CN114428836A - Information processing method and device, readable storage medium and electronic equipment - Google Patents
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Abstract
Description
Technical Field
The present disclosure relates to the field of information processing technologies, and in particular, to an information processing method and apparatus, a readable storage medium, and an electronic device.
Background
As a treasure in the traditional Chinese medicine, the traditional Chinese medicine condenses the precious experience of saving old people and strengthening the wound of the suspended kettle in the past millennium. In the process of diagnosis and treatment of traditional Chinese medicine, a very core treatment means is to prepare formulas by a plurality of different Chinese herbal medicines, and patients are decocted with the traditional Chinese medicines based on the formulas to take the traditional Chinese medicines for treating diseases. The prescriptions are generally classified into two types, one is a classic traditional Chinese medicine prescription recorded in the traditional Chinese medicine works, the prescription is usually composed of a plurality of fixed Chinese herbal medicines, and the other is a prescription prepared by the traditional Chinese medicine according to the actual physical state of a patient, namely a self-prepared prescription. Each Chinese herbal medicine usually contains a plurality of chemical components, and when different Chinese herbal medicines are matched for use, some chemical reactions are likely to occur. Particularly, in the process of preparing the traditional Chinese medicine self-prepared prescription, because the diagnosis and treatment levels are uneven, adverse reactions possibly generated to human bodies in the process of matching and using Chinese herbal medicines are not perfectly considered when the traditional Chinese medicine self-prepared prescription is used. Therefore, how to match Chinese herbal medicines more safely by computer-aided traditional Chinese medicine becomes the key of research.
Disclosure of Invention
In order to overcome the problems in the related art, the present disclosure provides an information processing method, an information processing apparatus, a readable storage medium, and an electronic device.
In order to achieve the above object, in a first aspect, the present disclosure provides an information processing method including:
obtaining a bag-of-words vector corresponding to a self-prepared prescription, wherein the self-prepared prescription comprises information of a plurality of Chinese herbal medicines;
determining target topic probability distribution corresponding to the self-prepared prescription through a pre-trained topic model according to the bag-of-words vector and the probability of each Chinese herbal medicine appearing in the prescription library;
and generating verification result information aiming at the self-prepared prescription according to the target theme probability distribution.
Optionally, the determining, according to the bag-of-words vector and the probability of each Chinese herbal medicine appearing in the prescription library, a target topic probability distribution corresponding to the self-prepared prescription through a pre-trained topic model includes:
inputting the bag-of-words vector into a pre-trained topic model to obtain the probability distribution of the topic-Chinese herbal medicine corresponding to the self-prepared prescription;
and determining the target topic probability distribution corresponding to the self-prepared prescription according to the topic-Chinese herbal medicine probability distribution and the probability of each Chinese herbal medicine appearing in the prescription library.
Optionally, the determining a target topic probability distribution corresponding to the self-prepared formula according to the topic-Chinese herbal medicine probability distribution and the probability of each Chinese herbal medicine appearing in the formula library includes:
for each Chinese herbal medicine, determining the probability distribution of the Chinese herbal medicine belonging to each topic according to the topic-Chinese herbal medicine probability distribution and the probability of the Chinese herbal medicine appearing in the prescription library;
and determining the sum of the probability distribution of each Chinese herbal medicine belonging to each topic as the target topic probability distribution corresponding to the self-prepared prescription.
Optionally, the determining the probability distribution of the Chinese herbal medicine belonging to each topic according to the topic-Chinese herbal medicine probability distribution and the probability of the Chinese herbal medicine appearing in the prescription library includes:
obtaining edge probability distribution of a theme;
and determining the probability distribution of the Chinese herbal medicine belonging to each topic based on Bayes theorem according to the topic-Chinese herbal medicine probability distribution, the probability of the Chinese herbal medicine appearing in the prescription library and the marginal probability distribution of the topic.
Optionally, the obtaining the edge probability distribution of the topic includes:
acquiring a prescription-subject probability distribution corresponding to each reference prescription in the prescription library;
and determining the marginal probability distribution of the theme based on Bayes theorem according to the prescription-theme probability distribution corresponding to each reference prescription and the probability of each reference prescription being adopted.
Optionally, the generating, according to the target topic probability distribution, verification result information for the self-prepared prescription includes:
acquiring the reference theme probability distribution corresponding to each reference prescription in the prescription library;
determining N reference prescriptions with the highest similarity between the corresponding reference subject probability distribution and the target subject probability distribution from the prescription library, wherein N is greater than 1;
if the syndrome type corresponding to the N reference prescriptions with the highest similarity comprises the syndrome type corresponding to the self-prepared prescription, determining that the self-prepared prescription passes the verification;
and if the syndrome type corresponding to the self-prepared prescription is not contained in the syndrome types corresponding to the N reference prescriptions with the highest similarity, determining that the self-prepared prescription fails to be verified.
