CN109637619A - Recognition methods, device, server and the medium of antimicrobial behavior are issued in violation of rules and regulations - Google Patents
Recognition methods, device, server and the medium of antimicrobial behavior are issued in violation of rules and regulations Download PDFInfo
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Abstract
The invention discloses recognition methods, device, server and media that antimicrobial behavior is issued in a kind of violation, the method includes the steps: obtain the diagnosis and treatment data of user's upload, and Forecasting recognition is carried out using the diagnosis and treatment data as the input of preset data identification model, to obtain diagnosis and treatment data label；Obtain the default corresponding diagnosis and treatment anomalous identification condition of antimicrobial guide；By nested round-robin algorithm, diagnosis and treatment data label is compared with the diagnosis and treatment anomalous identification condition, the recognition result of antimicrobial behavior is issued using comparison result as User Violations.The present invention is on the basis of obtaining antimicrobial guide corresponding diagnosis and treatment anomalous identification condition, diagnosis and treatment data label is obtained by the data identification model based on machine learning, nested round-robin algorithm recognition detection is recycled to go out the behavior that User Violations issue antimicrobial, the third-party institution is helped to carry out intelligent decision, it quickly solves medical institutions and issues the problem of antimicrobial influences patient health, wastes social medical resource in violation of rules and regulations.
Description
Technical field
The present invention relates to recognition methods, devices, clothes that computer field more particularly to a kind of violation issue antimicrobial behavior
Business device and computer readable storage medium.
Background technique
Antimicrobial refers to the drug with sterilization or bacteriostatic activity, including various antibiotic, sulfamido, imidazoles, nitro
The chemical synthetic drugs such as imidazoles and quinolones.Since the antibacterial range of every kind of antimicrobial is different, it is possible in treatment
While killing or inhibiting pathogenic bacteria, it would be beneficial to flora also eliminate or inhibit together, it is therefore desirable to it is additional to pay attention to and pay attention to
The use of antibacterials.But during actual medical diagnosis, have Partial Medical Institutions in order to seek interests to patient it is false or
Antimicrobial is mistakenly used, this is not only related to the health of patient, also results in the waste of social medical resource.
Summary of the invention
The main purpose of the present invention is to provide recognition methods, devices, server that antimicrobial behavior is issued in a kind of violation
And computer readable storage medium, it is intended to solve medical institutions and issue antimicrobial influence patient health in violation of rules and regulations, waste social doctor
The problem for the treatment of resource.
To achieve the above object, the present invention provides the recognition methods that antimicrobial behavior is issued in a kind of violation, the method packet
Include step:
The diagnosis and treatment data that user uploads are obtained, and are carried out the diagnosis and treatment data as the input of preset data identification model
Forecasting recognition, to obtain diagnosis and treatment data label；
Obtain the default corresponding diagnosis and treatment anomalous identification condition of antimicrobial guide；
By nested round-robin algorithm, the diagnosis and treatment data label is compared with the diagnosis and treatment anomalous identification condition, with
The recognition result of antimicrobial behavior is issued using comparison result as the User Violations.
Optionally, described to carry out Forecasting recognition for the diagnosis and treatment data as the input of preset data identification model, to obtain
Diagnosis and treatment data label the step of include:
According to the default noise entity dictionary in preset data identification model, the noise text in the diagnosis and treatment data is screened out
Data, to obtain standard diagnosis and treatment data；
The standard diagnosis and treatment data are segmented, obtain multiple diagnosis and treatment text participles, and each diagnosis and treatment text is segmented
Be converted to corresponding term vector；
The sequence of all term vectors is obtained, and according to the sequence of each term vector, by preset data identification model
Bidirectional circulating neural network RNN model encodes all term vectors, forms text matrix；
By the text matrix compression be diagnosis and treatment text vector after, pass through the prediction in the preset data identification model
Network is predicted, the corresponding diagnosis and treatment data label of the diagnosis and treatment text vector is obtained.
Optionally, described by nested round-robin algorithm, by the diagnosis and treatment data label and the diagnosis and treatment anomalous identification condition
The step of being compared, comparison result is issued the recognition result of antimicrobial behavior as the User Violations include:
The outer layer driving table of the nested round-robin algorithm is established according to the diagnosis and treatment data label, and different according to the diagnosis and treatment
The other condition of common sense establishes the internal layer of the nested round-robin algorithm by driving table；
Diagnosis and treatment data mark is successively selected according to the sequence of diagnosis and treatment data generation time from morning to night in the outer layer driving table
Label, and the diagnosis and treatment data label of selection is compared with the internal layer by each diagnosis and treatment anomalous identification condition in driving table；
When all diagnosis and treatment data labels and the internal layer compare completion by each diagnosis and treatment anomalous identification condition in driving table
Afterwards, the recognition result of antimicrobial behavior is issued using all comparison results as the User Violations.
Optionally, the diagnosis and treatment data label by selection and the internal layer are by each diagnosis and treatment anomalous identification in driving table
The step of condition is compared include:
Diagnosis and treatment anomalous identification condition is successively selected in driving table from the internal layer according to preset order；
According to the diagnosis and treatment data label of selection, determine whether the diagnosis and treatment anomalous identification condition currently selected is true；
When determining that the diagnosis and treatment anomalous identification condition currently selected is set up according to the diagnosis and treatment data label of selection, will currently select
Mapping relations data between the diagnosis and treatment data label selected and the diagnosis and treatment anomalous identification condition are saved to the nested round-robin algorithm
Comparison result table, and continue to be selected next diagnosis and treatment anomalous identification condition in driving table from the internal layer, until having selected institute
When stating internal layer by diagnosis and treatment anomalous identification condition in driving table, continue to select next diagnosis and treatment data label；
When determining that the diagnosis and treatment anomalous identification condition that currently selects is invalid according to the diagnosis and treatment data label of selection, continue from
The internal layer is selected next diagnosis and treatment anomalous identification condition in driving table, until having selected the internal layer by examining in driving table
When treating anomalous identification condition, continue to select next diagnosis and treatment data label.
Optionally, the diagnosis and treatment data label according to selection, whether the determining diagnosis and treatment anomalous identification condition currently selected
The step of establishment includes:
Within the unit time be that same patient issues for the user when the diagnosis and treatment anomalous identification condition selected includes anti-
There are when antimicrobial incompatibility in the prescription of bacterium medicine, according to the diagnosis and treatment data label of selection, the Subscriber Unit time is obtained
The interior prescription information including antimicrobial issued for same patient；
According to the prescription information including antimicrobial, the prescription that the user issues in the unit time for the patient is judged
In whether there is antimicrobial incompatibility；
When current there are determining when antimicrobial incompatibility in the prescription that user described in the unit time issues for the patient
The diagnosis and treatment anomalous identification condition of selection is set up.
