease condition information, and inputting the vector matrix into a pre-constructed disease condition analysis model to obtain an output result of the disease condition analysis model;
and calculating by using a preset disease name loss function to obtain the loss value of the output result and the truly adaptive medicine information, and optimizing the disease analysis model according to the loss value to obtain a standard disease analysis model.
In an embodiment of the present invention, the disease analysis model is a pre-training language model, including but not limited to a BERT model (Bidirectional Encoder Representations from transforms), and an LSTM model (Long-Short Term Memory model).
In an embodiment of the present invention, the calculating the target vector matrix by using the pre-constructed disease analysis model to obtain the applicable drug information includes:
performing convolution, pooling and full connection on the target vector matrix for preset times through the disease analysis model to obtain disease analysis information;
and calculating to obtain the applicable medicine information corresponding to the disease state analysis information through the activator.
For example, if the disease condition is described as "headache, low fever", the disease condition is input to the disease condition analysis model, and the output result is acetaminophen, ibuprofen, and aspirin, the output result can be used as the information of the applicable drugs.
And S7, checking the prescription according to the medicine information, the forbidden medicine information and the applicable medicine information, and outputting a checking result.
In an embodiment of the present invention, the auditing the prescription according to the medicine information, the forbidden medicine information, and the applicable medicine information, and outputting an auditing result includes:
judging whether the medicine information does not belong to forbidden medicine information and belongs to applicable medicine information;
if the medicine information does not belong to forbidden medicine information and belongs to applicable medicine information, the prescription is judged to be approved;
if the medicine information does not belong to forbidden medicine information and does not belong to applicable medicine information, executing and judging that the prescription order is not approved;
if the medicine information belongs to forbidden medicine information and does not belong to applicable medicine information, executing and judging that the prescription order examination does not pass;
and if the medicine information belongs to forbidden medicine information and applicable medicine information, executing and judging that the prescription order is not approved.
In the embodiment of the invention, forbidden medicine information and applicable medicine information are obtained by analyzing the information of the person who takes the medicine and the information of the state of illness respectively, and the medicine range is marked out; and when the medicine information belongs to the applicable medicine information and the forbidden medicine information, the medicine information does not belong to the applicable medicine information and the forbidden medicine information, or the medicine information does not belong to the applicable medicine information and the forbidden medicine information, the medicine information is judged not to be taken, namely the audit is not passed.
In the embodiment of the invention, if the medicine information does not belong to forbidden medicine information and belongs to applicable medicine information, the medicine is indicated and is applicable to the condition of the illness of the user, the medicine is applicable to the user, certain harm can not be caused to the user, and the purchase permission of the medicine can be granted; if the medicine information does not belong to forbidden medicine information and does not belong to applicable medicine information, the situation that the medicine is not applicable to the illness state of the user is indicated, and the purchasing authority of the medicine is not granted; if the medicine information belongs to forbidden medicine information and does not belong to applicable medicine information, the medicine is indicated to be not applicable to the user, certain harm may be caused to the user, the medicine is not applicable to the condition of the patient, and the purchase permission of the medicine should not be granted; if the medicine information belongs to the forbidden medicine information and the applicable medicine information, the medicine is not applicable to the user, certain harm may be caused to the user, and the purchase permission of the medicine should not be granted.
In an optional embodiment of the invention, after the audit is not passed due to the fact that the audit does not belong to the applicable medicine information, the reason why the audit is not passed and the suggested applicable medicine can be displayed to the user; after the approval fails due to the information of the forbidden medicines, the reason of the failure of the approval, the recommended applicable medicines and the medicines forbidden to be applied can be displayed for the user; after the audit is not passed due to belonging to the prohibited drug information, the reason for the audit not being passed may be displayed to the user, as well as paying attention to the prohibition of applicable drugs.
In another optional embodiment of the invention, after the review fails, the user can further extract the request of manual review according to the feedback information, and the on-line doctor is used for reviewing the prescription list.