Optionally, the bag-of-words vector obtained from the quasi-prescription agent includes:
obtained from the weight of each herb in the quasi-formulation;
determining the occurrence frequency of each Chinese herbal medicine in the word bag vector corresponding to the self-prepared prescription according to the weight of each Chinese herbal medicine;
aiming at each Chinese herbal medicine, generating an extended sequence corresponding to the Chinese herbal medicine according to the occurrence frequency of the Chinese herbal medicine, wherein each element in the extended sequence is the Chinese herbal medicine, and the length of the extended sequence is the occurrence frequency of the Chinese herbal medicine;
and combining the extended sequences corresponding to each Chinese herbal medicine according to a preset combination sequence to obtain a bag-of-word vector corresponding to the self-prepared prescription.
In a second aspect, the present disclosure provides an information processing apparatus comprising:
the acquisition module is used for acquiring the corresponding bag-of-words vector of the self-prepared prescription, wherein the self-prepared prescription comprises a plurality of Chinese herbal medicine information;
the determining module is used for determining target theme probability distribution corresponding to the self-prepared prescription through a pre-trained theme model according to the bag-of-word vector acquired by the acquiring module and the probability of each Chinese herbal medicine in the prescription library;
and the generating module is used for generating verification result information aiming at the self-prepared prescription according to the target subject probability distribution determined by the determining module.
In a third aspect, the present disclosure provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method provided by the first aspect of the present disclosure.
In a fourth aspect, the present disclosure provides an electronic device comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method provided by the first aspect of the present disclosure.
In the technical scheme, firstly, a bag-of-words vector corresponding to a self-prepared prescription is obtained, wherein the self-prepared prescription comprises information of a plurality of Chinese herbal medicines; then, according to the bag-of-words vector and the probability of each Chinese herbal medicine appearing in the prescription library, determining the target theme probability distribution corresponding to the self-prepared prescription through a pre-trained theme model; and finally, generating verification result information aiming at the self-prepared prescription according to the probability distribution of the target subject. Therefore, whether the medicine collocation of the self-prepared prescription is reasonable or not can be automatically verified, and the Chinese herbal medicine is matched more safely in the traditional Chinese medicine. In addition, the target theme probability distribution corresponding to the self-prepared prescription is determined through the theme model, the self-prepared prescription can be more accurately described, and therefore the accuracy of the verification result information of the self-prepared prescription is improved.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure, but do not constitute a limitation of the disclosure. In the drawings:
fig. 1 is a flow chart illustrating an information processing method according to an example embodiment.
FIG. 2 is a flow diagram illustrating a method of obtaining bag of words vectors corresponding to self-prepared prescriptions according to an example embodiment.
FIG. 3 is a flow chart illustrating a method for determining a target topic probability distribution for a self-prepared prescription based on a bag-of-words vector and a probability of each herbal occurrence in a library of prescriptions using a pre-trained topic model according to an exemplary embodiment.
FIG. 4 is a flow diagram illustrating a method for generating verification result information for a self-proposed prescription based on a target topic probability distribution, according to an example embodiment.
Fig. 5 is a block diagram illustrating an information processing apparatus according to an example embodiment.
FIG. 6 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 7 is a block diagram illustrating an electronic device in accordance with an example embodiment.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flow chart illustrating an information processing method according to an example embodiment. As shown in fig. 1, the method includes the following S101 to S103.
In S101, a bag-of-words vector corresponding to a self-prepared prescription is obtained, wherein the self-prepared prescription includes information of a plurality of Chinese herbal medicines.
In the present disclosure, a self-prepared formulation is a formulation prepared by traditional Chinese medicine according to the actual physical condition of a patient, and generally consists of a plurality of Chinese herbal medicines, wherein the Chinese herbal medicine components and the weight in the self-prepared formulation are determined according to the actual physical condition of the patient. The Chinese herbal medicine information can comprise Chinese herbal medicine names and Chinese herbal medicine weights, and the bag-of-words vector corresponding to the self-prepared prescription is composed of the names of the Chinese herbal medicines in the self-prepared prescription and can be determined according to the names of the Chinese herbal medicines in the self-prepared prescription and the weights of the Chinese herbal medicines.
In addition, the self-prepared prescription can be regarded as a text in text mining, each Chinese herbal medicine in the self-prepared prescription is regarded as a word, the weight of the Chinese herbal medicine is regarded as the occurrence frequency, meanwhile, the Chinese herbal medicine used in the self-prepared prescription only considers the weight and does not consider the sequence in a Chinese medical diagnosis and treatment system, and the bag-of-words vector does not consider the sequence of the words and is very matched with the relevant characteristics of the self-prepared prescription on the sequence of the Chinese herbal medicine, so that the bag-of-words vector is adopted for carrying out data modeling on the self-prepared prescription.
