CN111508579A - Method, device, equipment and storage medium for continuously circulating electronic prescription - Google Patents
Method, device, equipment and storage medium for continuously circulating electronic prescription Download PDFInfo
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- CN111508579A CN111508579A CN202010613766.2A CN202010613766A CN111508579A CN 111508579 A CN111508579 A CN 111508579A CN 202010613766 A CN202010613766 A CN 202010613766A CN 111508579 A CN111508579 A CN 111508579A
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H20/13—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered from dispensers
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention relates to the technical field of artificial intelligence, and particularly discloses a method, a device, computer equipment and a computer readable storage medium for sustainable circulation of an electronic prescription, wherein the method comprises the following steps: obtaining an electronic prescription and body parameters of a user in a block chain; predicting first medicine information corresponding to the body parameters according to a preset neural network model; determining whether to modify the electronic prescription based on the electronic prescription and the first drug information; if the electronic prescription is determined not to be modified, prompt information is sent to the equipment of the user, so that the electronic prescription of the user can be rapidly determined to continuously circulate according to the body parameters of the user, the inquiry times of the user are reduced, and the time of the user is saved.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for sustainable transfer of electronic prescriptions, a computer device, and a computer-readable storage medium.
Background
Electronic prescription (Electronic prescription) refers to a medical Electronic document which is transmitted by means of a network, programmed by adopting an information technology, filled with medicine treatment information in diagnosis and treatment activities, made into a prescription, transmitted to a pharmacy through the network, audited, allocated, checked and charged by professional pharmacy technicians, and used for dispensing medicines and taking medicines in the pharmacy. At present, chronic patients need continuous medication, but as the chronic patients are mostly old people, the operation of going to and fro a hospital for many times or frequently operating an electronic device to communicate with a doctor on line to prepare a new electronic prescription is not only troublesome, but also increases much time, money and energy cost.
Disclosure of Invention
The present application mainly aims to provide a method, an apparatus, a computer device and a computer readable storage medium for sustainable circulation of electronic prescriptions, which aims to solve the technical problems that patients with chronic diseases need to take medicines continuously, but the patients need to go to and fro a hospital for many times or frequently operate the electronic device to communicate with doctors online to prepare new electronic prescriptions, which is not only troublesome, but also increases much time, money and energy costs.
In a first aspect, the present application provides a method for sustainable circulation of electronic prescriptions, comprising the steps of:
acquiring an electronic prescription and body parameters of a user in a block chain;
predicting first medicine information corresponding to the body parameters according to a preset neural network model;
determining whether to modify the electronic prescription based on the electronic prescription and the first drug information;
and if the electronic prescription is determined not to be modified, sending prompt information to the equipment of the user.
In a second aspect, the present application also provides a sustainable electronic prescription circulation apparatus, including:
the acquisition module is used for acquiring the electronic prescription and the body parameters of the user in the block chain;
the prediction module is used for predicting first medicine information corresponding to the body parameters according to a preset neural network model;
a determination module to determine whether to modify the electronic prescription based on the electronic prescription and the first drug information;
and the sending module is used for sending prompt information to the equipment of the user if the electronic prescription is determined not to be modified.
In a third aspect, the present application further provides a computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the steps of the method for sustainable circulation of electronic prescriptions as described above.
In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, where the computer program, when executed by a processor, implements the steps of the method for sustainable flow of electronic prescriptions as described above.
The application provides a method, a device, computer equipment and a computer readable storage medium for continuously circulating electronic prescriptions, which are used for obtaining electronic prescriptions and body parameters of users in a block chain; predicting first medicine information corresponding to the body parameters according to a preset neural network model; determining whether to modify the electronic prescription based on the electronic prescription and the first drug information; if the electronic prescription is determined not to be modified, prompt information is sent to the equipment of the user, so that the electronic prescription of the user can be rapidly determined to continuously circulate according to the body parameters of the user, the inquiry times of the user are reduced, and the time of the user is saved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flowchart illustrating a method for sustainable flow of electronic prescriptions according to an embodiment of the present disclosure;
FIG. 2 is a flow diagram illustrating sub-steps of the method for sustainable flow of electronic prescriptions of FIG. 1;
FIG. 3 is a flow diagram illustrating sub-steps of the method for sustainable flow of electronic prescriptions of FIG. 1;
FIG. 4 is a flowchart illustrating another method for sustainable flow of electronic prescriptions according to an embodiment of the present disclosure;
FIG. 5 is a schematic block diagram of a sustainable electronic prescription flow apparatus according to an embodiment of the present disclosure;
fig. 6 is a block diagram schematically illustrating a structure of a computer device according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The embodiment of the application provides a method and a device for continuously circulating electronic prescriptions, computer equipment and a computer readable storage medium. The method for continuously circulating the electronic prescription can be applied to terminal equipment, and the terminal equipment can be electronic equipment such as a mobile phone, a tablet computer, a notebook computer and a desktop computer.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for sustainable flow of electronic prescriptions according to an embodiment of the present application.