Optionally, the diagnosis and treatment data label according to selection, whether the determining diagnosis and treatment anomalous identification condition currently selected
The step of establishment includes:
When the diagnosis and treatment anomalous identification condition selected has used correspondence special for special pathology patient and/or physiological status patient
When the antimicrobial that pathology and/or physiological status are forbidden to use, according to the diagnosis and treatment data label of selection, the diagnosis and treatment data label is obtained
The diagnosis report information of corresponding all patients；
According to the diagnosis report information, special pathology is filtered out from the corresponding all patients of the diagnosis and treatment data label and is suffered from
Person and/or physiological status patient, and according to the diagnosis and treatment data label of selection, obtaining the user is the special pathology patient
And/or the corresponding antibacterial effective prescription that physiological status patient issues；
According to antibacterial effective prescription, determine whether special pathology patient and/or physiological status patient have used corresponding specific disease
The antimicrobial that reason and/or physiological status are forbidden to use；
When special pathology patient and/or physiological status patient have used corresponding special pathology and/or physiological status to forbid making
When antimicrobial, determine that the diagnosis and treatment anomalous identification condition currently selected is set up.
Optionally, the diagnosis and treatment data label according to selection, whether the determining diagnosis and treatment anomalous identification condition currently selected
The step of establishment includes:
When the diagnosis and treatment anomalous identification condition selected is that patient has issued broad spectrum antibiotic, and the patient uses for the user
When the number of days of the broad spectrum antibiotic is more than preset time threshold, according to the diagnosis and treatment data label of selection, from the diagnosis and treatment data mark
It signs in corresponding all patients and filters out the patient for having used broad spectrum antibiotic；
Obtain the number of days that the patient filtered out uses broad spectrum antibiotic respectively；
When any patient filtered out is more than preset time threshold using the number of days of broad spectrum antibiotic, current selection is determined
Diagnosis and treatment anomalous identification condition set up.
To achieve the above object, the present invention also provides the identification devices that antimicrobial behavior is issued in a kind of violation, comprising:
Forecasting recognition module, for obtaining the diagnosis and treatment data of user's upload, and using the diagnosis and treatment data as preset data
The input of identification model carries out Forecasting recognition, to obtain diagnosis and treatment data label；
Module is obtained, for obtaining the corresponding diagnosis and treatment anomalous identification condition of default antimicrobial guide；
Comparison module, for passing through nested round-robin algorithm, by the diagnosis and treatment data label and the diagnosis and treatment anomalous identification item
Part is compared, and the recognition result of antimicrobial behavior is issued using comparison result as the User Violations.
To achieve the above object, the present invention also provides a kind of servers, comprising: communication module, memory, processor and deposits
The computer program that can be run on the memory and on the processor is stored up, the computer program is by the processor
The step of issuing the recognition methods of antimicrobial behavior in violation of rules and regulations as described above is realized when execution.
To achieve the above object, the present invention also provides a kind of computer readable storage medium, the computer-readable storages
It is stored with computer program on medium, is realized when the computer program is executed by processor and issues antibacterial in violation of rules and regulations as described above
The step of recognition methods of medicine behavior.
The diagnosis and treatment data that the present invention is uploaded by obtaining user, and using the diagnosis and treatment data as preset data identification model
Input carry out Forecasting recognition, to obtain diagnosis and treatment data label；Obtain the default corresponding diagnosis and treatment anomalous identification item of antimicrobial guide
Part；By nested round-robin algorithm, the diagnosis and treatment data label is compared with the diagnosis and treatment anomalous identification condition, will be compared
As a result the recognition result of antimicrobial behavior is issued as the User Violations.To obtain the corresponding diagnosis and treatment of antimicrobial guide
On the basis of anomalous identification condition, diagnosis and treatment data label is obtained by the data identification model based on machine learning, is recycled embedding
Set round-robin algorithm recognition detection has gone out the behavior that User Violations issue antimicrobial, helps the third-party institution to carry out intelligent decision, fastly
Speed solves medical institutions and issues the problem of antimicrobial influences patient health, wastes social medical resource in violation of rules and regulations.
Detailed description of the invention
Fig. 1 is the structural schematic diagram for the server that the embodiment of the present invention is related to；
Fig. 2 is the flow diagram for the recognition methods first embodiment that the present invention issues antimicrobial behavior in violation of rules and regulations；
Fig. 3 is the refinement process that the present invention issues step S10 in the recognition methods second embodiment of antimicrobial behavior in violation of rules and regulations
Schematic diagram；
Fig. 4 is the refinement process that the present invention issues step S30 in the recognition methods 3rd embodiment of antimicrobial behavior in violation of rules and regulations
Schematic diagram；
Fig. 5 is one the functional block diagram of identification device that the present invention issues antimicrobial behavior in violation of rules and regulations.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
Fig. 1 is please referred to, Fig. 1 is the hardware structural diagram of server provided by the present invention.The server can be
Cloud Server based on computer equipment is also possible to local server, including communication module 10, memory 20 and processor
30 equal components.In the server, the processor 30 is connect with the memory 20 and the communication module 10 respectively,
Computer program is stored on the memory 20, the computer program is executed by processor 30 simultaneously.
Communication module 10 can be connect by network with external communications equipment.Communication module 10 can receive external communication and set
The request that preparation goes out, can also send request, instruction and information to the external communications equipment.The external communications equipment can be with
It is other servers and/or the terminal that user uses.It should be noted that in the present solution, terminal to can be medical institutions special
Settlement terminal, computer, tablet computer and/or mobile phone, user then indicate medical institutions or medical institutions doctor or
Staff.
Memory 20 can be used for storing software program and various data.Memory 20 can mainly include storing program area
The storage data area and, wherein storing program area can application program needed for storage program area, at least one function (for example obtain
Take diagnosis and treatment anomalous identification condition) etc.；Storage data area may include database, and storage data area can store the use according to server
Data or information for being created etc..In addition, memory 20 may include high-speed random access memory, it can also include non-volatile
Property memory, a for example, at least disk memory, flush memory device or other volatile solid-state parts.In this programme
In, it may include default antimicrobial guide and corresponding diagnosis and treatment anomalous identification condition in memory 20, can also include being based on
The data identification model of machine learning.