In addition, comma intervals can be adopted among the Chinese herbal medicine names in the word bag vector, so that Chinese word segmentation is convenient when the self-prepared formula is used as a Chinese text for calculation.
In S102, according to the bag-of-words vector and the probability of each Chinese herbal medicine appearing in the prescription library, the target theme probability distribution corresponding to the self-prepared prescription is determined through a pre-trained theme model.
In the present disclosure, the prescription library may be composed of classic traditional Chinese medicine prescriptions, wherein the classic traditional Chinese medicine prescriptions may be collected from traditional Chinese medicine works such as "treatise on typhoid", "formula of decoction", and "Changsha prescription Song Shu", wherein the classic traditional Chinese medicine prescriptions are composed of several fixed Chinese herbal medicines, and it is considered that the medicine matching in the classic traditional Chinese medicine prescriptions is a combination that does not cause adverse chemical reactions. The probability of a Chinese herbal medicine appearing in the prescription library is the number of prescriptions containing the Chinese herbal medicine in the prescription library/the total number of prescriptions in the prescription library.
In S103, verification result information for the self-prepared prescription is generated according to the target topic probability distribution.
In the present disclosure, the verification result information may include verification pass and verification fail.
In the technical scheme, firstly, a bag-of-words vector corresponding to a self-prepared prescription is obtained, wherein the self-prepared prescription comprises information of a plurality of Chinese herbal medicines; then, according to the bag-of-words vector and the probability of each Chinese herbal medicine appearing in the prescription library, determining the target theme probability distribution corresponding to the self-prepared prescription through a pre-trained theme model; and finally, generating verification result information aiming at the self-prepared prescription according to the probability distribution of the target subject. Therefore, whether the medicine collocation of the self-prepared prescription is reasonable or not can be automatically verified, so that the Chinese herbal medicine is matched more safely in the traditional Chinese medicine. In addition, the target theme probability distribution corresponding to the self-prepared prescription is determined through the theme model, the self-prepared prescription can be more accurately described, and therefore the accuracy of the verification result information of the self-prepared prescription is improved.
A detailed description will be given below of a specific embodiment of the bag-of-words vector obtained from the pseudo-formula in S101. Specifically, this can be realized by S1011 to S1014 shown in fig. 2.
In S1011, the weight of each herb in the pseudo formula is obtained.
In S1012, the number of occurrences of each herb in the bag-of-words vector corresponding to the self-prepared prescription is determined based on the weight of each herb.
In the disclosure, the integer ratio of the weight of each Chinese herbal medicine in the self-prepared prescription can be determined, and then, for each Chinese herbal medicine, the ratio corresponding to the Chinese herbal medicine in the integer ratio is determined as the occurrence frequency of the Chinese herbal medicine in the bag-of-words vector corresponding to the self-prepared prescription.
In S1013, for each Chinese herbal medicine, an extended sequence corresponding to the Chinese herbal medicine is generated according to the occurrence frequency of the Chinese herbal medicine.
In the present disclosure, each element in the extended sequence is the Chinese herbal medicine, and the length of the extended sequence is the number of occurrences of the Chinese herbal medicine.
In S1014, the expansion sequences corresponding to each chinese herbal medicine are combined according to a preset combination order to obtain a bag-of-word vector corresponding to the self-prepared prescription.
In the disclosure, the preset combination order may be the order of the Chinese herbal medicines in the self-prepared formula, or the order of the Chinese herbal medicines in the self-prepared formula from large to small in weight, or the order of the Chinese herbal medicines in the self-prepared formula from small to large in weight, and the disclosure is not particularly limited.
Illustratively, the self-prepared prescription is composed of four Chinese herbal medicines, namely ephedra, cassia twig, almond and liquorice, wherein the weight of the ephedra is 9 g, the weight of the cassia twig is 6 g, the weight of the almond is 5g, and the weight of the liquorice is 2g, so that the integral ratio of the weight of the Chinese herbal medicines in the self-prepared prescription is 9:6:5:2, namely the weight of the ephedra: weight of cassia twig: the weight of the almonds is as follows: if the weight of the liquorice is 9:6:5:2, the occurrence frequency of the ephedra, the occurrence frequency of the cassia twig, the occurrence frequency of the almond and the occurrence frequency of the liquorice in the corresponding word bag vector of the self-prepared formula are 9, 6 and 2 respectively; therefore, the extended sequence corresponding to the ephedra is 'ephedra, ephedra', the extended sequence corresponding to the cassia twig is 'cassia twig, cassia twig', the extended sequence corresponding to the almond is 'almond, almond', the extended sequence corresponding to the licorice is 'licorice, licorice'; then, according to the sequence of the Chinese herbal medicines in the self-prepared prescription, combining the extension sequences corresponding to the Chinese herbal medicines to obtain a word bag vector corresponding to the self-prepared prescription, wherein the word bag vector is { ephedra, cassia twig, almond, licorice }.