As shown in fig. 1, the method for sustainable circulation of electronic prescriptions includes steps S101 to S104.
Step S101, acquiring an electronic prescription and a body parameter of a user in a block chain.
The user uploads the detected body parameters to a blockchain through equipment, wherein the blockchain is a medical system of a hospital or a community. The equipment comprises a mobile terminal and a fixed terminal, such as a mobile phone, a computer and the like. The user detects the current blood pressure value through the blood pressure measuring instrument, the current blood sugar value through the blood sugar measuring instrument and the like, and uploads the detected blood pressure value, blood sugar value and the like as body parameters to the block chain through the mobile terminal or the fixed terminal. Or automatically uploading the detected body parameters such as the blood pressure value, the blood sugar value and the like to a block chain through the wearable device, wherein the electronic prescription is uploaded to the block chain by a doctor or a user, and the block chain records a plurality of electronic prescriptions of the user.
And S102, predicting first medicine information corresponding to the body parameters according to a preset neural network model.
And training the neural network model through training data in advance to obtain a preset neural network model. For example, a physical parameter, a corresponding drug type, and a drug dose of the drug type are obtained as training data, wherein the physical parameter includes a heartbeat parameter, a blood pressure parameter, a blood glucose parameter, a blood lipid parameter, and the like. The medicine types comprise furosemide, hydrochlorothiazide, valsartan, candesartan, captopril, perindopril and the like. The dosage of the medicine comprises 20-40mg (1-2 tablets) of furosemide taken orally, 1 time per day, 25-50mg of hydrochlorothiazide each time, and 1-2 times per day. And training the characteristic vector information of the neural network model through the training data to obtain the trained preset neural network model. Calling a preset neural network model, predicting body parameters of a user through the preset neural network model, and predicting first medicine information corresponding to the body parameters of the user, wherein the first medicine information comprises a first medicine type and medicine dosage information of the first medicine type.
In an embodiment, specifically referring to fig. 2, step S102 includes: substeps 1021 to substep S1023.
And a substep S1021, inputting the body parameters into an input layer of a preset neural network model.
And inputting the body parameters uploaded by the user into an input layer of the preset neural network model, wherein the body parameters comprise heartbeat parameters, blood sugar parameters, blood fat parameters and the like. For example, the heartbeat parameters 110 beats/minute, the blood pressure 90/150, etc.
And a substep S1022, obtaining parameter feature vector information of the body parameter through a hidden layer of the preset neural network model.
Parameter values of body parameters are obtained through a neural unit in a hidden layer of a preset neural network model, and parameter feature vector information of the parameter values is obtained through a weight matrix in the hidden layer. And obtaining the identification between the parameters and the medicine information through the training data to be identified in advance to train the weight matrix of the neural network model. And through the weight matrix of the trained neural network model, when the parameter value is obtained through the neural unit in the hidden layer, the parameter weight matrix of the parameter value is obtained through mapping of the weight matrix.
And a substep S1023 of predicting first medicine information corresponding to the body parameter through an output layer of the neural network model based on the parameter feature vector information.
And acquiring the medicine information corresponding to the parameter characteristic vector information when the parameter characteristic vector information mapped by the weight matrix is passed through the output layer of the neural network model. Wherein the output layer includes a drug classifier. The medicine classifier acquires the probability value of the medicine information corresponding to the parameter feature vector information based on the parameter feature vector information, compares the acquired probability values of the corresponding medicine information, and predicts that the first medicine information with the maximum probability value is the first medicine information, so that the first medicine information corresponding to the body parameter is output.
Step S103, determining whether to modify the electronic prescription based on the electronic prescription and the first medicine information.
And acquiring the medicine types in the electronic prescription, and comparing the medicine types in the electronic prescription with the medicine types in the target medicine information so as to determine whether the electronic prescription needs to be newly made. For example, the number of the types of medicines in the electronic prescription and the number of the types of target medicines are obtained, and if the number of the types of medicines in the electronic prescription is the same as the number of the types of target medicines, it is determined that the electronic prescription does not need to be re-made; and if the number of the medicine types in the electronic prescription is different from the number of the target medicine types, determining that the electronic prescription needs to be newly made.