The prescription list is generated by two modes of generating the prescription list according to the prescription information identified by the input information and generating the prescription list according to the prescription information returned by the user according to the prescription information collection request, so that the threshold of the user for purchasing the medicine on line is reduced, and the convenience of medicine purchase is improved; forbidden medicines are obtained by analyzing the medical history of the information of the person using the medicine in the prescription list; and then, a vector matrix is constructed according to the disease condition information in the prescription order, model calculation is carried out on the vector matrix by using the disease condition analysis model to obtain applicable medicine information, and then the medicine information is audited according to forbidden medicines and applicable medicines, so that the variety of audit angles of the prescription order is increased, and the audit accuracy of the prescription order is improved. Therefore, the prescription auditing method based on artificial intelligence can solve the problem of low auditing accuracy of prescription in medicine purchase.
Fig. 4 is a functional block diagram of a prescription auditing apparatus based on artificial intelligence according to an embodiment of the present invention.
The prescription auditing device 100 based on artificial intelligence can be installed in electronic equipment. According to the realized functions, the artificial intelligence based prescription auditing device 100 can comprise a prescription information judging module 101, a prescription list generating module 102, a forbidden medicine information generating module 103, an applicable medicine information generating module 104 and a prescription list auditing module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform a fixed function, and are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the prescription information judging module 101 is configured to receive input information of a user, and judge whether prescription information exists in the input information;
the prescription list generating module 102 is configured to generate a prescription list according to prescription information when the input information includes the prescription information; when the input information does not contain prescription information, sending a prescription information collection request to a user; receiving prescription information returned by a user according to the prescription information collection request, and generating a prescription list according to the returned prescription information;
the forbidden medicine information generation module 103 is used for extracting information of a person taking the medicine, information of illness and medicine information according to the prescription, and analyzing medical history according to the information of the person taking the medicine to obtain forbidden medicine information;
the applicable drug information generating module 104 is configured to construct a target vector matrix according to the disease condition information, and calculate the target vector matrix by using a pre-constructed disease condition analysis model to obtain applicable drug information;
and the prescription order auditing module 105 is configured to audit the prescription order according to the medicine information, the forbidden medicine information and the applicable medicine information, and output an auditing result.
In detail, when the modules in the manual-intelligence-based prescription auditing apparatus 100 according to the embodiment of the present invention are used, the same technical means as the manual-intelligence-based prescription auditing method described in fig. 1 to 3 are adopted, and the same technical effects can be produced, which are not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a recipe auditing method based on artificial intelligence according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as an artificial intelligence based recipe auditing program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a recipe auditing program based on artificial intelligence, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a prescription auditing program based on artificial intelligence, etc., but also to temporarily store data that has been output or is to be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 5 only shows an electronic device with components, and it will be understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The artificial intelligence based prescription auditing program stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, enable:
receiving input information of a user, and judging whether prescription information exists in the input information;
if the input information contains prescription information, generating a prescription list according to the prescription information;
if the input information does not contain prescription information, sending a prescription information collection request to the user;
receiving prescription information returned by a user according to the prescription information collection request, and generating a prescription list according to the returned prescription information;
extracting information of a person taking the medicine, information of illness state and medicine information according to the prescription, and analyzing medical history according to the information of the person taking the medicine to obtain forbidden medicine information;
constructing a target vector matrix according to the illness state information, and calculating the target vector matrix by using a pre-constructed illness state analysis model to obtain applicable medicine information;
and auditing the prescription according to the medicine information, the forbidden medicine information and the applicable medicine information, and outputting an auditing result.