In another example, the self-prepared prescription is composed of four Chinese herbal medicines including ephedra, cassia twig, almond and licorice, wherein the weight of the ephedra is 1.5 g, the weight of the cassia twig is 2.5 g, the weight of the almond is 3g, and the weight of the licorice is 2g, so that the integral ratio of the weight of the Chinese herbal medicines in the self-prepared prescription is 3:5:6:4, namely the weight of the ephedra: weight of cassia twig: the weight of the almonds is as follows: if the weight of the liquorice is 3:5:6:4, the occurrence frequency of the ephedra, the occurrence frequency of the cassia twig, the occurrence frequency of the almond and the occurrence frequency of the liquorice in the corresponding word bag vector of the self-prepared formula are 3, 5 and 6 respectively; therefore, the extended sequence corresponding to the ephedra is 'ephedra, ephedra', the extended sequence corresponding to the cassia twig is 'cassia twig, cassia twig', the extended sequence corresponding to the almond is 'almond, almond', the extended sequence corresponding to the liquorice is 'liquorice, liquorice and liquorice'; then, the Chinese herbal medicines in the self-prepared prescription are combined according to the sequence of the weights from large to small, and the obtained word bag vector corresponding to the self-prepared prescription is { almond, cassia twig, liquorice, ephedra and ephedra }.
The following is a detailed description of a specific embodiment of determining a target topic probability distribution corresponding to a self-prepared formula through a pre-trained topic model according to the bag-of-word vector and the probability of each Chinese herbal medicine appearing in the formula library in S102. Specifically, it may be realized by S1021 and S1022 shown in fig. 3.
In S1021, the bag-of-words vector is input into a pre-trained topic model to obtain the topic-Chinese herbal medicine probability distribution corresponding to the self-prepared prescription.
In the present disclosure, the topic model may be, for example, Latent Dirichlet Allocation (LDA), Probabilistic Latent Semantic Analysis (PLSA), or the like. The subject is used as an intermediate implicit variable, and the potential collocation rule of Chinese herbal medicines used in the self-prepared formula is shown to a certain extent; topic-Chinese herbal probability distribution is used to characterize the probability distribution of Chinese herbal on each topic.
In S1022, the target topic probability distribution corresponding to the self-prepared formula is determined according to the topic-Chinese herbal medicine probability distribution and the probability of each Chinese herbal medicine appearing in the formula library.
In the present disclosure, the target topic probability distribution corresponding to the self-prepared formula can be determined through the following steps (1) and (2):
(1) for each Chinese herbal medicine, determining the probability distribution of the Chinese herbal medicine belonging to each topic according to the topic-Chinese herbal medicine probability distribution and the probability of the Chinese herbal medicine appearing in the prescription library.
Specifically, the edge probability distribution of the topic may be obtained first; then, according to the probability distribution of the theme-Chinese herbal medicine, the probability of the Chinese herbal medicine appearing in the prescription library and the marginal probability distribution of the theme, the probability distribution of the Chinese herbal medicine belonging to each theme is determined based on Bayes' theorem.
Illustratively, the probability distribution of the chinese herbal medicine belonging to each topic can be determined by the following equation (1) based on bayes' theorem according to the topic-chinese herbal medicine probability distribution, the probability of the chinese herbal medicine appearing in the formula library, and the marginal probability distribution of the topic:
p (topic | chinese herbal medicine i) ═ P (topic) × P (topic)/P (chinese herbal medicine i) (1)
Wherein, the Chinese herbal medicine i is the ith Chinese herbal medicine in the self-prepared prescription, and P (subject | Chinese herbal medicine i) is the probability distribution of the Chinese herbal medicine i belonging to each subject; p (Chinese herbal medicine i | theme) is theme-Chinese herbal medicine probability distribution corresponding to the Chinese herbal medicine i in theme-Chinese herbal medicine probability distribution corresponding to the self-prepared prescription, namely the probability distribution of the Chinese herbal medicine i on each theme; p (topic) is the edge probability distribution of the topic; p (Chinese herbal medicine i) is the probability of occurrence of the Chinese herbal medicine i in the prescription library.
(2) And determining the sum of the probability distribution of each Chinese herbal medicine belonging to each topic as the target topic probability distribution corresponding to the self-prepared prescription.
