CN115101192B - Symptom recommendation method, device, equipment and storage medium based on prescription - Google Patents
Symptom recommendation method, device, equipment and storage medium based on prescription Download PDFInfo
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Abstract
The application provides a symptom recommending method, a symptom recommending device, symptom recommending equipment and a symptom recommending storage medium based on a prescription, which can intelligently recommend symptoms to a patient user, so that the patient user can further perfect own uncomfortable information based on a recommending result, a doctor does not need to intervene for many times, and diagnosis and treatment efficiency is improved; in addition, as the target prescription is a prescription capable of treating the symptoms of the user, the recommended symptoms related to the input symptoms of the user can be determined according to the target prescription, namely, the symptoms conforming to the current physical discomfort condition of the user can be deduced for the user to check by himself, the recommended result is reliable, so that more comprehensive symptom information can be collected, and the accuracy of the diagnosis and treatment result can be improved.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a symptom recommendation method, device and equipment based on a prescription and a storage medium.
Background
The inquiry of the patient is an important task of the doctor, and the doctor can take the medicine according to the symptoms only if all the symptoms of the patient are accurately acquired. With the vigorous development of big data and artificial intelligence technology, a big data platform has been put into an actual operation stage. The method is a technical mode suitable for the current big data service, and is expected to provide a solution to the problem of the current inquiry of the traditional Chinese and Western medicine.
During the inquiry process, it is sometimes difficult for the patient to accurately describe his own symptoms, or some less-than-moderate symptoms may be missed. For example, when a patient initiates a request for a consultation through an online consultation platform, the patient may miss some less-inappropriate symptoms by emphasizing more-inappropriate symptoms, which may affect doctor diagnosis. If the doctor is actively inquiring, a great deal of medical time is occupied, and the doctor user and the patient user cannot have better online diagnosis and treatment experience. Therefore, it is necessary to intelligently recommend symptoms to the patient to prompt the patient for symptoms.
Disclosure of Invention
The embodiment of the application aims to provide a novel symptom recommendation method, device, equipment and storage medium based on a prescription, which are used for solving the problem that diagnosis and treatment efficiency is low because symptom recommendation cannot be intelligently performed on a user in the prior art.
The embodiment of the application provides a symptom recommendation method based on a prescription, which comprises the following steps:
acquiring user information; the user information includes user symptoms entered by a user;
determining a target prescription for treating the user's symptoms;
determining recommended symptoms according to the target prescription;
recommending the recommended symptoms to the user.
In the implementation process, symptom recommendation can be intelligently performed on the patient user, so that the patient user can further perfect own uncomfortable information based on the recommendation result, multiple times of intervention of doctors are not needed, and diagnosis and treatment efficiency is improved; in addition, as the target prescription is a prescription capable of treating the symptoms of the user, the recommended symptoms related to the input symptoms of the user can be determined according to the target prescription, namely, the symptoms conforming to the current physical discomfort condition of the user can be deduced for the user to check by himself, the recommended result is reliable, so that more comprehensive symptom information can be collected, and the accuracy of the diagnosis and treatment result can be improved.
Further, the determining a target prescription for treating the user's symptoms includes:
determining a target prescription for treating the user symptom based on the trained prescription recommendation model.
In the implementation process, the target prescription is determined according to the prescription recommendation model obtained through training, so that the reliability of the determined target prescription can be improved, and the determined target prescription can be ensured to treat the symptoms of the user.
Further, the prescription recommendation model is a linear model obtained by training based on a plurality of training sample data; each training sample data comprises information of a plurality of user symptoms and information of prescriptions for treating the user symptoms.
In the implementation process, the target prescription is determined through the linear model, the model training mode is simple, and the model output result is reliable.
Further, the linear model is: p (P) j ＝W j0 +W j1 S 1 +W j2 S 2 +W j3 S 3 +…+W jn S n ；
Wherein W is ji Representing the correlation coefficient between the jth preset prescription and the ith preset symptom, S i Indicating the ith preset symptom, P j An evaluation score representing the jth preset prescription, n representing the total number of the preset symptoms, m representing the total number of the preset prescriptions, W j0 Representing the preset intercept of a linear equation corresponding to the jth preset prescription, wherein the target prescription is a prescription in a prescription set formed by the preset prescriptions;
the determining a target prescription for treating the user symptoms according to the user symptoms and the prescription recommendation model obtained through training comprises the following steps:
inputting the user symptoms into the linear model to obtain evaluation scores corresponding to the preset prescriptions respectively;
and determining a target prescription from the prescription set according to the evaluation score.
