CN111599433A - Auxiliary prescription method and device for medicinal materials, storage medium and terminal - Google Patents
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Abstract
The invention discloses a method, a device, a storage medium and a terminal for auxiliary prescription of medicinal materials, wherein the method comprises the following steps: receiving symptom description text information input by a user; inputting the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set; generating a recommended medicinal material set based on the medicinal material set, wherein the number of the Chinese medicinal materials in the medicinal material set is greater than or equal to that in the recommended medicinal material set; and displaying the recommended medicinal material set on a display interface. Therefore, by adopting the embodiment of the application, the medicine dispensing can be assisted for the clinician aiming at the symptoms of the patient, and the clinical diagnosis efficiency is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for auxiliary prescription development of medicinal materials, a storage medium and a terminal.
Background
As an important component of Chinese traditional culture, TCM has a long history and profound underlying implications. In the development process of traditional Chinese medicine for thousands of years, people have deep knowledge on the properties and the effects of traditional Chinese medicines, and a plurality of classical formulas with remarkable effect on disease treatment are developed on the basis. With the development of artificial intelligence technology, especially natural language processing technology, advanced artificial intelligence technology is applied to massive traditional Chinese medicine medical record data analysis, the relationship between symptoms and medicinal materials precipitated in the development process of thousands of years of traditional Chinese medicine is extracted from the data, and the related technology is applied to clinic, so that the medicine dispensing of clinical doctors for the symptoms of patients is assisted to have important value.
At present, a traditional Chinese medicine auxiliary prescription method for assisting a majority of clinicians to dispense medicines according to symptoms of patients mainly finds a prescription with the highest similarity to the symptoms by calculating text similarity. At present, the traditional Chinese medicine auxiliary prescription method can only select a prescription from a fixed prescription set, and the medicinal material composition of the prescription is fixed and unchangeable and can not completely adapt to the change of different disease symptoms, thereby reducing the clinical diagnosis efficiency.
Disclosure of Invention
The embodiment of the application provides a method and a device for auxiliary prescription development of medicinal materials, a storage medium and a terminal. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides an auxiliary prescription method for medicinal materials, including:
receiving symptom description text information input by a user;
inputting the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set;
generating a recommended medicinal material set based on the medicinal material set, wherein the number of the Chinese medicinal materials in the medicinal material set is greater than or equal to that in the recommended medicinal material set;
and displaying the recommended medicinal material set on a display interface.
Optionally, the generating a recommended medicine set based on the medicine set includes:
acquiring the priority of each medicinal material in the medicinal material set;
and generating a recommended medicinal material set according to the high-low sequence of the priority.
Optionally, the obtaining the priority of each medicinal material in the medicinal material set includes:
obtaining probability values of all medicinal materials in the medicinal material set;
and determining the priority of each medicinal material according to the probability value of each medicinal material.
Optionally, before receiving the symptom description text information input by the user, the method further includes:
constructing a training data set, wherein the training data set comprises first training data and second training data;
preprocessing the first training data and the second training data to generate a preprocessed training data set;
adopting a deep neural network algorithm to create an auxiliary evolution model;
training the auxiliary evolution model by utilizing the preprocessed training data set to generate a trained auxiliary evolution model; wherein the content of the first and second substances,
and optimizing the auxiliary evolution model by adopting random weight averaging, weight pruning and gradient clipping technologies when the auxiliary evolution model is trained.
Optionally, the generating a preprocessed training data set after preprocessing the first training data and the second training data includes:
acquiring characters in the first training data;
inputting characters in the first training data into a pre-trained character vector model to generate a character vector;
acquiring historical medical record data in the second training data;
acquiring the name of the medicinal material and symptom description information in the historical medical record data;
converting the medicinal material name according to a preset rule to generate a standard medicinal material name;
matching the standard medicinal material name and the symptom description information to generate matching data;
and taking the word vector and the pairing data as a pre-processed training data set.
Optionally, before the obtaining the characters in the first training data, the method further includes:
creating a word vector model;
and training the word vector model by adopting a CBOW model in a gensim toolkit, and then generating a trained word vector model.
Optionally, the auxiliary evolution model obtains the medicinal materials related to the symptom description text information through text classification.