For example, the self-prepared formula includes chinese herbal medicine 1, chinese herbal medicine 2, chinese herbal medicine 3, and chinese herbal medicine 4, and the target topic probability distribution corresponding to the self-prepared formula is P (topic | chinese herbal medicine 1) + P (topic | chinese herbal medicine 2) + P (topic | chinese herbal medicine 3) + P (topic | chinese herbal medicine 4).
The following is a detailed description of a specific embodiment of obtaining the edge probability distribution of the topic involved in the step (1). Specifically, the method can be realized by the following steps (i) and (ii):
firstly, acquiring a prescription-subject probability distribution corresponding to each reference prescription in a prescription library.
In the present disclosure, a prescription-topic probability distribution refers to a probability distribution where a reference prescription belongs to each topic. Specifically, for each reference prescription, the bag-of-words vector corresponding to the reference prescription may be obtained in a manner similar to the bag-of-words vector obtained from the quasi-prescription, which is not described in detail in this disclosure; and then, inputting the bag-of-words vector corresponding to the reference prescription into a pre-trained topic model to obtain the prescription-topic probability distribution corresponding to the reference prescription. Wherein, the output of the theme model comprises the theme probability distribution corresponding to the reference prescription and the theme-Chinese herbal medicine probability distribution corresponding to the reference prescription.
Secondly, determining the marginal probability distribution of the theme based on Bayes theorem according to the prescription-theme probability distribution corresponding to each reference prescription and the probability of each reference prescription being adopted.
For example, the edge probability distribution of the topic may be determined by the following equation (2) based on bayesian theorem according to the prescription-topic probability distribution corresponding to each reference prescription and the probability that each reference prescription is adopted:
wherein P (topic | topic j) is the topic-topic probability distribution corresponding to the jth reference topic in the topic library, j is 1,2, L, M is the total number of topics in the topic library; p (formula j) is the probability that the jth reference formula in the formula library was adopted.
The reference prescriptions in the prescription library are individuals which independently exist, and the probability of each reference prescription being adopted accords with the independent distribution, so the probability of each reference prescription being adopted can be assigned in the same way, for example, the probability of each reference prescription being adopted is a constant c, wherein c is more than 0 and less than or equal to 1. For the sake of simplicity of calculation, the probability of each reference formula being used is assigned to 1, i.e., P (formula j) is 1, j is 1,2, L, M.
In addition, when the edge probability distribution of the theme is acquired for the first time, the edge probability distribution of the theme can be acquired by adopting the steps I and II, and then the acquired edge probability distribution of the theme can be stored in the corresponding storage module, so that the edge distribution of the theme can be quickly acquired by accessing the storage module.
A detailed description will be given below of a specific embodiment of generating verification result information for a self-prepared prescription from the target topic probability distribution in S103. Specifically, it can be realized by S1031 to S1035 shown in fig. 4.
In S1031, a reference topic probability distribution corresponding to each reference prescription in the prescription library is obtained.
In this disclosure, the probability distribution of the reference subject corresponding to each reference prescription in the prescription library may be determined in a manner similar to the determination of the probability distribution of the target subject corresponding to the self-prepared prescription in S102, which is not described in detail in this disclosure.
In S1032, the N reference prescriptions with the highest similarity between the corresponding reference topic probability distribution and the target topic probability distribution are determined from the prescription library.
In the present disclosure, N is greater than 1.
For example, the similarity between the reference topic probability distribution and the target topic probability distribution corresponding to each reference prescription can be measured through the euclidean distance, the cosine similarity, and the like.
In S1033, it is determined whether the syndrome corresponding to the N reference formulas with the highest similarity includes the syndrome corresponding to the self-prepared formula.
In the present disclosure, syndrome type refers to the expression form of "syndrome" with the normative pathological contents of disease location, normal cause, disease nature and disease condition, which reflects the temporal and spatial variation of the disease, wherein syndrome, i.e. syndrome, refers to the generalization of pathological attributes at a certain stage in the disease process.
If the syndrome corresponding to the N reference formulas with the highest similarity includes the syndrome corresponding to the self-prepared formula, it indicates that the self-prepared formula is reasonable in medication matching, and at this time, the following S1034 is performed; if the syndrome corresponding to the N reference formulas with the highest similarity does not include the syndrome corresponding to the self-prepared formula, it indicates that the self-prepared formula is not appropriate for administration, and at this time, the following S1035 is performed.
In S1034, the self-prepared formula is determined to be validated.
In S1035, it is determined that the self-prepared prescription failed to verify.
