CN112071388A - Intelligent medicine dispensing and preparing method based on deep learning - Google Patents
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Abstract
The invention discloses an intelligent medicine dispensing and preparing method based on deep learning. The method comprises the steps of carrying out data preprocessing on patient condition data collected by an intelligent device end, transmitting the processed data to a prepared medicine control server, carrying out training and learning on the prepared medicine control server by adopting an intelligent medicine dispensing and pharmaceutical algorithm based on deep learning, carrying out accurate analysis on subsequent unknown data by adopting a trained network model, outputting a ratio table of the medicine amount required by different patients, periodically receiving the preprocessed patient data by the prepared medicine control server, and tracking and adjusting the ratio table of the medicine amount required by the patients in time, thereby realizing differentiated and customized medicine configuration services for the patients, simultaneously improving the audience range of medicine preparation for manufacturers and realizing intelligent differentiated management of the prepared medicines.
Description
Technical Field
The invention relates to the field of medical intelligent dispensing and pharmacy, in particular to a method for realizing intelligent control and differential management of dispensed medicine components in the process of medicine production and dispensing by combining an artificial intelligence technology.
Background
At present, pharmaceutical manufacturers in China generally pre-set a dosage ratio table of certain medicines, extract various components of the medicines according to the dosage ratio table, and process the medicines on a machine to prepare the medicines. The method for customizing the dose ratio table in advance by a professional pharmacist is usually configured according to professional knowledge, and lacks of differential and personalized configuration, so that the professional pharmacist inspects the etiology, pathology and prescription of a patient on a large scale on the spot, and a plurality of challenges exist in reality; if the illness state of the patient is tracked for a long time, great manpower, material resources and financial resources are consumed, and in addition, the patient condition of the patient is not common in reality because a pharmacist directly communicates with the patient to know the probability of the illness state due to different times and times. According to the principle that market is led by demands, manufacturers prepare medicines in a differentiated mode by taking user demands as guidance, and intelligent medicine preparation and customized service are achieved.
With the rapid development of technologies such as artificial intelligence, internet of things and the like, the application of some intelligent configurations is also widely popularized. By combining intelligent equipment and an artificial intelligence technology, the medicine amount control of the dispensing and pharmacy area is realized through 'private customization' and differentiated management, and the method has important market value for future intelligent, differentiated and dynamic services in medical application.
Disclosure of Invention
Aiming at the defects of intellectualization, differentiation and dynamicity such as lack of real-time tracking with patients, incapability of dynamically acquiring user requirements, need of mechanically pre-formulating a medicine proportioning table and the like when the domestic pharmaceutical manufacturers prepare medicines, the intelligent medicine dispensing and preparing method based on deep learning is provided. The method comprises the steps of preprocessing patient condition data collected by an intelligent device end, training and learning the processed data by adopting an intelligent medicine dispensing and preparing algorithm based on deep learning, analyzing the subsequent unknown data accurately by a trained network model, and outputting a ratio table of the medicine amount required by different patients, so that differentiated and customized medicine configuration services are realized for the patients, meanwhile, the audience range for preparing medicines is expanded for manufacturers, and intelligent differentiated management of the prepared medicines is realized.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: an intelligent medicine dispensing and preparing method based on deep learning. The method comprises the following steps:
step A1: the intelligent equipment collects the disease condition data of a current patient, preprocesses the data and transmits the data to the control server for preparing the medicine; the data preprocessing is to file the disease data and arrange the disease data into structured data; the structured data is classified, recorded and filed according to the name, sex, age, medical history, physical examination data, etiology, treatment data, drug use condition, duration and current situation of the patient;
step A2: the prepared medicine control server adopts an intelligent medicine preparation algorithm based on deep learning to output a ratio table of the medicine amount required by different patients;
step A3: the prepared medicine control server regularly collects the preprocessed disease condition data of the patient in real time through intelligent equipment, changes the dosage ratio of the medicine required by the patient by adopting the intelligent medicine dispensing and preparing algorithm based on deep learning, and transmits the changed dosage ratio table to the patient or a doctor.