In a second aspect, an embodiment of the present application provides an auxiliary prescription device for medicinal materials, the device includes:
the information receiving module is used for receiving symptom description text information input by a user;
the first set generation module is used for inputting the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set;
a second set generating module, configured to generate a recommended medicinal material set based on the medicinal material set, where the number of medicinal materials in the medicinal material set is greater than or equal to the number of medicinal materials in the recommended medicinal material set;
and the medicinal material display module is used for displaying the recommended medicinal materials on a display interface.
In a third aspect, embodiments of the present application provide a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, a user terminal firstly receives symptom description text information input by a user, then inputs the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set, then generates a recommended medicinal material set based on the medicinal material set, wherein the number of the medicinal materials in the medicinal material set is greater than or equal to that of the medicinal materials in the recommended medicinal material set, and finally displays the recommended medicinal material set on a display interface. According to the method, the presentation learning capacity of the deep learning technology is fully utilized, the medicinal material set of the prescription can be directly generated from the symptom description, the problem of fixed prescription components in the traditional method is solved, the relation between the symptoms and the Chinese medicinal materials can be better found, the medicinal material combination which is the most matched is generated aiming at different symptoms, a clinician can be assisted to dispense medicines aiming at the symptoms of a patient, and the clinical diagnosis efficiency is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flow chart of an auxiliary prescription method for medicinal materials provided in the embodiments of the present application;
fig. 2 is a schematic diagram of an RNN chained network according to an embodiment of the present application;
fig. 3 is an overall structure diagram of an auxiliary evolution system according to an embodiment of the present application;
FIG. 4 is a schematic flow chart of another method for assisting prescription of medicinal materials according to the embodiment of the present application;
fig. 5 is a diagram of a neural network structure of an auxiliary evolution system according to an embodiment of the present application;
fig. 6 is a diagram of an LSTM network architecture provided in an embodiment of the present application;
FIG. 7 is a schematic diagram of an attention mechanism of an auxiliary evolution system provided in an embodiment of the present application;
fig. 8 is a schematic device diagram of an auxiliary extraction device for medicinal materials according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
To date, aiming at the traditional Chinese medicine auxiliary prescription method, the prescription with the highest similarity to symptoms is found mainly by calculating text similarity. At present, the traditional Chinese medicine auxiliary prescription method can only select a prescription from a fixed prescription set, and the medicinal material composition of the prescription is fixed and unchangeable and can not completely adapt to the change of different disease symptoms, thereby reducing the clinical diagnosis efficiency. Therefore, the application provides a method, a device, a storage medium and a terminal for assisting prescription of medicinal materials, so as to solve the problems in the related technical problems. In the technical scheme provided by the application, because the representation learning capability of the deep learning technology is fully utilized, the medicinal material set of the prescription can be directly generated from the symptom description, the problem of fixed prescription components in the traditional method is solved, the relationship between the symptoms and the Chinese medicinal materials can be better found, the best matched medicinal material combination is generated aiming at different symptoms, a clinician can be assisted to dispense medicines aiming at the symptoms of a patient, the clinical diagnosis efficiency is improved, and the following detailed description is provided by adopting an exemplary embodiment.
The method for assisting prescription of medicinal materials provided by the embodiment of the present application will be described in detail below with reference to fig. 1 to 7. The method can be implemented by means of a computer program and can be run on a medicinal material auxiliary prescription device based on a Von Neumann system. The computer program may be integrated into the application or may run as a separate tool-like application. The auxiliary prescription-opening device for the medicinal materials in the embodiment of the present application may be a user terminal, including but not limited to: personal computers, tablet computers, handheld devices, in-vehicle devices, wearable devices, computing devices or other processing devices connected to a wireless modem, and the like. The user terminals may be called different names in different networks, for example: user equipment, access terminal, subscriber unit, subscriber station, mobile station, remote terminal, mobile device, user terminal, wireless communication device, user agent or user equipment, cellular telephone, cordless telephone, Personal Digital Assistant (PDA), terminal equipment in a 5G network or future evolution network, and the like.
Please refer to fig. 1, which provides a schematic flow chart of a method for assisting prescription of a medicinal material in an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application may include the steps of:
s101, receiving symptom description text information input by a user;
the symptom description text information is the patient symptom text information input to the client after the clinician diagnoses the patient, such as symptom information of headache, cough, fever, and the like.