For example, N ═ 3, the syndrome type corresponding to the self-prepared prescription L is a heart-blood deficiency syndrome, and the 3 reference prescriptions determined from the prescription library and having the highest similarity between the corresponding reference subject probability distribution and the target subject probability distribution are reference prescription a, reference prescription B and reference prescription C, respectively, wherein the syndrome type corresponding to the reference prescription a is a heart-qi deficiency syndrome, the syndrome type corresponding to the reference prescription B is a heart-blood deficiency syndrome, and the syndrome type corresponding to the reference prescription C is a lung-qi deficiency syndrome. As can be seen, the syndrome types corresponding to the 3 reference prescriptions with the highest similarity include heart qi deficiency syndrome, heart blood deficiency syndrome and lung qi deficiency syndrome, which includes the syndrome type "heart blood deficiency syndrome" corresponding to the self-prepared prescription L, and therefore, it can be determined that the self-prepared prescription L passes the verification.
For example, N ═ 4, the syndrome type corresponding to the self-prepared prescription K is lung yin deficiency syndrome, and the 4 reference prescriptions determined from the prescription library and having the highest similarity between the corresponding reference subject probability distribution and the target subject probability distribution are reference prescription a, reference prescription B, reference prescription C, and reference prescription D, respectively, wherein the syndrome type corresponding to the reference prescription a is heart qi deficiency syndrome, the syndrome type corresponding to the reference prescription B is heart blood deficiency syndrome, the syndrome type corresponding to the reference prescription C is lung qi deficiency syndrome, and the syndrome type corresponding to the reference prescription D is spleen yang deficiency syndrome. As can be seen, the syndrome types corresponding to the 4 reference prescriptions with the highest similarity include heart qi deficiency syndrome, heart blood deficiency syndrome, lung qi deficiency syndrome and spleen yang deficiency syndrome, and they do not include the syndrome type "lung yin deficiency syndrome" corresponding to the self-prepared prescription K, so that it can be determined that the self-prepared prescription K fails in verification.
Fig. 5 is a block diagram illustrating an information processing apparatus according to an example embodiment. As shown in fig. 5, theapparatus 500 includes:
theacquisition module 501 is used for acquiring a bag-of-words vector corresponding to a self-prepared prescription, wherein the self-prepared prescription comprises information of a plurality of Chinese herbal medicines;
a determiningmodule 502, configured to determine, according to the bag-of-words vector acquired by the acquiringmodule 501 and the probability of each Chinese herbal medicine appearing in the prescription library, a target theme probability distribution corresponding to the self-prepared prescription through a pre-trained theme model;
agenerating module 503, configured to generate verification result information for the self-prepared prescription according to the target subject probability distribution determined by the determiningmodule 502.
In the technical scheme, firstly, a bag-of-words vector corresponding to a self-prepared prescription is obtained, wherein the self-prepared prescription comprises information of a plurality of Chinese herbal medicines; then, according to the bag-of-words vector and the probability of each Chinese herbal medicine appearing in the prescription library, determining the target theme probability distribution corresponding to the self-prepared prescription through a pre-trained theme model; and finally, generating verification result information aiming at the self-prepared prescription according to the probability distribution of the target subject. Therefore, whether the medicine collocation of the self-prepared prescription is reasonable or not can be automatically verified, so that the Chinese herbal medicine is matched more safely in the traditional Chinese medicine. In addition, the target theme probability distribution corresponding to the self-prepared prescription is determined through the theme model, the self-prepared prescription can be more accurately described, and therefore the accuracy of the verification result information of the self-prepared prescription is improved.
Optionally, the determiningmodule 502 includes:
the input submodule is used for inputting the bag-of-words vector into a pre-trained topic model to obtain the probability distribution of the topic-Chinese herbal medicine corresponding to the self-prepared prescription;
and the first determining submodule is used for determining the target topic probability distribution corresponding to the self-prepared prescription according to the topic-Chinese herbal medicine probability distribution and the probability of each Chinese herbal medicine appearing in the prescription library.
Optionally, the first determining sub-module includes:
a second determining submodule for determining, for each of the Chinese herbal medicines, a probability distribution of the Chinese herbal medicine belonging to each topic based on the topic-Chinese herbal medicine probability distribution and a probability of occurrence of the Chinese herbal medicine in the prescription library;
and the third determining submodule is used for determining the sum of the probability distribution of each Chinese herbal medicine belonging to each topic as the target topic probability distribution corresponding to the self-prepared prescription.
Optionally, the second determining sub-module includes:
the first obtaining submodule is used for obtaining the edge probability distribution of the theme;
and the fourth determining submodule is used for determining the probability distribution of the Chinese herbal medicine belonging to each topic based on Bayes theorem according to the topic-Chinese herbal medicine probability distribution, the probability of the Chinese herbal medicine appearing in the prescription library and the marginal probability distribution of the topic.