Further, the intelligent medicine dispensing and preparing algorithm based on deep learning comprises the following steps:
step B1: respectively initializing parameters w and theta of an Actor network and a Critic network at random;
step B2: judging whether the current time T is smaller than a preset iteration period T, if so, turning to a step B3, otherwise, turning to a step B10;
step B3: initializing a state set S, and obtaining a characteristic vector phi (S) of the state set S after data preprocessing;
step B4: the Actor network uses phi (S) as data input, outputs a corresponding behavior set A at the current moment, and obtains a state S' at the next moment and an immediate reward R of the Critic network based on the behavior set A;
step B5: the Critic network uses phi (S), phi (S ') as data inputs, respectively, to get the total accumulated award V (S) for the current state and the total accumulated award V (S ') for the subsequent state of the output, where V (S) and V (S ') are calculated according to equations (1) and (2), respectively:
V(S′)＝R(t)+βV[S′，argmaxA′V(S′，A′|w|θ)] (2)
wherein R (t) represents an instant prize at time t, β represents a discount factor, A' represents a behavior at the next time, StAnd AtRespectively representing the state and the behavior at the moment t;
step B6: the criticic network randomly draws D samples from the playback buffer pool and calculates an error function according to equation (3):
＝R(t)+αV(S′)-V(S) (3)
wherein α represents an update rate, which ranges from [0, 1 ];
step B7: the criticic network is used as a loss function L (w) according to the formula (4), the gradient of the loss function is calculated according to the formula (5), and the criticic network parameter w is updated according to the formula (6) and used for updating the gradient of the criticic network parameter:
where k represents the statistical variable of the sample count, γ represents the learning step size, represents the learning rate, and ranges from [0, 1 ];
step B8: updating the network parameter θ of the Actor according to equation (7):
wherein,
for the learning step size, i.e. learning rate, the range is 0, 1](ii) a Pi represents the strategy according to which the Actor network takes action A in the state S,
representing a gradient with respect to a parameter θ;
step B9: outputting a strategy set pi of the Actor network;
step B10: stopping the circulation;
preferably, the dispensing medicine control server is an APP and a hardware device capable of running an intelligent dispensing medicine algorithm based on deep learning, the APP is an application program executing application software on the hardware device, and the hardware device refers to a hardware device included in a computer or a mobile phone.
Preferably, the dosage proportioning table at least comprises the following two items: the name of the drug and the amount of drug required.
Preferably, the playback buffer pool is used for storing Actor network and Critic network samples, and the samples include (S, a, R, S').
Has the advantages that: the invention provides an intelligent medicine dispensing and preparing method based on deep learning. The method comprises the steps of carrying out data preprocessing on patient condition data collected by an intelligent device end, transmitting the processed data to a prepared medicine control server, carrying out training and learning on the prepared medicine control server by adopting an intelligent medicine dispensing and pharmaceutical algorithm based on deep learning, carrying out accurate analysis on subsequent unknown data by adopting a trained network model, outputting a ratio table of the medicine amount required by different patients, periodically receiving the preprocessed patient data by the prepared medicine control server, and tracking and adjusting the ratio table of the medicine amount required by the patients in time, thereby realizing differentiated and customized medicine configuration services for the patients, simultaneously improving the audience range of medicine preparation for manufacturers and realizing intelligent differentiated management of the prepared medicines.
Drawings
FIG. 1 is a flow chart diagram of an intelligent medicine dispensing and preparing method based on deep learning;
FIG. 2 is a schematic diagram of an algorithm operation principle in an intelligent medicine dispensing and preparing method based on deep learning;
fig. 3 is a schematic diagram of an algorithm flow in an intelligent medicine dispensing and preparing method based on deep learning.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
Basic knowledge: the intelligent medicine dispensing and preparing algorithm based on deep learning comprises three networks: actor, Critic network and Critic target network. The Actor network is formed by a network, and the network also adopts a deep convolutional neural network structure, namely an input layer, a hidden layer and an output layer. The network receives input data and reward or punishment signals fed back by the system, parameters are continuously adjusted through the hidden layer according to the objective function, and a model of the network is trained and learnt. Which is a policy-based Q-network. The Critic network comprises two networks, similar to a deep Q network, an original network used for training the model and a target Q network used for periodically storing the network parameter model of the original network. We refer to the target Q network as Critic target network here.
The idea of the intelligent medicine dispensing and preparing algorithm based on deep learning is as follows:
1, adopting a strategy-based Q network by an Actor network, firstly observing the current state, and executing a behavior by adopting a method of E-greedy selection;
and 2, giving a score by the Critic network according to the current state and behavior of the Actor network, and continuously adjusting a scoring strategy according to the scoring of the total reward accumulated from the current moment t to the end of iteration and the comprehensive scoring judgment of the Critic target network. The total accumulated award is given by system or environmental feedback.