In a possible implementation mode, when a patient sees a doctor, a clinician can know symptom information of the patient through communication with the patient, then the specific symptom information of the patient is completely input into a client, the information is determined through triggering a client button to be processed, and after the client button is triggered, the client generates an instruction containing the patient symptom information and sends the instruction to a server through a wire or a wireless mode to be processed.
S102, inputting the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set;
the auxiliary evolution model is a mathematical model created by utilizing a deep neural network algorithm, the mathematical model is subjected to model training by adopting pre-constructed training data, and the auxiliary evolution model capable of generating a medicinal material set according to the symptom information of the patient is generated after the training is finished. The medicinal material set is different medicinal materials generated after the symptom text information of the patient is processed according to the auxiliary evolution model.
Generally, a deep Neural Network algorithm for constructing an auxiliary evolution model includes a Recurrent Neural Network (RNN) module and an attention module, and random weight averaging, weight pruning and gradient clipping techniques are employed in a model optimization process. The RNN is a recurrent neural network (recurrent neural network) in which sequence data is input, recursion is performed in the evolution direction of the sequence, and all nodes (cyclic units) are connected in a chained manner, and a schematic diagram of the RNN chained network is shown in fig. 2, for example, input elements x0, x1, x2, xt, and the like are input into the fully connected cyclic units to generate elements h0, h1, h2, ht, and the like.
In a possible implementation mode, when the server receives patient symptom information sent by a client, the server calls a pre-trained auxiliary evolution model through an internal setting program, medicinal materials related to symptom description are obtained through a text classification method, then the received patient symptom information is input into the auxiliary evolution model for information processing, and medicinal material set data information is generated for patient symptoms after the processing is finished.
S103, generating a recommended medicinal material set based on the medicinal material set, wherein the number of the Chinese medicinal materials in the medicinal material set is greater than or equal to that in the recommended medicinal material set;
wherein the recommended medicinal material set is a plurality of optimal medicinal materials selected from the medicinal material set.
In a possible implementation manner, when generating the data of the medicinal material set, the server performs probability value calculation on each medicinal material in the medicinal material set through an internally set probability value calculation program to obtain the probability value size corresponding to each medicinal material, then sequentially selects a preset number of medicinal materials from the medicinal materials with the highest probability value as recommended medicinal materials according to the probability value corresponding to each medicinal material in the medicinal material set, and after the generation of the data of the recommended medicinal materials is finished, the server sends each medicinal material in the recommended medicinal material set to the client side in a wired or wireless manner for display.
And S104, displaying the recommended medicinal material set on a display interface.
In the embodiment of the present application, for example, as shown in fig. 3, fig. 3 is a schematic diagram of an auxiliary prescription general structure of a medicinal material according to an embodiment of the present application, first, characters in a general corpus and a chinese medicine corpus are input into a gensim toolkit to be processed, and then, word vectors are generated, name and symptom data of a medicinal material in a medical plan are extracted from the chinese medicine plan, then, names of medicinal materials in the chinese medicine plan are preprocessed according to a Chinese medicine name standard and then matched with symptom data, so as to obtain a prescription data set corresponding to symptoms, finally, the word vectors and the prescription data set corresponding to symptoms are input into a created auxiliary prescription model to be trained, an auxiliary prescription system is generated after training is finished, and when the auxiliary prescription system receives a description of a patient symptom, a recommended medicinal material list is generated according to the description of the patient.
In the embodiment of the application, a user terminal firstly receives symptom description text information input by a user, then inputs the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set, then generates a recommended medicinal material set based on the medicinal material set, wherein the number of the medicinal materials in the medicinal material set is greater than or equal to that of the medicinal materials in the recommended medicinal material set, and finally displays the recommended medicinal material set on a display interface. According to the method, the presentation learning capacity of the deep learning technology is fully utilized, the medicinal material set of the prescription can be directly generated from the symptom description, the problem of fixed prescription components in the traditional method is solved, the relation between the symptoms and the Chinese medicinal materials can be better found, the medicinal material combination which is the most matched is generated aiming at different symptoms, a clinician can be assisted to dispense medicines aiming at the symptoms of a patient, and the clinical diagnosis efficiency is improved.