Processor 30 is the control centre of server, utilizes each portion of various interfaces and the entire server of connection
Point, by running or execute the software program and/or module that are stored in memory 20, and calls and be stored in memory 20
Data, the various functions of execute server and processing data, to carry out integral monitoring to server.Processor 30 may include
One or more processing units；Optionally, processor 30 can integrate application processor and modem processor, wherein application
The main processing operation system of processor, user interface and application program etc., modem processor mainly handles wireless communication.It can
With understanding, above-mentioned modem processor can not also be integrated into processor 30.
Although Fig. 1 is not shown, above-mentioned server can also include that circuit control module guarantees for connecting to power supply
The normal work of other component.Above-mentioned server can also include display module, for extracting the data in memory 20, and show
It is shown as the front end display interface of server, the front end display interface can show whether user issues antimicrobial behavior in violation of rules and regulations
Recognition result.It will be understood by those skilled in the art that server architecture shown in Fig. 1 does not constitute the limit to server
It is fixed, it may include perhaps combining certain components or different component layouts than illustrating more or fewer components.
Based on above-mentioned hardware configuration, each embodiment of the method for the present invention is proposed.
Referring to fig. 2, in the first embodiment of the recognition methods of the invention for issuing antimicrobial behavior in violation of rules and regulations, comprising:
Step S10 obtains the diagnosis and treatment data that user uploads, and using the diagnosis and treatment data as preset data identification model
Input carries out Forecasting recognition, to obtain diagnosis and treatment data label；
The present embodiment is applied to the third-party institution detected to diagnosis and treatment data, and the third-party institution for example can be
Social medical insurance management board.
Above-mentioned user representative medical institutions execute upload operation.Such as it can be when patient goes to a doctor or checks in medical institutions
When completion is settled accounts, patient's diagnosis and treatment data with insured qualification are uploaded to by the settlement terminal of medical institutions by user
The server of the third-party institution.Above-mentioned diagnosis and treatment data are that there is the patient of insured qualification to carry out outpatient service in medical institutions and/or live
The medical data generated when institute, cost detail during may include the medical diagnosis on disease data of patient, treatment are issued inspection and are examined
The prescription that the doctor of the inventory and medical institutions of project and/or drug issues.
After obtaining diagnosis and treatment data, diagnosis and treatment data can be identified by the preset data identification model in memory and be matched
Into corresponding standardization field, to obtain diagnosis and treatment data label, the diagnosis and treatment data label can be a real number value, one to
Amount or a kind of class label etc., as long as the corresponding content of diagnosis and treatment data can be identified by the diagnosis and treatment data label.Its
Middle preset data identification model is constructed based on machine learning, and neural network, natural language processing and attention have been related to
The technologies such as mechanism.
Step S20 obtains the default corresponding diagnosis and treatment anomalous identification condition of antimicrobial guide；
Preset antimicrobial guide is Antibiotic use specification as defined in the third-party institution of various regions, including broad spectrum antibiotic refers to
South and narrow spectrum antibiotic guide.It should be noted that broad spectrum antibiotic refers to that the antimicrobial of has a broad antifungal spectrum, narrow spectrum antibiotic refer to
The antimicrobial of narrow antimicrobial spectrum.It, can be by the third-party institution before carrying out identification user with the presence or absence of Misuse antimicrobial
Staff presets diagnosis and treatment anomalous identification condition according to antimicrobial guide, or can also be by server by utilizing deep learning
Algorithm learns default antimicrobial guide, to obtain diagnosis and treatment anomalous identification condition.Optionally, the antimicrobial guide of each locale
Difference, therefore corresponding diagnosis and treatment anomalous identification condition can also be set according to the antimicrobial guide of different regions.
It should be noted that indicating that there are the rows of Misuse antimicrobial by user when diagnosis and treatment anomalous identification condition is set up
For data relevant to unlawful practice can then be found by diagnosis and treatment anomalous identification condition.For example, diagnosis and treatment anomalous identification condition is
Patient has been more than preset time threshold using the number of days of broad spectrum antibiotic, if the diagnosis and treatment anomalous identification condition is set up, the patient
Medical medical institutions are in the presence of the behavior for issuing antimicrobial in violation of rules and regulations, and it is corresponding that the corresponding data of behavior abnormal point can be patient
The antibacterial effective prescription issued in time.Alternatively, be also possible to diagnosis and treatment anomalous identification condition it is invalid when, indicate user exist in violation of rules and regulations
Using the behavior of antimicrobial, unified setting can be specifically carried out according to user.
Step S30 is carried out the diagnosis and treatment data label and the diagnosis and treatment anomalous identification condition by nested round-robin algorithm
It compares, the recognition result of antimicrobial behavior is issued using comparison result as the User Violations.
Data acquisition system needed for nested round-robin algorithm operation includes diagnosis and treatment data label set and diagnosis and treatment anomalous identification item
Part set.The spatial cache dimidiation of memory needed for running in algorithm operation, half store diagnosis and treatment data label,
The other half storage diagnosis and treatment anomalous identification condition.Furthermore processor can also be according to the number and diagnosis and treatment anomalous identification of diagnosis and treatment data label
Corresponding set is split into several logical blocks respectively by the item number of condition, and then logical block exists according to the set classified and stored
In corresponding spatial cache.Half spatial cache be can choose as circulation outer layer, the other half, then will circulation as circulation internal layer
The logical block of outer layer is compared with the logical block of circulation internal layer one by one, to obtain recognition result.It is kept away by nested round-robin algorithm
The building of index structure is exempted from, I/O efficiency can be improved.
The diagnosis and treatment data that the present embodiment is uploaded by obtaining user, and mould is identified using the diagnosis and treatment data as preset data
The input of type carries out Forecasting recognition, to obtain diagnosis and treatment data label；Obtain the default corresponding diagnosis and treatment anomalous identification of antimicrobial guide
Condition；By nested round-robin algorithm, the diagnosis and treatment data label is compared with the diagnosis and treatment anomalous identification condition, will be compared
The recognition result of antimicrobial behavior is issued as the User Violations to result.To which antimicrobial guide is corresponding to examine obtaining
On the basis for the treatment of anomalous identification condition, diagnosis and treatment data label is obtained by the data identification model based on machine learning, is recycled
Nested round-robin algorithm recognition detection has gone out the behavior that User Violations issue antimicrobial, helps the third-party institution to carry out intelligence and determines
Plan quickly solves medical institutions and issues the problem of antimicrobial influences patient health, wastes social medical resource in violation of rules and regulations.