Specifically, the specific implementation method of the processor 10 for the instruction may refer to the description of the relevant steps in the embodiment corresponding to the drawing, and is not repeated here.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
receiving input information of a user, and judging whether prescription information exists in the input information;
if the input information contains prescription information, generating a prescription list according to the prescription information;
if the input information does not contain prescription information, sending a prescription information collection request to the user;
receiving prescription information returned by a user according to the prescription information collection request, and generating a prescription list according to the returned prescription information;
extracting information of a person taking the medicine, information of illness state and medicine information according to the prescription order, and analyzing medical history according to the information of the person taking the medicine to obtain information of forbidden medicines;
constructing a target vector matrix according to the illness state information, and calculating the target vector matrix by using a pre-constructed illness state analysis model to obtain applicable medicine information;
and auditing the prescription according to the medicine information, the forbidden medicine information and the applicable medicine information, and outputting an auditing result.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A prescription auditing method based on artificial intelligence, the method comprising:
receiving input information of a user, and judging whether prescription information exists in the input information;
if the input information contains prescription information, generating a prescription list according to the prescription information;
if the input information does not contain prescription information, sending a prescription information collection request to the user;
receiving prescription information returned by a user according to the prescription information collection request, and generating a prescription list according to the returned prescription information;
extracting information of a person taking the medicine, information of illness state and medicine information according to the prescription order, and analyzing medical history according to the information of the person taking the medicine to obtain information of forbidden medicines;
constructing a target vector matrix according to the illness state information, and calculating the target vector matrix by using a pre-constructed illness state analysis model to obtain applicable medicine information;
and auditing the prescription according to the medicine information, the forbidden medicine information and the applicable medicine information, and outputting an auditing result.
2. The artificial intelligence based prescription auditing method of claim 1, where said determining if there is prescription information in the input information comprises:
extracting an information format of the input information, and judging whether the information format contains a preset format or not;
if the information format comprises a preset format, determining that prescription information exists in the input information;
and if the information format does not contain a preset format, judging that the input information does not contain prescription information.
3. The artificial intelligence based prescription review method of claim 1, wherein the generating a prescription order from the returned prescription information comprises:
performing semantic analysis on the returned prescription information to obtain a plurality of semantic paragraphs;
calculating the similarity according to the plurality of semantic paragraphs and template paragraphs of a preset prescription template;
and inputting the semantic paragraphs with the similarity calculation results larger than the threshold value into the corresponding template paragraphs to obtain the square list.
4. The artificial intelligence based prescription auditing method of claim 1 where said performing medical history analysis based on said medication person information to obtain forbidden drug information comprises:
performing word segmentation processing on the information of the person using the medicine to obtain a plurality of information word segments;
utilizing a preset forbidden medicine information base to search the plurality of information word segments one by one;
and extracting the corresponding forbidden medicine from the forbidden medicine information base according to the selected and searched information participles to obtain forbidden medicine information.
5. The artificial intelligence based prescription review method of claim 1, wherein the constructing a target vector matrix according to the condition information comprises:
extracting data to be masked from the disease condition information, and performing masking operation on the data to be masked to obtain masked data;
performing vector conversion on all data in the masked data to obtain a vector set, and performing position coding on the vector set to obtain a positioning vector set;
and converting the positioning vector set into a positioning vector matrix, and adjusting an iteration weight factor in a pre-constructed feedforward neural network by using the positioning vector matrix to obtain a target vector matrix.
6. The artificial intelligence based prescription auditing method of claim 1 where the calculating of the target vector matrix using a pre-constructed disease analysis model to obtain applicable drug information comprises:
performing convolution, pooling and full connection on the target vector matrix for preset times through the disease analysis model to obtain disease analysis information;
and calculating to obtain the applicable medicine information corresponding to the disease state analysis information through the activator.
7. The artificial intelligence based prescription auditing method according to any one of claims 1 to 6, wherein said auditing said prescription order and outputting an auditing result according to said drug information, said forbidden drug information and said applicable drug information comprises:
judging whether the medicine information does not belong to forbidden medicine information and belongs to applicable medicine information;
if the medicine information does not belong to forbidden medicine information and belongs to applicable medicine information, the prescription is judged to be approved;
if the medicine information does not belong to forbidden medicine information and does not belong to applicable medicine information, executing and judging that the prescription order is not approved;
if the medicine information belongs to forbidden medicine information and does not belong to applicable medicine information, executing and judging that the prescription order examination does not pass;
and if the medicine information belongs to forbidden medicine information and applicable medicine information, executing and judging that the prescription order is not approved.