Please refer to fig. 4, which is a schematic flow chart of an auxiliary prescription method for medicinal materials according to an embodiment of the present application. The present embodiment is exemplified by applying the method for assisting prescription of medicinal materials to the user terminal. The auxiliary prescription development method of the medicinal materials can comprise the following steps:
s201, constructing a training data set, wherein the training data set comprises first training data and second training data;
wherein the training data set is data for training word vectors and the auxiliary evolution model. The first training data is general corpus data and Chinese medicine corpus data for training word vectors, and the data mainly comes from Wikipedia and various Chinese medicine classics. The second training data is 'symptom-prescription' pairing data used for training the auxiliary prescription model and a traditional Chinese medicine name standard name and alias data set, the pairing data is from various public medical records books, and the traditional Chinese medicine name standard name and alias data set is from authority books, such as 'Chinese materia Medica' and 'Chinese pharmacopoeia' and the like.
In a feasible implementation mode, general corpus data and traditional Chinese medicine corpus data are collected from Wikipedia and various traditional Chinese medical books, pairing data are collected from various public medical records and books, and traditional Chinese medicine name standard names and alias data sets are collected from Chinese materia medica, Chinese pharmacopoeia and other works.
S202, preprocessing the first training data and the second training data to generate a preprocessed training data set;
in the embodiment of the application, a user terminal firstly obtains characters in first training data, then inputs the characters in the first training data into a pre-trained character vector model to generate a character vector, then obtains historical medical record data in second training data, then obtains medicinal material names and symptom description information in the historical medical record data, then converts the medicinal material names according to preset rules to generate standard medicinal material names, pairs the standard medicinal material names and the symptom description information to generate paired data, and finally takes the character vector and the paired data as a pre-processed training data set.
In a possible implementation mode, in the preprocessing of the general corpus data and the Chinese medicine corpus data, the user terminal converts the complex and simple characters, removes special symbols and uniform punctuation marks in the text, and removes various annotated and explained texts. When data preprocessing is carried out on the matched data, the standard names and the alias data sets of the names of the traditional Chinese medicinal materials, the user terminal extracts symptom description information from each piece of medical case data, removes dosage information of the medicinal materials from prescription information, only keeps the names of the medicinal materials, and converts all the names of the medicinal materials into standard names through the established medicinal material name data sets. It should be noted that the general corpus data and the chinese herbal corpus data are first training data, and the matching data and the chinese herbal name standard name and alias data set are second data.
S203, creating an auxiliary evolution model by adopting a deep neural network algorithm;
in this embodiment of the application, the auxiliary evolution model is a deep neural network model, for example, as shown in fig. 5, after the symptom text is input into the auxiliary evolution model, the symptom text passes through an Embedding layer, an LSTM network layer, an attention layer and a classification layer of the auxiliary evolution model in sequence, and after being processed by each layer in the auxiliary evolution model, the name of the medicinal material is output.
Specifically, the core of the deep neural network model in the assisted evolution model is a multi-label text classification neural network, in this example, a long-term memory (LSTM) network layer is used to extract features of text information, as shown in fig. 6, for example, an LSTM network layer structure diagram is a special implementation manner of a Recurrent Neural Network (RNN), and a chained network structure thereof enables the sequential data to be processed effectively, because in a chained network such as an RNN, the current input is affected by the previously input information, and by memorizing the previous information, the associative learning of the sequential text information can be realized. The core of the LSTM is a cell state (cell state) which is mainly used to maintain the memory information of the entire model, and how to integrate and extract the effective information from the input information and the memory information at each moment is mainly controlled by three gates, respectively: input gate, output gate and forget the gate. The input information of the three gates is the hidden state information at the previous moment and the input at the current moment. The forgetting gate is used for controlling the information of the state of the joining unit, the input gate is used for controlling the input information at the current moment, and the output gate is used for controlling the output information at the current moment.
In general, a conventional LSTM network can only predict information at a next time by using information before the current time, but in the classification of symptoms, description order between symptoms sometimes has no obvious precedence, and therefore, in the embodiment of the present application, a bidirectional LSTM network is used. The bidirectional LSTM takes the beginning and the end of a text as input at the same time, and can be regarded as two unidirectional LSTM networks running in parallel, so that information before and after the current time can be obtained at each time for prediction, and hidden features can be better extracted from symptom description.