In an embodiment, specifically referring to fig. 3, step S103 includes: sub-step S1031 to sub-step S1032.
And a substep S1031 of obtaining second medicine information in the electronic prescription.
And acquiring second medicine information in the electronic prescription, wherein the second medicine information comprises a second medicine name and second medicine dosage information, and the second medicine dosage information comprises dosage information of the second medicine, component content information of the second medicine and the like. The method for acquiring the electronic prescription information includes image processing, character recognition and the like.
In one embodiment, the obtaining of the second medicine information in the electronic prescription includes: extracting a font track in the electronic prescription; acquiring at least one of characters, numbers or letters matched with the font track; and acquiring the second medicine information by combining the characters, the numbers or the letters.
And identifying the electronic prescription, and extracting a font track in the electronic prescription. And matching the extracted font track with a preset font track library through the preset font track library to obtain the identifier of the preset font track matched with the preset font track library. Wherein the mark comprises only one of characters, numbers and letters, such as, for example, furosemide, rice, oral, clothes, 20, 40, m, g, 1, 2, tablets and the like, and the obtained furosemide, oral, clothes, 20, 40, m, g, 1, 2 and tablets are sequenced according to the position to obtain the furosemide oral 20-40mg1-2 tablets.
And a substep S1032 of comparing the first medicine information with the second medicine information and determining whether to modify the electronic prescription.
And when the second medicine information in the electronic prescription is acquired, comparing the acquired second medicine information with the first medicine information. And if the first medicine information is consistent with the second medicine information, determining not to modify the electronic prescription. For example, if the first drug information is 20-40mg (1-2 tablets) of furosemide for oral administration and the second drug information is 20-40mg (1-2 tablets) of furosemide for oral administration, it is determined that the electronic prescription does not need to be modified. And if the first medicine information is inconsistent with the second medicine information, determining to modify the electronic prescription. For example, if the first medicine information is 20-40mg (1-2 tablets) of furosemide oral administration and the second medicine information is 20-40mg (3 tablets) of furosemide oral administration, determining that the electronic prescription needs to be modified; or the first medicine information is 20-40mg (1-2 tablets) of furosemide oral administration, and the second medicine information is 25-50mg of hydrochlorothiazide each time, and the electronic prescription is determined to need to be modified.
And step S104, if the electronic prescription is determined not to be modified, sending prompt information to the equipment of the user.
And if the electronic prescription is determined not to be newly issued, sending prompt information to the equipment of the user in a mode of e-mail, telephone, short message and the like, wherein the prompt information comprises character information, picture information, voice information and the like. The prompt message also includes information on whether to deliver the medicine, etc.
In the embodiment, the electronic prescription and the body parameters of the user in the block chain are obtained, the first medicine information corresponding to the body parameters is predicted according to the preset neural network model, the first medicine information is compared with the second medicine information in the electronic prescription, if the comparison is consistent, the electronic prescription is determined not to be modified, and prompt information is sent to equipment of the user, so that the user is prevented from re-online/offline inquiry, the inquiry times of the chronic patient are reduced, and the time is saved.
Referring to fig. 4, fig. 4 is a flowchart illustrating another method for sustainable flow of electronic prescriptions according to an embodiment of the present application.
As shown in fig. 4, the method for sustainable circulation of electronic prescriptions includes steps S201 to S207.
Step S201, acquiring an electronic prescription and a body parameter of a user in a block chain.
The user uploads the detected body parameters to a blockchain through equipment, wherein the blockchain is a medical system of a hospital or a community. The equipment comprises a mobile terminal and a fixed terminal, such as a mobile phone, a computer and the like. The user detects the current blood pressure value through the blood pressure measuring instrument, the current blood sugar value through the blood sugar measuring instrument and the like, and uploads the detected blood pressure value, blood sugar value and the like as body parameters to the block chain through the mobile terminal or the fixed terminal. Or automatically uploading the detected body parameters such as the blood pressure value, the blood sugar value and the like to a block chain through the wearable device, wherein the electronic prescription is uploaded to the block chain by a doctor or a user, and the block chain records a plurality of electronic prescriptions of the user.
Step S202, predicting first medicine information corresponding to the body parameters according to a preset neural network model.