Optionally, the first obtaining sub-module includes:
the second acquisition submodule is used for acquiring the prescription-subject probability distribution corresponding to each reference prescription in the prescription library;
and the fifth determining submodule is used for determining the edge probability distribution of the theme based on the Bayes theorem according to the prescription-theme probability distribution corresponding to each reference prescription and the probability of each reference prescription being adopted.
Optionally, thegenerating module 503 includes:
the third acquisition submodule is used for acquiring the reference theme probability distribution corresponding to each reference prescription in the prescription library;
a sixth determining submodule, configured to determine, from the prescription library, N reference prescriptions with a highest similarity between the corresponding reference topic probability distribution and the target topic probability distribution, where N is greater than 1;
the seventh determining submodule is used for determining that the self-prepared prescription passes the verification if the syndrome corresponding to the N reference prescriptions with the highest similarity contains the syndrome corresponding to the self-prepared prescription;
and the eighth determining submodule is used for determining that the self-prepared prescription fails to verify if the syndrome corresponding to the N reference prescriptions with the highest similarity does not contain the syndrome corresponding to the self-prepared prescription.
Optionally, the obtainingmodule 501 includes:
a fourth obtaining submodule for obtaining the weight of each Chinese herbal medicine in the pseudo-formula;
a ninth determining submodule, configured to determine, according to the weight of each Chinese herbal medicine, the number of occurrences of each Chinese herbal medicine in the bag-of-words vector corresponding to the self-prepared prescription;
a generation submodule, configured to generate, for each Chinese herbal medicine, an extended sequence corresponding to the Chinese herbal medicine according to the occurrence frequency of the Chinese herbal medicine, where each element in the extended sequence is the Chinese herbal medicine, and the length of the extended sequence is the occurrence frequency of the Chinese herbal medicine;
and the combination submodule is used for combining the expansion sequences corresponding to the Chinese herbal medicines according to a preset combination sequence to obtain the bag-of-word vector corresponding to the self-prepared prescription.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The present disclosure also provides a non-transitory computer-readable storage medium on which a computer program is stored, which program, when executed by a processor, implements the steps of the above-mentioned information processing method provided by the present disclosure.
Fig. 6 is a block diagram illustrating anelectronic device 600 in accordance with an example embodiment. As shown in fig. 6, theelectronic device 600 may include: aprocessor 601, amemory 602. Theelectronic device 600 may also include one or more of amultimedia component 603, an input/output (I/O)interface 604, and acommunications component 605.
Theprocessor 601 is configured to control the overall operation of theelectronic device 600, so as to complete all or part of the steps in the information processing method. Thememory 602 is used to store various types of data to support operation at theelectronic device 600, such as instructions for any application or method operating on theelectronic device 600 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and so forth. TheMemory 602 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. Themultimedia components 603 may include a screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in thememory 602 or transmitted through thecommunication component 605. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 604 provides an interface between theprocessor 601 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. Thecommunication component 605 is used for wired or wireless communication between theelectronic device 600 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, which is not limited herein. Thecorresponding communication component 605 may therefore include: Wi-Fi module, Bluetooth module, NFC module, etc.
In an exemplary embodiment, theelectronic Device 600 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for performing the above-mentioned information Processing method.
In another exemplary embodiment, there is also provided a computer-readable storage medium including program instructions which, when executed by a processor, implement the steps of the information processing method described above. For example, the computer readable storage medium may be thememory 602 including the program instructions that are executable by theprocessor 601 of theelectronic device 600 to perform the information processing method described above.
Fig. 7 is a block diagram illustrating anelectronic device 700 in accordance with an example embodiment. For example, theelectronic device 700 may be provided as a server. Referring to fig. 7, anelectronic device 700 includes aprocessor 722, which may be one or more in number, and amemory 732 for storing computer programs that are executable by theprocessor 722. The computer programs stored inmemory 732 may include one or more modules that each correspond to a set of instructions. Further, theprocessor 722 may be configured to execute the computer program to perform the information processing method described above.
Additionally, theelectronic device 700 may also include apower component 726 that may be configured to perform power management of theelectronic device 700 and acommunication component 750 that may be configured to enable communication, e.g., wired or wireless communication, of theelectronic device 700. Theelectronic device 700 may also include input/output (I/O) interfaces 758. Theelectronic device 700 may operate based on an operating system, such as Windows Server, stored in thememory 732TM，Mac OS XTM，UnixTM，LinuxTMAnd so on.
In another exemplary embodiment, there is also provided a computer-readable storage medium including program instructions which, when executed by a processor, implement the steps of the information processing method described above. For example, the non-transitory computer readable storage medium may be thememory 732 described above including program instructions that are executable by theprocessor 722 of theelectronic device 700 to perform the information processing method described above.