For example, as shown in fig. 5, an attention network layer is further introduced between the output of the LSTM network layer and the input of the classification layer, and an attention mechanism schematic diagram of the attention network layer is shown in fig. 7, so that the model can learn to pay attention to the input symptoms and simulate the expression mode of the main symptoms and the accompanying symptoms in the description of the clinical symptoms. The attention network is realized through linear mapping, and the model can learn key semantic information in the text by learning the weight vector of the linear mapping, so that the classification accuracy is improved.
S204, training the auxiliary evolution model by using the preprocessed training data set to generate a trained auxiliary evolution model;
in a possible implementation manner, the preprocessed first training data and second training data are obtained based on step S202, the created auxiliary evolution model is obtained based on step S203, then the auxiliary evolution model is trained by using the preprocessed first training data and second training data, and after the training is finished, an auxiliary evolution model with a diagnosis function is generated, wherein the diagnosis function at least comprises automatically generating recommended medicinal materials according to the patient symptoms.
Furthermore, optimization methods capable of improving the convergence speed of model training and improving the generalization performance of the model are introduced in the process of evolution model training, and the optimization methods comprise techniques such as random weight averaging, weight pruning and gradient clipping. The random weight averaging method can improve the generalization capability of the model under the condition of not increasing the reasoning time of the model. By introducing weight pruning at different stages of the model, the overfitting degree can be reduced in the model training process. The introduction of gradient clipping can improve the stability of the model in the training process and accelerate the convergence speed.
Further, top5 accuracy is used as a model performance evaluation criterion, that is, 5 labels with the maximum prediction probability are compared with real labels, and the prediction accuracy is calculated.
S205, receiving symptom description text information input by a user;
specifically, refer to step S101, which is not described herein again.
S206, inputting the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set;
specifically, refer to step S102, which is not described herein again.
S207, acquiring the priority of each medicinal material in the medicinal material set;
the priority is the priority obtained by sequencing according to the calculated probability values of the medicinal materials in the medicinal material set, and the higher the probability value of the medicinal material is, the higher the corresponding priority is.
S208, generating a recommended medicinal material set according to the high-low sequence of the priority, wherein the number of the Chinese medicinal materials in the medicinal material set is greater than or equal to that in the recommended medicinal material set;
in one possible implementation, for example, as shown in Table 1, it can be seen from Table 1 that the Chinese herbs are in the Chinese herb set
| Notoginseng radix | Black nightshade | White orchid | Ginseng radix | Codonopsis pilosula | Radix astragali |
| 10/100 | 22/100 | 13/100 | 20/100 | 28/100 | 17/100 |
The probability value of pseudo-ginseng is 10/100, the probability value of black nightshade is 22/100, the probability value of michelia alba is 13/100, the probability value of ginseng is 20/100, the probability value of codonopsis pilosula is 28/100, the records of astragalus membranaceus are 17/100, the records are arranged according to the sequence of priorities, and the priorities are codonopsis pilosula, black nightshade, ginseng, astragalus membranaceus and michelia alba. When three traditional Chinese medicines are recommended according to the priority level, the recommended traditional Chinese medicines are codonopsis pilosula, black nightshade and ginseng respectively. The recommended amount can be set according to the actual scene, and is not limited here.
S209, displaying the recommended medicinal material set on a display interface.
Specifically, refer to step S104, which is not described herein again.
In the embodiment of the application, a user terminal firstly receives symptom description text information input by a user, then inputs the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set, then generates a recommended medicinal material set based on the medicinal material set, wherein the number of the medicinal materials in the medicinal material set is greater than or equal to that of the medicinal materials in the recommended medicinal material set, and finally displays the recommended medicinal material set on a display interface. The traditional Chinese medicine auxiliary prescription method provided by the application makes full use of the representation learning capacity of the deep neural network, avoids excessive introduction of human experience knowledge in the model, performs end-to-end model training through high-quality labeled data, automatically acquires key information from symptom description through the model, and predicts related medicinal materials with treatment effects, so that a clinician can be assisted to dispense medicines for the symptoms of a patient, and the clinical diagnosis efficiency is improved.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Please refer to fig. 8, which illustrates a schematic structural diagram of an auxiliary prescription device for medicinal materials according to an exemplary embodiment of the present invention. The auxiliary prescription-opening device of the medicinal materials can be realized into all or part of the terminal through software, hardware or the combination of the software and the hardware. The device 1 comprises an information receiving module 10, a first set generating module 20, a second set generating module 30 and a medicinal material display module 40.