Further, referring to Fig. 3, the first embodiment based on the recognition methods of the invention for issuing antimicrobial behavior in violation of rules and regulations is mentioned
The present invention issues the second embodiment of the recognition methods of antimicrobial behavior, in the present embodiment, the step S10 packet in violation of rules and regulations out
It includes:
Step S11 obtains the diagnosis and treatment data that user uploads；
Obtained in the present embodiment it is consistent in the realization process and first embodiment for the diagnosis and treatment data that user uploads, herein not into
Row repeats.
Step S12 is screened out in the diagnosis and treatment data according to the default noise entity dictionary in preset data identification model
Noise text data, to obtain standard diagnosis and treatment data；
Default noise entity dictionary be it is pre- first pass through training and obtain, wherein be stored with noise text data, for example including
Label symbol, annotation text and JS (JavaScript) code.It is examined according to noise entity dictionary iteration diagnosis and treatment data with eliminating
The noise text data in data is treated, i.e., the standard diagnosis and treatment data of exportable noiseless text data are realized to diagnosis and treatment data
Received text fields match.
Step S13 segments the standard diagnosis and treatment data, obtains multiple diagnosis and treatment texts participle, and by each diagnosis and treatment
Text participle is converted to corresponding term vector；
Standard diagnosis and treatment data are the diagnosis and treatment texts removed after making an uproar, can be by diagnosis and treatment text dividing at several diagnosis and treatment by participle
Text participle, all diagnosis and treatment text participles constitute diagnosis and treatment text participle set.The side that standard diagnosis and treatment data are segmented
Method can be executed with reference to existing participle tool and segmentation methods, herein without repeating.
After by standard diagnosis and treatment data participle, diagnosis and treatment text can also be segmented and carry out the classification of word variant.Word variant is sorted out
Refer to that all differences by diagnosis and treatment text participle change into standardized format.Such as can use preset standard semanteme dictionary,
By regular expression or manual compiling dictionary field, by the word being not present in standard semantic dictionary or word from all diagnosis and treatment
It is found out in text participle and is deleted or corrected.Word variant classification help is carried out more by segmenting to all diagnosis and treatment texts
The good semanteme for quickly identifying diagnosis and treatment data, realizes text standardization.
In the present embodiment, diagnosis and treatment text can be segmented into vectorization, obtain each diagnosis and treatment text segment corresponding word to
It measures (Word embedding).The term vector is the vector that diagnosis and treatment text participle is mapped to real number, can either indicate word sheet
Body, and semantic distance can be considered.
Step S14 obtains the sequence of all term vectors, and according to the sequence of each term vector, is identified by preset data
Bidirectional circulating neural network RNN model in model encodes all term vectors, forms text matrix；
The sequence of term vector can be analogous to and put in order, and be to utilize two-way RNN (Recurrent in this programme
Neural Network, Recognition with Recurrent Neural Network) model is reference with the sequence of sentence in diagnosis and treatment text, after splitting conversion
Term vector recompiles combination and forms text matrix.Every a line of this text matrix indicates each word institute's table within a context
The meaning reached, is equivalent to term vector.
Above-mentioned two-way RNN model is the neural network of processing sequence data, can establish power between neuron between layers
Connection can be according to script diagnosis and treatment after the forward direction for carrying out every a line term vector by two-way RNN model calculates and inversely calculates
The sequence order that text segments corresponding term vector splices and combines term vector, to obtain complete text matrix, or cry sentence to
Moment matrix.
Step S15, by the text matrix compression be diagnosis and treatment text vector after, pass through the preset data identification model
In prediction network predicted, obtain the corresponding diagnosis and treatment data label of the diagnosis and treatment text vector.
Diagnosis and treatment text vector then can be sent into prediction network first by text matrix compression at a diagnosis and treatment text vector
In predicted, thus study obtain diagnosis and treatment data label.It is examined it should be noted that diagnosis and treatment data label can be model understanding
The volume of data obtained after text is treated, these data can be divided into each diagnosis and treatment data mark in the form of data generation time
Label, can be combined with the information such as user and sufferer and are ranked up.
Wherein, prediction network can be using standard Architecture of Feed-forward Neural Network and Recursive Neural Network Structure etc..With standard
Feedforward neural network is illustrated the simple forecast process of this programme, can be standardization field being input to LTSM (Long
Short-Term Memory, length memory network) in, to export continuous label characteristics, these continuous label characteristics pass through
Feedforward neural network operation；Diagnosis and treatment text vector is also sent to feedforward neural network simultaneously, with extracted vector feature, then will
Vector characteristics and label characteristics cascade up to form vector-label characteristics；Finally vector-label binding characteristic combination is used for pre-
Survey the diagnosis and treatment data label of output.
This programme passes through nerve net according to the sequence of term vector after diagnosis and treatment data segment dyad by noise remove
Network is encoded and is predicted to obtain diagnosis and treatment data label, how is given by preset data identification model progress Forecasting recognition
The process of diagnosis and treatment data label is obtained, is also helped the prescription provided including doctor, the inspection inspection item issued and/or drug
The contents such as inventory diagnosis and treatment Data Matching into corresponding standardization field, reflect word in diagnosis and treatment data in the text
Meaning.
Further, referring to fig. 4, the first embodiment based on the recognition methods of the invention for issuing antimicrobial behavior in violation of rules and regulations mentions
The present invention issues the 3rd embodiment of the recognition methods of antimicrobial behavior, in the present embodiment, the step S30 packet in violation of rules and regulations out
It includes:
Step S31 establishes the outer layer driving table of the nested round-robin algorithm according to the diagnosis and treatment data label, and according to institute
It states diagnosis and treatment anomalous identification condition and establishes the internal layer of the nested round-robin algorithm by driving table；
The outer layer that the standardization field that diagnosis and treatment data label is representative is used as nested round-robin algorithm is driven table by the present embodiment,
To drive internal layer by driving table.It wherein include the diagnosis and treatment data label that there is data generation time to mark in outer layer driving table, it is interior
Layer is included all diagnosis and treatment abnormity diagnosis conditions in driving table.
Step S32 successively selects to examine according to the sequence of diagnosis and treatment data generation time from morning to night in the outer layer driving table
Treat data label, and by the diagnosis and treatment data label of selection and the internal layer by each diagnosis and treatment anomalous identification condition in driving table into
Row compares；
Server is referred to the data generation time label that every diagnosis and treatment data label carries, when with the generation of diagnosis and treatment data
Between sequence from morning to night be arranged successively selection, or selection diagnosis and treatment data label, then by the diagnosis and treatment data label of selection with it is interior
Layer is compared one by one by the diagnosis and treatment anomalous identification condition in driving table.It is alternatively possible to suitable according to the arrangement of internal layer driving table
Sequence is successively compared.When being matched when the diagnosis and treatment data label for finding with currently being selected in driving table, it is believed that compare knot
Fruit is that user produces the behavior of Misuse antimicrobial, then that the diagnosis and treatment data label currently selected and matched diagnosis and treatment is different
The other condition of common sense connects, and is then generated and is mapped according to the diagnosis and treatment data label and matched anomalous identification condition that currently select
Relation data is stored in the comparison result table of nested round-robin algorithm.