And 3, the Actor network adjusts the behavior strategy of the Actor network according to the scoring, namely the reward, of the Critic network.
And 4, the Critic network scores according to the real reward of the system or environment and the Critic target network, the self scoring strategy is continuously adjusted, the Actor network and the Critic network are continuously trained through the network, and finally the Actor network outputs the optimal behavior set.
The invention provides an intelligent medicine dispensing and preparing method based on deep learning, a flow diagram of the method is shown in figure 1, and the method comprises the following steps:
step A1: the intelligent equipment collects the disease condition data of a current patient, preprocesses the data and transmits the data to the control server for preparing the medicine; the data preprocessing is to file the disease data and arrange the disease data into structured data; the structured data is classified, recorded and filed according to the name, sex, age, medical history, physical examination data, etiology, treatment data, drug use condition, duration and current situation of the patient;
specific example 1: the patient uses the smart phone, the smart bracelet with medical function or the equipment such as blood pressure, blood oxygen, blood sugar and temperature meter to collect the illness state data.
Specific example 2: the patient transmits the illness state data to a drug preparation control server or a drug preparation person through a remote medical device by checking a diagnosis sheet of medical staff, a CT image shot by a machine and the like; the medicine preparation control server adopts an intelligent medicine preparation method based on deep learning to generate a medicine preparation table in a differentiation and intelligent mode.
Specific example 3: medical professionals or sick persons transmit disease data to a dispensing medicine control server or a dispensing person through intelligent equipment through voice or video.
Specific example 4: acquiring a service database of the medical equipment: medical records, electronic medical records, and clinical data including patient admission, diagnosis, hospitalization, treatment, examination, medication, and discharge information.
Step A2: the prepared medicine control server adopts an intelligent medicine preparation algorithm based on deep learning to output a ratio table of the medicine amount required by different patients;
step A3: the prepared medicine control server regularly collects the preprocessed disease condition data of the patient in real time through intelligent equipment, changes the dosage ratio of the medicine required by the patient by adopting the intelligent medicine dispensing and preparing algorithm based on deep learning, and transmits the changed dosage ratio table to the patient or a doctor.
Fig. 2 is a schematic diagram illustrating an algorithm operation principle and structure in an intelligent medicine dispensing and manufacturing method based on deep learning, in which an Actor network first initializes a current state and executes an action by using a certain strategy;
in the specific embodiment 1, the Actor network adopts an e-greedy selection strategy, namely a certain behavior is selected with the probability of e, and a certain behavior is selected from a playback cache pool with the probability of 1-e for execution.
In specific embodiment 2, the Actor network employs a random selection policy, that is, a certain behavior is randomly selected and executed.
In specific embodiment 3, the Actor network selects a policy by using a priority sampling method, that is, selects a certain behavior to execute in a preset priority order during sample sampling.
Further, the Actor network feeds back the current state and the executed behavior to the Critic network, the Critic network initially scores (i.e. rewards) the behavior of the Actor network by adopting a random strategy, the Actor network adjusts the own strategy and behavior according to the rewards, obtains the state at the next moment, sends the obtained state to the Critic network, and stores the current state, the behavior, the rewards and the state at the next moment as a sample tuple in a playback buffer pool. In the subsequent time, the Critic network calculates a loss function according to a real reward given by the environment, a score of the Critic target network and a sample learning score strategy accessed by the Critic network at present, the Critic network and the Critic target network calculate a gradient of the loss function by adopting a formula (5), so that error estimation is carried out, network parameters of the Critic network are continuously adjusted, the trained and adjusted network parameters are periodically copied to the Critic target network, and the Critic target network stores network model parameters which are better trained.
The invention provides an intelligent medicine dispensing and preparing method based on deep learning, an algorithm flow schematic diagram of the method is shown in fig. 3, the algorithm flow is described in steps B1-B10 in the invention content, and details are not repeated here.
Specific example 1: the dosage proportioning table comprises: the name of a certain medicine, the type of the medicine, the property of the medicine and the required dosage.
Specific example 2: the dosage proportioning table comprises: patient name, drug name, required dosage and recommended number of days of administration.
The drug preparation control server is an APP and hardware equipment capable of running an intelligent drug preparation algorithm based on deep learning, the APP is an application program and executes application software on the hardware equipment, and the hardware equipment refers to hardware equipment included in a computer.
Specific example 1: the drug dispensing control server employs an APP computer running an intelligent drug dispensing algorithm based on deep learning.