And training the neural network model through training data in advance to obtain a preset neural network model. For example, a physical parameter, a corresponding drug type, and a drug dose of the drug type are obtained as training data, wherein the physical parameter includes a heartbeat parameter, a blood glucose parameter, a blood lipid parameter, and the like. The medicine types comprise furosemide, hydrochlorothiazide, valsartan, candesartan, captopril, perindopril and the like. The dosage of the medicine comprises 20-40mg (1-2 tablets) of furosemide taken orally, 1 time per day, 25-50mg of hydrochlorothiazide each time, and 1-2 times per day. And training the characteristic vector information of the neural network model through the training data to obtain the trained preset neural network model. Calling a preset neural network model, predicting body parameters of a user through the preset neural network model, and predicting first medicine information corresponding to the body parameters of the user, wherein the first medicine information comprises a first medicine type and medicine dosage information of the first medicine type.
And step S203, acquiring second medicine information in the electronic prescription.
And acquiring second medicine information in the electronic prescription, wherein the second medicine information comprises a second medicine name and second medicine dosage information, and the second medicine dosage information comprises dosage information of the second medicine, component content information of the second medicine and the like. The method for acquiring the electronic prescription information includes image processing, character recognition and the like.
Step S204, comparing the first medicine information with the second medicine information, and determining whether to modify the electronic prescription.
And when the second medicine information in the electronic prescription is acquired, comparing the acquired second medicine information with the first medicine information. And determining whether to modify the electronic prescription by comparing the second medicine information with the first medicine information. The first medicine information comprises a first medicine name; the second medicine information includes a second medicine name.
Step S205, if the first medicine name is consistent with the second medicine name, determining that the electronic prescription does not need to be re-issued.
And comparing the first medicine name with the second medicine name, and if the first medicine name is consistent with the second medicine name, determining that the electronic prescription does not need to be newly issued. For example, if the first drug information is orally administered furosemide and the second drug information is orally administered furosemide, it is determined that the electronic prescription does not need to be modified.
The first medicine information further comprises first medicine dosage information, wherein the first medicine dosage information comprises first amount information and first component information of the medicine; wherein the second drug information further includes second drug dose information, wherein the second drug dose information includes second usage information and second component information of the drug. If the first amount information is consistent with the second drug dosage information and/or the first component information is consistent with the second component information, it is determined that the electronic prescription does not need to be modified.
For example, if the first drug name is consistent with the second drug name, the first amount information is 1 time a day and 1-2 tablets each time, and the second amount information is 1 time a day and 1 tablet each time, it is determined that the electronic prescription does not need to be modified. And/or determining that the electronic prescription does not need to be modified if the first component information comprises dibazole, promethazine hydrochloride, chloroquine phosphate and guanoxan sulfate and the second component information comprises dibazole, promethazine hydrochloride, chloroquine phosphate and guanoxan sulfate.
Step S206, if the first medicine name is not consistent with the second medicine name, determining that the electronic prescription needs to be renewed.
And comparing the first medicine name with the second medicine name, and if the first medicine name is inconsistent with the second medicine name, determining that the electronic prescription needs to be renewed. For example, if the first drug information is furosemide oral administration and the second drug information is hydrochlorothiazide, it is determined that the electronic prescription needs to be modified.
And step S207, if the electronic prescription is determined not to be modified, sending prompt information to the equipment of the user.
And if the electronic prescription is determined not to be newly issued, sending prompt information to the equipment of the user in a mode of e-mail, telephone, short message and the like, wherein the prompt information comprises character information, picture information, voice information and the like. The prompt message also includes information on whether to deliver the medicine, etc.
And S208, sending the body parameters, the first medicine information and the electronic prescription to a node corresponding to a doctor in the block chain, and sending prompt information.
After the electronic prescription is determined to need to be newly issued, the body parameters of the user, the first medicine information and the electronic prescription flow to nodes corresponding to doctors in a block chain, the block chain comprises the nodes corresponding to the doctors, the nodes corresponding to the patients and the like, the patients upload the body parameters to a public chain of the block chain, the body parameters flow to a preset neural network model, the preset neural network model determines the first medicine information of the patients based on the body parameters of the patients, and the first medicine information flows to the nodes corresponding to the electronic prescription for comparison. And if the electronic prescription needs to be newly issued after comparison, the electronic prescription flows to a node corresponding to the doctor and sends prompt information, wherein the sending mode comprises mails, telephones, short messages and the like, and the prompt information comprises characters, pictures, voice and the like.