The information receiving module 10 is used for receiving symptom description text information input by a user;
a first set generating module 20, configured to input the symptom description text information into a pre-trained auxiliary evolution model, so as to generate a medicinal material set;
a second set generating module 30, configured to generate a recommended medicinal material set based on the medicinal material set, where the number of Chinese medicinal materials in the medicinal material set is greater than or equal to the number of Chinese medicinal materials in the recommended medicinal material set;
and the medicinal material display module 40 is used for displaying the recommended medicinal materials on a display interface.
It should be noted that, when the auxiliary extraction device for medicinal materials provided in the above embodiment executes the auxiliary extraction method for medicinal materials, the division of the above functional modules is merely used as an example, and in practical applications, the above function distribution can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the above described functions. In addition, the device for assisting prescription development of medicinal materials and the method for assisting prescription development of medicinal materials provided by the above embodiments belong to the same concept, and the detailed implementation process is shown in the method embodiments and will not be described herein.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the embodiment of the application, a user terminal firstly receives symptom description text information input by a user, then inputs the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set, then generates a recommended medicinal material set based on the medicinal material set, wherein the number of the medicinal materials in the medicinal material set is greater than or equal to that of the medicinal materials in the recommended medicinal material set, and finally displays the recommended medicinal material set on a display interface. The traditional Chinese medicine auxiliary prescription method provided by the application makes full use of the representation learning capacity of the deep neural network, avoids excessive introduction of human experience knowledge in the model, performs end-to-end model training through high-quality labeled data, automatically acquires key information from symptom description through the model, and predicts related medicinal materials with treatment effects, so that a clinician can be assisted to dispense medicines for the symptoms of a patient, and the clinical diagnosis efficiency is improved.
The invention also provides a computer readable medium, on which program instructions are stored, and the program instructions, when executed by a processor, implement the method for assisting prescription of medicinal materials provided by the above-mentioned method embodiments.
The invention also provides a computer program product containing instructions, which when run on a computer causes the computer to execute the method for auxiliary prescription of medicinal materials according to the above method embodiments.
Please refer to fig. 9, which provides a schematic structural diagram of a terminal according to an embodiment of the present application. As shown in fig. 9, the terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
Wherein a communication bus 1002 is used to enable connective communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 9, the memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and an auxiliary prescription application program for medicinal materials.
In the terminal 1000 shown in fig. 9, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; the processor 1001 may be configured to call the auxiliary prescription application of the medicinal material stored in the memory 1005, and specifically perform the following operations:
receiving symptom description text information input by a user;
inputting the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set;
generating a recommended medicinal material set based on the medicinal material set, wherein the number of the Chinese medicinal materials in the medicinal material set is greater than or equal to that in the recommended medicinal material set;
and displaying the recommended medicinal material set on a display interface.
In one embodiment, the processor 1001, when executing the generating of the recommended set of medicinal materials based on the set of medicinal materials, specifically performs the following operations:
acquiring the priority of each medicinal material in the medicinal material set;
and generating a recommended medicinal material set according to the high-low sequence of the priority.
In one embodiment, the processor 1001, when performing the acquiring the priority of each of the medicinal materials in the medicinal material set, specifically performs the following operations:
obtaining probability values of all medicinal materials in the medicinal material set;
and determining the priority of each medicinal material according to the probability value of each medicinal material.
In one embodiment, the processor 1001, before performing the receiving of the symptom description text information for the user input, further performs the following:
constructing a training data set, wherein the training data set comprises first training data and second training data;
preprocessing the first training data and the second training data to generate a preprocessed training data set;
adopting a deep neural network algorithm to create an auxiliary evolution model;
training the auxiliary evolution model by utilizing the preprocessed training data set to generate a trained auxiliary evolution model; wherein the content of the first and second substances,
and optimizing the auxiliary evolution model by adopting random weight averaging, weight pruning and gradient clipping technologies when the auxiliary evolution model is trained.