Optionally, the process that above-mentioned steps S32 is compared may include:
Step S321 is successively selected diagnosis and treatment anomalous identification condition from the internal layer according to preset order in driving table；
Wherein preset order can be at random, can also be from internal layer by the gauge outfit of driving table or a certain diagnosis and treatment anomalous identification
Condition starts to carry out sequential selection.
Step S322, according to the diagnosis and treatment data label of selection, determine the diagnosis and treatment anomalous identification condition that currently selects whether at
It is vertical；If so, thening follow the steps S323；If it is not, thening follow the steps S324；
Step S323, by the mapping relations data between the diagnosis and treatment data label currently selected and the diagnosis and treatment anomalous identification condition
It saves to the comparison result table of the nested round-robin algorithm, and continues to be selected next diagnosis and treatment different in driving table from the internal layer
The other condition of common sense, until continuing to select next examine when having selected the internal layer by diagnosis and treatment anomalous identification condition in driving table
Treat data label；
The present embodiment is to indicate that user issues antimicrobial in the presence of violation with the diagnosis and treatment anomalous identification condition establishment currently selected
Behavior on the basis of execute.If determining that diagnosis and treatment anomalous identification condition is set up according to the diagnosis and treatment data label of selection, save
The mapping relations data in anomalous identification conditions and data source (the diagnosis and treatment data label currently selected) are to comparison result table.
Step S324 continues to be selected next diagnosis and treatment anomalous identification condition in driving table from the internal layer, until selection
When the complete internal layer is by diagnosis and treatment anomalous identification condition in driving table, continue to select next diagnosis and treatment data label.
It should be noted that storage has users and opens in violation of rules and regulations in comparison result table when diagnosis and treatment data label has selected
Mapping relations data when having antimicrobial behavior between all anomalous identification condition and corresponding data source, thus can directly by
Data in comparison result table are as comparison result, i.e. the User Violations recognition result of issuing antimicrobial behavior.Pass through ectonexine
Circulation executes comparison, will compare number and minimizes, improves delivery efficiency, also avoid the foundation of index structure, very convenient.
Step S33, when all diagnosis and treatment data labels and the internal layer are by each diagnosis and treatment anomalous identification condition in driving table
After the completion of comparison, the recognition result of antimicrobial behavior is issued using all comparison results as the User Violations.
After the completion of diagnosis and treatment data label selection all in driving table, the data of comparison result table can be exported into conduct
User Violations issue the recognition result of antimicrobial behavior.User Violations, which are exported, by nested round-robin algorithm issues antimicrobial behavior
Related data can quickly help the third-party institution including medical insurance management board to find out the unlawful practice of user, requirement is facilitated to use
Family carries out reparation rectification, and to prevent user from continuing in violation of rules and regulations, issuing antimicrobial endangers user health, also strengthens social medical institutions
Management and social medical resource reasonable utilization.
Optionally, in other embodiments, the above-mentioned diagnosis and treatment data label according to selection determines that the diagnosis and treatment currently selected are different
The whether true process of the other condition of common sense can carry out selection setting according to actual needs.For example, it may be following determination process
At least one of.
Within the unit time be that same patient issues for the user when the diagnosis and treatment anomalous identification condition selected includes anti-
There are when antimicrobial incompatibility in the prescription of bacterium medicine, according to the diagnosis and treatment data label of selection, the Subscriber Unit time is obtained
The interior prescription information including antimicrobial issued for same patient；According to the prescription information including antimicrobial, unit is judged
With the presence or absence of antimicrobial incompatibility in the prescription that the user issues in time for the patient；As user described in the unit time
There are when antimicrobial incompatibility, determine the diagnosis and treatment anomalous identification condition establishment currently selected in the prescription issued for the patient.
Incompatibility refers to drug compatibility in vitro, and physical or chemically interaction, which directly occurs, will affect medicine
Toxic reaction occurs for object curative effect, and incompatibility is generally divided into physical and chemically two classes.Such as penicillins with
Energy mistura, sodium bicarbonate, aminophylline, heparin, glutamic acid and arginine compatibility will appear discoloration, muddiness and antibiotic and lose
Situation living.
In the deterministic process for carrying out incompatibility, it can check in the unit time, such as one day or half a day, user is same
The prescription information including antimicrobial that one patient issues.If its other medicine in the antimicrobial and prescription information in prescription information is deposited
In incompatibility, then it is assumed that diagnosis and treatment anomalous identification condition is set up, and user issues antimicrobial behavior in the presence of violation in this aspect.
When the diagnosis and treatment anomalous identification condition selected has used correspondence special for special pathology patient and/or physiological status patient
When the antimicrobial that pathology and/or physiological status are forbidden to use, according to the diagnosis and treatment data label of selection, the diagnosis and treatment data label is obtained
The diagnosis report information of corresponding all patients；It is corresponding all from the diagnosis and treatment data label according to the diagnosis report information
Special pathology patient and/or physiological status patient are filtered out in patient, and according to the diagnosis and treatment data label of selection, obtain the use
Family is the corresponding antibacterial effective prescription that the special pathology patient and/or physiological status patient issue；According to antibacterial effective prescription, really
Whether fixed special pathology patient and/or physiological status patient have used corresponding special pathology and/or physiological status to be forbidden to use
Antimicrobial；When special pathology patient and/or physiological status patient have used corresponding special pathology and/or physiological status to be forbidden to use
Antimicrobial when, determine that the diagnosis and treatment anomalous identification condition that currently selects is set up.
Special pathology for example may include renal hypofunction, hypofunction of liver, and can check in antibacterial effective prescription detail is
It is no to have issued the antimicrobial that impacted to liver/renal function, or whether reduce making for the antimicrobial impacted to it
Dosage, if not, then it is assumed that Misuse antimicrobial.Physiological status patient for example including newborn, pregnant woman and nursing period patient,
It may determine whether the drug that corresponding physiological status particular patients ' disabling has been issued in Antibiotic use detail, if it is,
Determine Misuse antimicrobial.