8. An artificial intelligence based prescription auditing apparatus, the apparatus comprising:
the prescription information judging module is used for receiving input information of a user and judging whether the input information contains prescription information or not;
the prescription generating module is used for generating a prescription according to the prescription information when the input information contains the prescription information; when the input information does not contain prescription information, sending a prescription information collection request to a user; receiving prescription information returned by a user according to the prescription information collection request, and generating a prescription list according to the returned prescription information;
the forbidden medicine information generating module is used for extracting information of a person taking the medicine, information of illness state and medicine information according to the prescription, and analyzing medical history according to the information of the person taking the medicine to obtain forbidden medicine information;
the applicable drug information generation module is used for constructing a target vector matrix according to the illness state information and calculating the target vector matrix by using a pre-constructed illness state analysis model to obtain applicable drug information;
and the prescription order auditing module is used for auditing the prescription order according to the medicine information, the forbidden medicine information and the applicable medicine information and outputting an auditing result.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based prescription review method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the artificial intelligence based prescription auditing method according to any one of claims 1-7.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210288219.0A CN114550870A (en) | 2022-03-23 | 2022-03-23 | Prescription auditing method, device, equipment and medium based on artificial intelligence |
| PCT/CN2022/122999 WO2023178978A1 (en) | 2022-03-23 | 2022-09-30 | Prescription review method and apparatus based on artificial intelligence, and device and medium |
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| CN202210288219.0A CN114550870A (en) | 2022-03-23 | 2022-03-23 | Prescription auditing method, device, equipment and medium based on artificial intelligence |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115719628A (en) * | 2022-11-16 | 2023-02-28 | 联仁健康医疗大数据科技股份有限公司 | Traditional Chinese medicine prescription generation method, device, equipment and storage medium |
| CN116631573A (en) * | 2023-07-25 | 2023-08-22 | 讯飞医疗科技股份有限公司 | Prescription drug auditing method, device, equipment and storage medium |
| WO2023178978A1 (en) * | 2022-03-23 | 2023-09-28 | 康键信息技术（深圳）有限公司 | Prescription review method and apparatus based on artificial intelligence, and device and medium |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107731270A (en) * | 2017-10-25 | 2018-02-23 | 康美药业股份有限公司 | A kind of method and device of automatic examination ＆ verification prescription accuracy |
| CN108899070B (en) * | 2018-05-31 | 2024-02-02 | 平安医疗科技有限公司 | Prescription recommendation generation method, device, computer equipment and storage medium |
| CN109920508B (en) * | 2018-12-28 | 2022-11-01 | 安徽省立医院 | Prescription auditing method and system |
| KR102156197B1 (en) * | 2019-05-09 | 2020-09-15 | 박회준 | Method and device for prividing medical information through prescription information |
| CN110853723A (en) * | 2019-11-24 | 2020-02-28 | 曹庆恒 | Intelligent medicine guidance method, system and equipment |
| JP6890364B1 (en) * | 2021-02-19 | 2021-06-18 | 株式会社ミナケア | Prescription support system, prescription support method, information processing device and their methods and programs |
| CN113764066A (en) * | 2021-03-29 | 2021-12-07 | 北京京东拓先科技有限公司 | Method and device for auditing prescription data |
| CN113782146B (en) * | 2021-09-16 | 2023-08-22 | 平安科技（深圳）有限公司 | Artificial intelligence-based general medicine recommendation method, device, equipment and medium |
| CN114550870A (en) * | 2022-03-23 | 2022-05-27 | 康键信息技术（深圳）有限公司 | Prescription auditing method, device, equipment and medium based on artificial intelligence |
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- 2022-03-23 CN CN202210288219.0A patent/CN114550870A/en active Pending
- 2022-09-30 WO PCT/CN2022/122999 patent/WO2023178978A1/en unknown
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023178978A1 (en) * | 2022-03-23 | 2023-09-28 | 康键信息技术（深圳）有限公司 | Prescription review method and apparatus based on artificial intelligence, and device and medium |
| CN115719628A (en) * | 2022-11-16 | 2023-02-28 | 联仁健康医疗大数据科技股份有限公司 | Traditional Chinese medicine prescription generation method, device, equipment and storage medium |
| CN116631573A (en) * | 2023-07-25 | 2023-08-22 | 讯飞医疗科技股份有限公司 | Prescription drug auditing method, device, equipment and storage medium |
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