In the implementation process, each preset prescription is used as a dependent variable of the linear model, each preset symptom is used as an independent variable of the linear model, the evaluation score corresponding to each preset prescription can be obtained according to the input user symptom, and the target prescription is determined from the preset prescriptions according to the evaluation score, namely, further screening can be performed according to the output result of the linear model, so that the reliability of the finally determined target prescription is improved.
Further, the determining recommended symptoms according to the target prescription includes:
determining a correlation coefficient between each target prescription and each preset symptom;
and determining recommended symptoms from a symptom set consisting of each preset symptom according to the association coefficient.
In the implementation process, the recommended symptoms are determined from the preset symptoms according to the association coefficient between the target prescription and each preset symptom, so that the determined recommended symptoms are guaranteed to be related to the target prescription as much as possible, and further, the recommended symptoms are guaranteed to be related to the user symptoms input by the user, so that the recommended symptoms are more in line with the current physical discomfort condition of the user, and the accuracy of symptom prompt is improved.
Further, the determining the association coefficient between each target prescription and each preset symptom includes:
and obtaining the association coefficient between each target prescription and each preset symptom from the linear model.
In the implementation process, the target prescription is determined based on the linear model, and then the recommended symptoms related to the target prescription are determined based on the association coefficient in the linear model; in addition, since the correlation coefficient can be directly obtained from the linear model, no other resource storage or calculation of the correlation coefficient is required.
Further, the determining, according to the association coefficient, a recommended symptom from a symptom set consisting of each of the preset symptoms includes:
aiming at each target prescription, according to the sequence from big to small of the association coefficient between the target prescription and each preset symptom, the symptoms meeting the preset quantity are screened out from the symptom set, and the recommended symptoms are determined from the screened symptoms.
In the implementation process, the recommended symptoms are selected from the selected symptoms, so that each determined recommended symptom has higher relevance with the corresponding target prescription, and therefore, the recommended symptoms also have higher relevance with the input user symptoms, and the reliability of the recommended result is further improved.
The embodiment of the application also provides a symptom recommendation device based on the prescription, which comprises:
the acquisition module is used for acquiring user information; the user information includes user symptoms entered by a user;
a first determination module for determining a target prescription for treating the user's symptoms;
the second determining module is used for determining recommended symptoms according to the target prescription;
and the recommending module is used for recommending the recommending symptoms to the user.
The embodiment of the application also provides electronic equipment, which comprises a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to realize any one of the methods.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by at least one processor to realize any one of the methods.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a prescription-based symptom recommendation method according to an embodiment of the application;
FIG. 2 is a diagram illustrating symptom recommendation for a user according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a symptom recommendation device based on a prescription according to a second embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the descriptions of "first," "second," etc. in the embodiments of the present application are for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present application.
In the description of the present application, it should be understood that the numerical references before the steps do not identify the order in which the steps are performed, but are merely used to facilitate description of the present application and to distinguish between each step, and thus should not be construed as limiting the present application.
Various embodiments are provided below to specifically describe a prescription-based symptom recommendation method, apparatus, device, and storage medium.
Embodiment one:
in order to solve the problem that in the prior art, the diagnosis accuracy is affected due to insufficient description of symptoms of a patient, the embodiment of the application provides a prescription-based symptom recommendation method, and the prescription-based symptom recommendation method provided by the embodiment of the application can be applied to electronic equipment, wherein the electronic equipment comprises, but is not limited to, a PC (Personal Computer, a personal computer), a mobile phone, a tablet computer, a notebook computer and the like.
Referring to fig. 1, the method provided by the embodiment of the application may include the following steps:
s101: acquiring user information; the user information includes user symptoms entered by the user.
The user in steps S101-S104 is typically a patient user, e.g. the patient himself, or a person familiar with the patient 'S condition, e.g. the patient' S family. The user can input user information through an application program on the electronic equipment according to the actual situation of the user. The user information here includes symptoms of the user, and may also include information that can assist the doctor in diagnosis and treatment, such as age, sex, and the like of the user.