Specific example 2: the drug preparation control server adopts a mobile phone running an APP of an intelligent drug preparation algorithm based on deep learning.
Specific example 3: the dispensing drug control server employs an APP workstation running an intelligent dispensing pharmaceutical algorithm based on deep learning.
Through the mode, the intelligent medicine dispensing and preparing method based on deep learning disclosed by the invention realizes real-time, differentiated and intelligent medicine proportioning formulation for patients, provides a high-efficiency and convenient method for intelligent and customized service transformation of the medicine dispensing and preparing mode of a medicine dispensing and preparing manufacturer, and has wide market application prospect.
The above description is only presented as an enabling solution for the present invention and should not be taken as a sole limitation on the solution itself.
Claims (5)
1. An intelligent medicine dispensing and preparing method based on deep learning comprises the following steps:
step A1: the intelligent equipment collects the disease condition data of a current patient, preprocesses the data and transmits the data to the control server for preparing the medicine; the data preprocessing is to file the disease data and arrange the disease data into structured data; the structured data is classified, recorded and filed according to the name, sex, age, medical history, physical examination data, etiology, treatment data, drug use condition, duration and current situation of the patient;
step A2: the prepared medicine control server adopts an intelligent medicine preparation algorithm based on deep learning to output a ratio table of the medicine amount required by different patients;
step A3: the prepared medicine control server regularly collects the preprocessed disease condition data of the patient in real time through intelligent equipment, changes the dosage ratio of the medicine required by the patient by adopting the intelligent medicine dispensing and preparing algorithm based on deep learning, and transmits the changed dosage ratio table to the patient or a doctor.
2. The intelligent medicine dispensing and preparing method based on deep learning of claim 1, wherein: the intelligent medicine dispensing and preparing algorithm based on deep learning comprises the following steps:
step B1: respectively initializing parameters w and theta of an Actor network and a Critic network at random;
step B2: judging whether the current time T is smaller than a preset iteration period T, if so, turning to a step B3, otherwise, turning to a step B10;
step B3: initializing a state set S, and obtaining a characteristic vector phi (S) of the state set S after data preprocessing;
step B4: the Actor network uses phi (S) as data input, outputs a corresponding behavior set A at the current moment, and obtains a state S' at the next moment and an immediate reward R of the Critic network based on the behavior set A;
step B5: the Critic network uses phi (S), phi (S ') as data inputs, respectively, to get the total accumulated award V (S) for the current state and the total accumulated award V (S ') for the subsequent state of the output, where V (S) and V (S ') are calculated according to equations (1) and (2), respectively:
V(S′)＝R(t)+βV[S′，argmaxA′V(S′，A′|w|θ)] (2)
wherein R (t) represents an instant prize at time t, β represents a discount factor, A' represents a behavior at the next time, StAnd AtRespectively representing the state and the behavior at the moment t;
step B6: the criticic network randomly draws D samples from the playback buffer pool and calculates an error function according to equation (3):
＝R(t)+αV(S′)-V(S) (3)
wherein α represents an update rate, which ranges from [0, 1 ];
step B7: the criticic network is used as a loss function L (w) according to the formula (4), the gradient of the loss function is calculated according to the formula (5), and the criticic network parameter w is updated according to the formula (6) and used for updating the gradient of the criticic network parameter:
where k represents the statistical variable of the sample count, γ represents the learning step size, represents the learning rate, and ranges from [0, 1 ];
step B8: updating the network parameter θ of the Actor according to equation (7):
wherein,
for the learning step size, i.e. learning rate, the range is 0, 1](ii) a Pi represents the strategy according to which the Actor network takes action A in the state S,
representing a gradient with respect to a parameter θ;
step B9: outputting a strategy set pi of the Actor network;
step B10: the cycle is terminated.
3. The intelligent medicine dispensing and preparing method based on deep learning of claim 1, wherein: the drug preparation control server is an APP and hardware equipment capable of running an intelligent drug preparation algorithm based on deep learning, the APP is an application program and executes application software on the hardware equipment, and the hardware equipment refers to hardware equipment included in a computer or a mobile phone.
4. The intelligent medicine dispensing and preparing method based on deep learning of claim 1, wherein: the dosage proportioning table at least comprises the following two items: the name of the drug and the amount of drug required.
5. The intelligent medicine dispensing and preparing method based on deep learning of claim 2, wherein: the playback buffer pool in step B6 is used to store the Actor network and criticc network samples, which include (S, a, R, S').
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