In an embodiment, when the processor 1001 performs the preprocessing on the first training data and the second training data to generate a preprocessed training data set, specifically performs the following operations:
acquiring characters in the first training data;
inputting characters in the first training data into a pre-trained character vector model to generate a character vector;
acquiring historical medical record data in the second training data;
acquiring the name of the medicinal material and symptom description information in the historical medical record data;
converting the medicinal material name according to a preset rule to generate a standard medicinal material name;
matching the standard medicinal material name and the symptom description information to generate matching data;
and taking the word vector and the pairing data as a pre-processed training data set.
In one embodiment, the processor 1001, when performing the acquiring of the character in the first training data, further performs the following:
creating a word vector model;
and training the word vector model by adopting a CBOW model in a gensim toolkit, and then generating a trained word vector model.
In the embodiment of the application, a user terminal firstly receives symptom description text information input by a user, then inputs the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set, then generates a recommended medicinal material set based on the medicinal material set, wherein the number of the medicinal materials in the medicinal material set is greater than or equal to that of the medicinal materials in the recommended medicinal material set, and finally displays the recommended medicinal material set on a display interface. The traditional Chinese medicine auxiliary prescription method provided by the application makes full use of the representation learning capacity of the deep neural network, avoids excessive introduction of human experience knowledge in the model, performs end-to-end model training through high-quality labeled data, automatically acquires key information from symptom description through the model, and predicts related medicinal materials with treatment effects, so that a clinician can be assisted to dispense medicines for the symptoms of a patient, and the clinical diagnosis efficiency is improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (10)
1. An auxiliary prescription method of medicinal materials is characterized by comprising the following steps:
receiving symptom description text information input by a user;
inputting the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set;
generating a recommended medicinal material set based on the medicinal material set, wherein the number of the Chinese medicinal materials in the medicinal material set is greater than or equal to that in the recommended medicinal material set;
and displaying the recommended medicinal material set on a display interface.
2. The method of claim 1, wherein generating a set of recommended drugs based on the set of drugs comprises:
acquiring the priority of each medicinal material in the medicinal material set;
and generating a recommended medicinal material set according to the high-low sequence of the priority.
3. The method of claim 2, wherein the obtaining the priority of each of the herbs in the herb collection comprises:
obtaining probability values of all medicinal materials in the medicinal material set;
and determining the priority of each medicinal material according to the probability value of each medicinal material.
4. The method of claim 1, wherein prior to receiving symptom description textual information for user input, further comprising:
constructing a training data set, wherein the training data set comprises first training data and second training data;
preprocessing the first training data and the second training data to generate a preprocessed training data set;
adopting a deep neural network algorithm to create an auxiliary evolution model;
training the auxiliary evolution model by utilizing the preprocessed training data set to generate a trained auxiliary evolution model; wherein the content of the first and second substances,
and optimizing the auxiliary evolution model by adopting random weight averaging, weight pruning and gradient clipping technologies when the auxiliary evolution model is trained.
5. The method of claim 4, wherein preprocessing the first training data and the second training data to generate a preprocessed training data set comprises:
acquiring characters in the first training data;
inputting characters in the first training data into a pre-trained character vector model to generate a character vector;
acquiring historical medical record data in the second training data;
acquiring the name of the medicinal material and symptom description information in the historical medical record data;
converting the medicinal material name according to a preset rule to generate a standard medicinal material name;
matching the standard medicinal material name and the symptom description information to generate matching data;
and taking the word vector and the pairing data as a pre-processed training data set.
6. The method of claim 5, wherein before the obtaining the characters in the first training data, further comprising:
creating a word vector model;
and training the word vector model by adopting a CBOW model in a gensim toolkit, and then generating a trained word vector model.
7. The method of claim 1, wherein the auxiliary evolution model obtains the medicinal materials related to the symptom description text information through text classification.
8. An auxiliary extraction device for medicinal materials, which is characterized in that the device comprises:
the information receiving module is used for receiving symptom description text information input by a user;
the first set generation module is used for inputting the symptom description text information into a pre-trained auxiliary evolution model to generate a medicinal material set;
a second set generating module, configured to generate a recommended medicinal material set based on the medicinal material set, where the number of medicinal materials in the medicinal material set is greater than or equal to the number of medicinal materials in the recommended medicinal material set;
and the medicinal material display module is used for displaying the recommended medicinal materials on a display interface.
9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to carry out the method steps according to any one of claims 1 to 7.
10. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1 to 7.
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