In the embodiment, the electronic prescription and the body parameters of the user in the block chain are obtained, the first medicine information corresponding to the body parameters is predicted according to the preset neural network model, the first medicine information is compared with the second medicine information in the electronic prescription, if the comparison is consistent, the electronic prescription is determined not to be modified, and prompt information is sent to equipment of the user, so that the user is prevented from re-online/offline inquiry, the inquiry times of the chronic patient are reduced, and the time is saved. If the comparison is inconsistent, the electronic prescription is determined to need to be modified, and prompt information is sent to the doctor node so as to upload the body parameters of the user in time and improve the diagnosis and treatment efficiency.
Referring to fig. 5, fig. 5 is a schematic block diagram of a sustainable electronic prescription flow apparatus according to an embodiment of the present disclosure.
As shown in fig. 5, the electronic prescription sustainable flow apparatus 400 includes: an obtaining module 401, a predicting module 402, a determining module 403 and a sending module 404.
An obtaining module 401, configured to obtain an electronic prescription and a body parameter of a user in a block chain;
a predicting module 402, configured to predict, according to a preset neural network model, first medicine information corresponding to the physical parameter;
a determination module 403, configured to determine whether to modify the electronic prescription based on the electronic prescription and the first medicine information;
a sending module 404, configured to send a prompt message to the device of the user if it is determined that the electronic prescription is not modified.
The prediction module 402 is further specifically configured to:
inputting the body parameters into an input layer of a preset neural network model; extracting parameter values of the body parameters through a convolutional layer and a pooling layer of the preset neural network model, and acquiring feature vector information of the parameter values; and predicting first medicine information corresponding to the body parameters based on the full-connected layer of the neural network model and the characteristic vector information.
Wherein, the determining module 403 is specifically further configured to:
acquiring second medicine information in the electronic prescription; and comparing the first medicine information with the second medicine information to determine whether to modify the electronic prescription.
Wherein, the determining module 403 is specifically further configured to:
if the first medicine name is consistent with the second medicine name, determining that the electronic prescription does not need to be re-issued; and if the first medicine name is inconsistent with the second medicine name, determining that the electronic prescription needs to be renewed.
Wherein, the determining module 403 is specifically further configured to:
and if the dosage information of the first medicine name is consistent with the dosage information of the second medicine name, determining that the electronic prescription does not need to be re-issued.
Wherein, the sustainable circulation of electron prescription device is still used for:
and sending the body parameters, the first medicine information and the electronic prescription to a node corresponding to the doctor in the block chain, and sending prompt information.
Wherein, the obtaining module 401 is specifically further configured to:
extracting a font track in the electronic prescription; acquiring at least one of characters, numbers or letters matched with the font track; and acquiring the second medicine information by combining the characters, the numbers or the letters.
It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working processes of the apparatus, the modules and the units described above may refer to the corresponding processes in the foregoing method embodiment for sustainable circulation of electronic prescriptions, and are not described herein again.
The apparatus provided by the above embodiments may be implemented in the form of a computer program, which can be run on a computer device as shown in fig. 6.
Referring to fig. 6, fig. 6 is a schematic block diagram illustrating a structure of a computer device according to an embodiment of the present disclosure. The computer device may be a terminal.
As shown in fig. 6, the computer device includes a processor, a memory, and a network interface connected by a system bus, wherein the memory may include a nonvolatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform any one of the methods for sustainable flow of electronic prescriptions.
The processor is used for providing calculation and control capability and supporting the operation of the whole computer equipment.
The internal memory provides an environment for the execution of a computer program on a non-volatile storage medium, which when executed by the processor, causes the processor to perform any of a number of methods for sustainable delivery of electronic prescriptions.
The network interface is used for network communication, such as sending assigned tasks and the like. Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein, in one embodiment, the processor is configured to execute a computer program stored in the memory to implement the steps of:
acquiring an electronic prescription and body parameters of a user in a block chain;
predicting first medicine information corresponding to the body parameters according to a preset neural network model;
determining whether to modify the electronic prescription based on the electronic prescription and the first drug information;
and if the electronic prescription is determined not to be modified, sending prompt information to the equipment of the user.
In one embodiment, when the determining, according to the preset neural network model, that the first drug information corresponding to the body parameter is implemented, the processor is configured to implement:
inputting the body parameters into an input layer of a preset neural network model; acquiring parameter characteristic vector information of the body parameters through a hidden layer of the preset neural network model; and predicting first medicine information corresponding to the body parameters based on the parameter feature vector information through an output layer of the neural network model.