In another exemplary embodiment, a computer program product is also provided, which contains a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-mentioned information processing method when executed by the programmable apparatus.
The preferred embodiments of the present disclosure are described in detail above with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details in the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure as long as it does not depart from the gist of the present disclosure.
Claims (10)
1. An information processing method characterized by comprising:
obtaining a bag-of-words vector corresponding to a self-prepared prescription, wherein the self-prepared prescription comprises information of a plurality of Chinese herbal medicines;
determining target topic probability distribution corresponding to the self-prepared prescription through a pre-trained topic model according to the bag-of-words vector and the probability of each Chinese herbal medicine appearing in the prescription library;
and generating verification result information aiming at the self-prepared prescription according to the target theme probability distribution.
2. The method of claim 1, wherein determining the target topic probability distribution corresponding to the self-proposed formulation according to the bag-of-words vector and the probability of each herbal medicine appearing in the formulation library by a pre-trained topic model comprises:
inputting the bag-of-words vector into a pre-trained topic model to obtain the probability distribution of the topic-Chinese herbal medicine corresponding to the self-prepared prescription;
and determining the target topic probability distribution corresponding to the self-prepared prescription according to the topic-Chinese herbal medicine probability distribution and the probability of each Chinese herbal medicine appearing in the prescription library.
3. The method of claim 2, wherein determining the target topic probability distribution for the self-prepared formulation based on the topic-Chinese herbal probability distribution and the probability of each Chinese herbal occurrence in the library comprises:
for each Chinese herbal medicine, determining the probability distribution of the Chinese herbal medicine belonging to each topic according to the topic-Chinese herbal medicine probability distribution and the probability of the Chinese herbal medicine appearing in the prescription library;
and determining the sum of the probability distribution of each Chinese herbal medicine belonging to each topic as the target topic probability distribution corresponding to the self-prepared prescription.
4. The method of claim 3, wherein determining the probability distribution of the chinese herbal medicine belonging to each topic based on the topic-chinese herbal medicine probability distribution and the probability of the chinese herbal medicine appearing in the library comprises:
obtaining edge probability distribution of a theme;
and determining the probability distribution of the Chinese herbal medicine belonging to each topic based on Bayes theorem according to the topic-Chinese herbal medicine probability distribution, the probability of the Chinese herbal medicine appearing in the prescription library and the marginal probability distribution of the topic.
5. The method of claim 4, wherein obtaining the edge probability distribution of the topic comprises:
acquiring prescription-subject probability distribution corresponding to each reference prescription in the prescription library;
and determining the marginal probability distribution of the theme based on Bayes theorem according to the prescription-theme probability distribution corresponding to each reference prescription and the probability of each reference prescription being adopted.
6. The method according to any one of claims 1-5, wherein generating verification result information for the self-prepared prescription according to the target topic probability distribution comprises:
acquiring the reference theme probability distribution corresponding to each reference prescription in the prescription library;
determining N reference prescriptions with the highest similarity between the corresponding reference subject probability distribution and the target subject probability distribution from the prescription library, wherein N is greater than 1;
if the syndrome type corresponding to the N reference prescriptions with the highest similarity comprises the syndrome type corresponding to the self-prepared prescription, determining that the self-prepared prescription passes the verification;
and if the syndrome type corresponding to the N reference formulas with the highest similarity does not contain the syndrome type corresponding to the self-prepared formula, determining that the self-prepared formula fails to verify.
7. The method according to any one of claims 1-5, wherein the bag-of-words vectors obtained from the pseudonym formula include:
obtained from the weight of each herb in the quasi-formulation;
determining the occurrence frequency of each Chinese herbal medicine in the word bag vector corresponding to the self-prepared prescription according to the weight of each Chinese herbal medicine;
aiming at each Chinese herbal medicine, generating an extended sequence corresponding to the Chinese herbal medicine according to the occurrence frequency of the Chinese herbal medicine, wherein each element in the extended sequence is the Chinese herbal medicine, and the length of the extended sequence is the occurrence frequency of the Chinese herbal medicine;
and combining the extended sequences corresponding to the Chinese herbal medicines according to a preset combination sequence to obtain a bag-of-words vector corresponding to the self-prepared prescription.
8. An information processing apparatus characterized by comprising:
the acquisition module is used for acquiring the corresponding bag-of-words vector of the self-prepared prescription, wherein the self-prepared prescription comprises a plurality of Chinese herbal medicine information;
the determining module is used for determining target theme probability distribution corresponding to the self-prepared prescription through a pre-trained theme model according to the bag-of-word vector acquired by the acquiring module and the probability of each Chinese herbal medicine in the prescription library;
and the generating module is used for generating verification result information aiming at the self-prepared prescription according to the target subject probability distribution determined by the determining module.
9. A non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 7.
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