When the diagnosis and treatment anomalous identification condition selected is that patient has issued broad spectrum antibiotic, and the patient uses for the user
When the number of days of the broad spectrum antibiotic is more than preset time threshold, according to the diagnosis and treatment data label of selection, from the diagnosis and treatment data mark
It signs in corresponding all patients and filters out the patient for having used broad spectrum antibiotic；It is anti-using wide spectrum respectively to obtain the patient filtered out
The number of days of bacterium medicine；When any patient filtered out is more than preset time threshold using the number of days of broad spectrum antibiotic, determine current
The diagnosis and treatment anomalous identification condition of selection is set up.
Due to the has a broad antifungal spectrum of broad spectrum antibiotic, it is easy also to kill patient's body profitable strain while killing harmful bacteria
Evil, being used for a long time is even more the health for seriously affecting patient.Therefore the use number of days using the patient of broad spectrum antibiotic can be monitored,
If the number of days using broad spectrum antibiotic has been more than preset time threshold, confirm that diagnosis and treatment anomalous identification condition is set up, Yong Hucun
In the behavior of Misuse antimicrobial.Preset time threshold for example can be 38 days.
Optionally, medical diagnosis on disease data, the prescription letter of patient can also according to the diagnosis and treatment data label currently selected, be obtained
It ceases, the broad spectrum antibiotic inventory and narrow spectrum antibiotic inventory of patient assessment medical institutions；And whether determine patient's illnesses
Narrow spectrum antibiotic is able to use to be treated, if can with but prescription used in be broad spectrum antibiotic, can further inquire
Medical medical institutions' drug inventory can exclude user in the presence of separated if not treating the narrow spectrum antibiotic of patient's illnesses
The case where rule are using antimicrobial situation.If there is the narrow spectrum antibiotic for the treatment of patient's illnesses in inventory, then it is assumed that user disobeys
Rule use the behavior of antimicrobial.
It can also determine whether patient belongs to critical sufferer according to the diagnosis and treatment data label currently selected；When patient is danger
Grave illness is suffered from, and can check that patient finds the front and back of pathogen and drug sensitivity tests, whether have treatment in the diagnostic report of the patient
Bad equal diagnosis word is imitated, if so, then further confirming that whether antimicrobial has adjustment, if do not adjusted, then it is assumed that user
There is the behavior for issuing antimicrobial in violation of rules and regulations.
Can also be according to the diagnosis and treatment data label of selection, the inspection Samples detection data and antibacterials for obtaining patient make
With detail, the drug sensitive test result (including sensitive, intermediary and drug resistance) of patient is determined.When the usage amount of Antimicrobial Drugs in Patients detail
It has been more than odd-numbered day/mono- month research on maximum utilized quantity that patient's drug sensitive test result is limited, determining user, there are Misuse antimicrobials
Behavior.
By the way that a variety of diagnosis and treatment anomalous identification conditions of different dimensions are arranged, help the third-party institution can many-sided detection user
It is abnormal, fully understand the case where user uses antimicrobial, behavior of quickly therefrom finding the problem.
Referring to Fig. 5, the present invention also provides the identification device that antimicrobial behavior is issued in a kind of violation, described device can be this
Ground server or Cloud Server based on computer equipment, comprising:
Forecasting recognition module 10, for obtaining the diagnosis and treatment data of user's upload, and using the diagnosis and treatment data as present count
Forecasting recognition is carried out according to the input of identification model, to obtain diagnosis and treatment data label；
Module 20 is obtained, for obtaining the corresponding diagnosis and treatment anomalous identification condition of default antimicrobial guide；
Comparison module 30, for passing through nested round-robin algorithm, by the diagnosis and treatment data label and the diagnosis and treatment anomalous identification
Condition is compared, and the recognition result of antimicrobial behavior is issued using comparison result as the User Violations.
Optionally, in another embodiment, the Forecasting recognition module includes:
Unit is screened out, for screening out the diagnosis and treatment number according to the default noise entity dictionary in preset data identification model
Noise text data in, to obtain standard diagnosis and treatment data；
Converting unit is segmented, for segmenting to the standard diagnosis and treatment data, obtains multiple diagnosis and treatment text participles, and will
Each diagnosis and treatment text participle is converted to corresponding term vector；
Coding unit passes through preset data for obtaining the sequence of all term vectors, and according to the sequence of each term vector
Bidirectional circulating neural network RNN model in identification model encodes all term vectors, forms text matrix；
Predicting unit, for by the text matrix compression be diagnosis and treatment text vector after, known by the preset data
Prediction network in other model is predicted, the corresponding diagnosis and treatment data label of the diagnosis and treatment text vector is obtained.
Optionally, in another embodiment, the comparison module includes:
Unit is established, for establishing the outer layer driving table of the nested round-robin algorithm according to the diagnosis and treatment data label, and
The internal layer of the nested round-robin algorithm is established by driving table according to the diagnosis and treatment anomalous identification condition；
Select comparing unit, for according to the outer layer driving table in the sequence of diagnosis and treatment data generation time from morning to night according to
Secondary selection diagnosis and treatment data label, and the diagnosis and treatment data label of selection and the internal layer are known extremely by each diagnosis and treatment in driving table
Other condition is compared；
Execution unit, for working as all diagnosis and treatment data labels and the internal layer by each diagnosis and treatment anomalous identification in driving table
After the completion of condition compares, the recognition result of antimicrobial behavior is issued using all comparison results as the User Violations.
Optionally, in another embodiment, the selection comparing unit includes:
Subelement is selected, for successively being selected diagnosis and treatment anomalous identification item in driving table from the internal layer according to preset order
Part；
It determines subelement, for the diagnosis and treatment data label according to selection, determines the diagnosis and treatment anomalous identification condition currently selected
It is whether true；
Saving subunit, for when according to the determining diagnosis and treatment anomalous identification condition currently selected of the diagnosis and treatment data label of selection
When establishment, the mapping relations data between the diagnosis and treatment data label currently selected and the diagnosis and treatment anomalous identification condition are saved to described
The comparison result table of nested round-robin algorithm, and trigger selection subelement continuation selected in driving table from the internal layer it is next
A diagnosis and treatment anomalous identification condition, until when select the internal layer by diagnosis and treatment anomalous identification condition in driving table, triggering continuation
Select next diagnosis and treatment data label；
Subelement is triggered, for when according to the determining diagnosis and treatment anomalous identification condition currently selected of the diagnosis and treatment data label of selection
When invalid, trigger the selection subelement and continue to be selected next diagnosis and treatment anomalous identification item in driving table from the internal layer
Part, until continuing to select next diagnosis and treatment data mark when having selected the internal layer by diagnosis and treatment anomalous identification condition in driving table
Label.