In one embodiment, the processor, in said determining whether to modify the electronic prescription implementation based on the electronic prescription and the first drug information, is to implement:
acquiring second medicine information in the electronic prescription; and comparing the first medicine information with the second medicine information to determine whether to modify the electronic prescription.
In one embodiment, the processor, when implemented subsequent to the determining whether to modify the electronic prescription, is configured to implement:
if the first medicine name is consistent with the second medicine name, determining that the electronic prescription does not need to be re-issued; and if the first medicine name is inconsistent with the second medicine name, determining that the electronic prescription needs to be renewed.
In one embodiment, the processor is; if the first medicine name is consistent with the second medicine name, then the following steps are implemented:
and if the dosage information of the first medicine name is consistent with the dosage information of the second medicine name, determining that the electronic prescription does not need to be re-issued.
In one embodiment, the processor, when implemented after determining that the electronic prescription needs to be redescrowed, is configured to implement:
and sending the body parameters, the first medicine information and the electronic prescription to a node corresponding to the doctor in the block chain, and sending prompt information.
In one embodiment, the processor, in obtaining the second drug information implementation in the electronic prescription, is configured to implement:
extracting a font track in the electronic prescription; acquiring at least one of characters, numbers or letters matched with the font track; and acquiring the second medicine information by combining the characters, the numbers or the letters.
Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, where the computer program includes program instructions, and when the program instructions are executed, a method implemented by the computer program may refer to various embodiments of the method for sustainable circulation of electronic prescriptions of the present application.
The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device.
Further, the computer-readable storage medium may mainly 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, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as electronic prescription and body parameter storage, point-to-point transmission, consensus mechanism, 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.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments. While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for sustainable circulation of electronic prescriptions, comprising:
acquiring an electronic prescription and body parameters of a user in a block chain;
predicting first medicine information corresponding to the body parameters according to a preset neural network model;
determining whether to modify the electronic prescription based on the electronic prescription and the first drug information;
and if the electronic prescription is determined not to be modified, sending prompt information to the equipment of the user.
2. The method for electronic prescription sustainable flow according to claim 1, wherein the determining the first medicine information corresponding to the physical parameter according to a preset neural network model comprises:
inputting the body parameters into an input layer of a preset neural network model;
acquiring parameter characteristic vector information of the body parameters through a hidden layer of the preset neural network model;
and predicting first medicine information corresponding to the body parameters based on the parameter feature vector information through an output layer of the neural network model.
3. The method of claim 1, wherein the determining whether to modify the electronic prescription based on the electronic prescription and the first drug information comprises:
acquiring second medicine information in the electronic prescription;
and comparing the first medicine information with the second medicine information to determine whether to modify the electronic prescription.
4. The method of claim 3, wherein the first drug information comprises a first drug name and the second drug information comprises a second drug name; after the determining whether to modify the electronic prescription, the method includes:
if the first medicine name is consistent with the second medicine name, determining that the electronic prescription does not need to be re-issued;
and if the first medicine name is inconsistent with the second medicine name, determining that the electronic prescription needs to be renewed.
5. The electronic prescription sustainable flow method of claim 4, wherein the first drug information comprises dose information for a first drug name and the second drug information comprises dose information for a second drug name; after the first medicine name is consistent with the second medicine name, the method further comprises the following steps:
and if the dosage information of the first medicine name is consistent with the dosage information of the second medicine name, determining that the electronic prescription does not need to be re-issued.
6. The method of claim 4, wherein after determining that the electronic prescription needs to be redescrowed, further comprising:
and sending the body parameters, the first medicine information and the electronic prescription to a node corresponding to the doctor in the block chain, and sending prompt information.
7. A method for sustainable flow of electronic prescriptions as in claim 3, wherein the obtaining second drug information in the electronic prescription comprises:
extracting a font track in the electronic prescription;
acquiring at least one of characters, numbers or letters matched with the font track;
and acquiring the second medicine information by combining the characters, the numbers or the letters.
8. An electronic prescription sustainable circulation apparatus, comprising:
the acquisition module is used for acquiring the electronic prescription and the body parameters of the user in the block chain;
the prediction module is used for predicting first medicine information corresponding to the body parameters according to a preset neural network model;
a determination module to determine whether to modify the electronic prescription based on the electronic prescription and the first drug information;
and the sending module is used for sending prompt information to the equipment of the user if the electronic prescription is determined not to be modified.
9. A computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the steps of the method for sustainable flow of electronic prescriptions as recited in any of claims 1 to 7.
10. A computer-readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for sustainable flow of electronic prescriptions of any of claims 1 to 7.
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