Optionally, in another embodiment, the determining subelement be also used to when the diagnosis and treatment anomalous identification condition that selects for
The user is in the prescription including antimicrobial that same patient issues within the unit time there are when antimicrobial incompatibility, root
According to the diagnosis and treatment data label of selection, the prescription letter including antimicrobial issued in the Subscriber Unit time for same patient is obtained
Breath；According to the prescription information including antimicrobial, judge be in the user issues in the unit time for the patient prescription
It is no that there are antimicrobial incompatibility；When there are antimicrobial compatibility taboos in the prescription that user described in the unit time issues for the patient
When avoiding, determine that the diagnosis and treatment anomalous identification condition currently selected is set up.
Optionally, in another embodiment, the determining subelement be also used to when the diagnosis and treatment anomalous identification condition that selects for
The antimicrobial that special pathology patient and/or physiological status patient have used corresponding special pathology and/or physiological status to be forbidden to use
When, according to the diagnosis and treatment data label of selection, obtain the diagnosis report information of the corresponding all patients of the diagnosis and treatment data label；According to
The diagnosis report information filters out special pathology patient and/or physiology from the corresponding all patients of the diagnosis and treatment data label
Situation patient, and according to the diagnosis and treatment data label of selection, obtaining the user is the special pathology patient and/or physiological status
The corresponding antibacterial effective prescription that patient issues；According to antibacterial effective prescription, determines special pathology patient and/or physiological status patient is
The no antimicrobial for having used corresponding special pathology and/or physiological status to be forbidden to use；When special pathology patient and/or physiological status
When the antimicrobial that patient has used corresponding special pathology and/or physiological status to be forbidden to use, determine that the diagnosis and treatment currently selected are abnormal
Identification condition is set up.
Optionally, in another embodiment, the determining subelement be also used to when the diagnosis and treatment anomalous identification condition that selects for
The user is that patient has issued broad spectrum antibiotic, and the patient is more than preset time threshold using the number of days of the broad spectrum antibiotic
When value, according to the diagnosis and treatment data label of selection, is filtered out from the corresponding all patients of the diagnosis and treatment data label and used wide spectrum
The patient of antimicrobial；Obtain the number of days that the patient filtered out uses broad spectrum antibiotic respectively；When any patient filtered out uses
When the number of days of broad spectrum antibiotic is more than preset time threshold, determine that the diagnosis and treatment anomalous identification condition currently selected is set up.
The present invention also proposes a kind of computer readable storage medium, is stored thereon with computer program.The computer can
Reading storage medium can be the memory 20 in the server of Fig. 1, be also possible to as ROM (Read-Only Memory, it is read-only to deposit
Reservoir)/RAM (Random Access Memory, random access memory), magnetic disk, at least one of CD, the calculating
Machine readable storage medium storing program for executing include some instructions use so that one with processor terminal device (can be mobile phone, computer,
Server or the network equipment etc.) execute method described in each embodiment of the present invention.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that the process, method, article or the server-side that include a series of elements not only include those elements,
It but also including other elements that are not explicitly listed, or further include for this process, method, article or server-side institute
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that wrapping
Include in process, method, article or the server-side of the element that there is also other identical elements.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
1. the recognition methods that antimicrobial behavior is issued in a kind of violation, which is characterized in that comprising steps of
The diagnosis and treatment data that user uploads are obtained, and are predicted using the diagnosis and treatment data as the input of preset data identification model
Identification, to obtain diagnosis and treatment data label；
Obtain the default corresponding diagnosis and treatment anomalous identification condition of antimicrobial guide；
By nested round-robin algorithm, the diagnosis and treatment data label is compared with the diagnosis and treatment anomalous identification condition, will be compared
The recognition result of antimicrobial behavior is issued as the User Violations to result.
2. the recognition methods that antimicrobial behavior is issued in violation according to claim 1, which is characterized in that described to be examined described
The input that data are treated as preset data identification model carries out Forecasting recognition, includes: the step of diagnosis and treatment data label to obtain
According to the default noise entity dictionary in preset data identification model, the noise textual data in the diagnosis and treatment data is screened out
According to obtain standard diagnosis and treatment data；
The standard diagnosis and treatment data are segmented, obtain multiple diagnosis and treatment text participles, and each diagnosis and treatment text is segmented and is converted
For corresponding term vector；
The sequence of all term vectors is obtained, and according to the sequence of each term vector, by two-way in preset data identification model
Recognition with Recurrent Neural Network RNN model encodes all term vectors, forms text matrix；
By the text matrix compression be diagnosis and treatment text vector after, pass through the prediction network in the preset data identification model
It is predicted, obtains the corresponding diagnosis and treatment data label of the diagnosis and treatment text vector.
3. the recognition methods that antimicrobial behavior is issued in violation according to claim 1, which is characterized in that described to pass through nesting
Round-robin algorithm the diagnosis and treatment data label is compared with the diagnosis and treatment anomalous identification condition, using comparison result as institute
Stating the step of User Violations issue the recognition result of antimicrobial behavior includes:
The outer layer driving table of the nested round-robin algorithm is established according to the diagnosis and treatment data label, and is known extremely according to the diagnosis and treatment
Other condition establishes the internal layer of the nested round-robin algorithm by driving table；
Diagnosis and treatment data label is successively selected according to the sequence of diagnosis and treatment data generation time from morning to night in the outer layer driving table, and
The diagnosis and treatment data label of selection is compared with the internal layer by each diagnosis and treatment anomalous identification condition in driving table；
It, will after the completion of all diagnosis and treatment data labels and the internal layer are compared by each diagnosis and treatment anomalous identification condition in driving table
All comparison results issue the recognition result of antimicrobial behavior as the User Violations.
4. the recognition methods that antimicrobial behavior is issued in violation according to claim 3, which is characterized in that described by selection
The step of diagnosis and treatment data label is compared with the internal layer by each diagnosis and treatment anomalous identification condition in driving table include:
Diagnosis and treatment anomalous identification condition is successively selected in driving table from the internal layer according to preset order；
According to the diagnosis and treatment data label of selection, determine whether the diagnosis and treatment anomalous identification condition currently selected is true；
When determining that the diagnosis and treatment anomalous identification condition currently selected is set up according to the diagnosis and treatment data label of selection, by what is currently selected
Mapping relations data between diagnosis and treatment data label and the diagnosis and treatment anomalous identification condition are saved to the comparison of the nested round-robin algorithm
As a result table, and continue to be selected next diagnosis and treatment anomalous identification condition in driving table from the internal layer, until having selected in described
When layer is by diagnosis and treatment anomalous identification condition in driving table, continue to select next diagnosis and treatment data label；
When determining that the diagnosis and treatment anomalous identification condition currently selected is invalid according to the diagnosis and treatment data label of selection, continue from described
Internal layer is selected next diagnosis and treatment anomalous identification condition in driving table, until having selected the internal layer different by the diagnosis and treatment in driving table
When the other condition of common sense, continue to select next diagnosis and treatment data label.
5. the recognition methods that antimicrobial behavior is issued in violation according to claim 4, which is characterized in that described according to selection
Diagnosis and treatment data label, determine that the step of whether the diagnosis and treatment anomalous identification condition that currently selects true includes:
When the diagnosis and treatment anomalous identification condition selected for the user be within the unit time same patient issue include antimicrobial
Prescription in there are when antimicrobial incompatibility, according to the diagnosis and treatment data label of selection, obtain in the Subscriber Unit time and be
The prescription information including antimicrobial that same patient issues；
According to the prescription information including antimicrobial, judge be in the user issues in the unit time for the patient prescription
It is no that there are antimicrobial incompatibility；
When there are when antimicrobial incompatibility, determine current selection in the prescription that user described in the unit time issues for the patient
Diagnosis and treatment anomalous identification condition set up.
6. the recognition methods that antimicrobial behavior is issued in violation according to claim 4, which is characterized in that described according to selection
Diagnosis and treatment data label, determine that the step of whether the diagnosis and treatment anomalous identification condition that currently selects true includes:
When the diagnosis and treatment anomalous identification condition selected has used corresponding special pathology for special pathology patient and/or physiological status patient
And/or physiological status be forbidden to use antimicrobial when, according to the diagnosis and treatment data label of selection, it is corresponding to obtain the diagnosis and treatment data label
All patients diagnosis report information；
According to the diagnosis report information, special pathology patient is filtered out from the corresponding all patients of the diagnosis and treatment data label
And/or physiological status patient, and according to the diagnosis and treatment data label of selection, obtain the user be the special pathology patient and/
Or the corresponding antibacterial effective prescription that physiological status patient issues；
According to antibacterial effective prescription, determine special pathology patient and/or physiological status patient whether used corresponding special pathology and/
Or the antimicrobial that physiological status is forbidden to use；
When special pathology patient and/or physiological status patient have used corresponding special pathology and/or physiological status to be forbidden to use
When antimicrobial, determine that the diagnosis and treatment anomalous identification condition currently selected is set up.
7. the recognition methods that antimicrobial behavior is issued in violation according to claim 4, which is characterized in that described according to selection
Diagnosis and treatment data label, determine that the step of whether the diagnosis and treatment anomalous identification condition that currently selects true includes:
When the diagnosis and treatment anomalous identification condition selected for the user is that patient has issued broad spectrum antibiotic, and described in patient's use
When the number of days of broad spectrum antibiotic is more than preset time threshold, according to the diagnosis and treatment data label of selection, from the diagnosis and treatment data label pair
The patient for having used broad spectrum antibiotic is filtered out in all patients answered；
Obtain the number of days that the patient filtered out uses broad spectrum antibiotic respectively；
When any patient filtered out is more than preset time threshold using the number of days of broad spectrum antibiotic, determine that is currently selected examines
Anomalous identification condition is treated to set up.
8. the identification device that antimicrobial behavior is issued in a kind of violation characterized by comprising
Forecasting recognition module is identified for obtaining the diagnosis and treatment data of user's upload, and using the diagnosis and treatment data as preset data
The input of model carries out Forecasting recognition, to obtain diagnosis and treatment data label；
Module is obtained, for obtaining the corresponding diagnosis and treatment anomalous identification condition of default antimicrobial guide；
Comparison module, for by nested round-robin algorithm, by the diagnosis and treatment data label and the diagnosis and treatment anomalous identification condition into
Row compares, and the recognition result of antimicrobial behavior is issued using comparison result as the User Violations.
9. a kind of server, which is characterized in that the server includes: communication module, memory, processor and is stored in described
It is real when the computer program is executed by the processor on memory and the computer program that can run on the processor
Now the step of recognition methods of antimicrobial behavior, is issued in the violation as described in any one of claims 1 to 7.
10. a kind of computer readable storage medium, which is characterized in that be stored with computer on the computer readable storage medium
Program realizes that antibacterial is issued in the violation as described in any one of claims 1 to 7 when the computer program is executed by processor
The step of recognition methods of medicine behavior.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811526944.7A CN109637619A (en) | 2018-12-13 | 2018-12-13 | Recognition methods, device, server and the medium of antimicrobial behavior are issued in violation of rules and regulations |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201811526944.7A CN109637619A (en) | 2018-12-13 | 2018-12-13 | Recognition methods, device, server and the medium of antimicrobial behavior are issued in violation of rules and regulations |
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| CN109637619A true CN109637619A (en) | 2019-04-16 |
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| WO2024098513A1 (en) * | 2022-11-07 | 2024-05-16 | 上海维小美网络科技有限公司 | Diagnosis and treatment record compliance management method for oral cavity management system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104615904A (en) * | 2015-02-28 | 2015-05-13 | 领智控股有限公司 | Antibacterial medicament clinical application decision support system and constructing method thereof |
| CN107133456A (en) * | 2017-04-24 | 2017-09-05 | 中国人民解放军第七五医院 | Intellectual treatment drug surveillance whole process control system and method based on HIS |
| CN108648792A (en) * | 2018-05-04 | 2018-10-12 | 河北省人民医院 | Medication information management system, method and terminal device |
| CN108648810A (en) * | 2018-05-11 | 2018-10-12 | 平安医疗健康管理股份有限公司 | Data processing method, device and the computer readable storage medium of medicine audit |
-
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- 2018-12-13 CN CN201811526944.7A patent/CN109637619A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104615904A (en) * | 2015-02-28 | 2015-05-13 | 领智控股有限公司 | Antibacterial medicament clinical application decision support system and constructing method thereof |
| CN107133456A (en) * | 2017-04-24 | 2017-09-05 | 中国人民解放军第七五医院 | Intellectual treatment drug surveillance whole process control system and method based on HIS |
| CN108648792A (en) * | 2018-05-04 | 2018-10-12 | 河北省人民医院 | Medication information management system, method and terminal device |
| CN108648810A (en) * | 2018-05-11 | 2018-10-12 | 平安医疗健康管理股份有限公司 | Data processing method, device and the computer readable storage medium of medicine audit |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024098513A1 (en) * | 2022-11-07 | 2024-05-16 | 上海维小美网络科技有限公司 | Diagnosis and treatment record compliance management method for oral cavity management system |
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