S102: a target prescription is determined that can treat the user's symptoms.
In step S102, a target prescription for treating the symptoms of the user may be determined based on the trained prescription recommendation model.
The prescription recommendation model in the embodiment of the application is a model obtained by training based on a plurality of training sample data. Each training sample data may include user information and information of a prescription for treating a plurality of symptoms of the user in the user information, and the user information is input as a model, and the prescription information is output as a model. The prescription recommendation model can be a model which is obtained by training based on a nonlinear model such as a random forest model, an XGBoost model and the like or other linear models.
In a first alternative embodiment, the acquired user information is input into the prescription recommendation model, and the model can directly output a target prescription for treating the symptoms of the corresponding user aiming at the user information.
In a second alternative embodiment, the obtained user information is input into the prescription recommendation model, the model can output corresponding evaluation scores for each preset prescription, and at this time, the target prescription can be screened out from a prescription set formed by the preset prescriptions according to the evaluation scores respectively corresponding to the preset prescriptions.
The second alternative embodiment described above is described in detail below.
The linear model in this embodiment may be:
P j ＝W j0 +W j1 S 1 +W j2 S 2 +W j3 S 3 +…+W jn S n 。
wherein W is ji Representing the correlation coefficient between the jth preset prescription and the ith preset symptom, S i Represents the ithPreset symptoms, P j An evaluation score representing the jth preset prescription, i=1, 2 … n, j=1 … m, n representing the total number of preset symptoms, m representing the total number of preset prescriptions, W j0 The preset intercept of the linear equation corresponding to the jth preset prescription is represented, and the target prescription is a prescription in a prescription set formed by the preset prescriptions.
At this time, for step S102, the obtained user symptoms may be input into the linear model to obtain evaluation scores corresponding to the preset prescriptions, and then the target prescriptions may be determined from the prescription set according to the evaluation scores. According to the method and the device for evaluating the scores, the evaluation scores represent the relevance between the corresponding formulas and the user symptoms input by the user, so that a preset number of formulas can be screened out from a formula set to serve as target formulas according to the order of the evaluation scores from high to low, and for example, the formulas with the top five ranks can be screened out to serve as target formulas. And the prescriptions with the evaluation scores larger than or equal to a preset evaluation score threshold can be screened out and used as target prescriptions.
In the process of training the linear model, a loss function may be determined based on a least square method, where the loss function may be:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the true value, P, of the jth preset recipe j The linear regression value of the j-th preset prescription calculated by the model is represented, namely the evaluation score.
S103: recommended symptoms are determined based on the target prescription.
In a first alternative embodiment, in step S103, a correlation coefficient between each target prescription and each preset symptom may be determined, and then a recommended symptom is determined from a symptom set composed of each preset symptom according to the correlation coefficient.
In this embodiment, for each target prescription, the symptoms satisfying the preset number may be screened out from the symptom set according to the order of the correlation coefficient between each target prescription and each preset symptom from the large to the small, and the recommended symptom may be determined from the screened symptoms. Specifically, the selected symptoms may be filtered to obtain the final recommended symptoms. For example, it is possible to filter user symptoms that have been entered by the user, filter symptoms that are mutually exclusive to the user symptoms that have been entered by the user, filter symptoms that do not belong to the age group in which the user is located, filter symptoms that do not belong to the gender of the user, and filter positive symptoms that correspond to negative symptoms among the user symptoms that have been entered.
In this embodiment, corresponding weight values may be given to each target prescription according to the order of the evaluation scores from large to small, and the number of symptoms to be screened out for recommendation corresponding to different weight values is different, that is, the preset number values corresponding to different weight values are different. For example, for a target prescription with a first rank of evaluation scores, the correlation degree between the target prescription and user symptoms input by a user is high, and the corresponding preset number value can be larger than the corresponding preset number value of other target prescriptions.
In the first example of the first alternative embodiment, the correlation coefficient between each target prescription and each preset symptom may be directly obtained from the linear model. For example, the corresponding W can be obtained directly from the linear model ji As a correlation coefficient between the target prescription and the preset symptoms.
According to the scheme provided by the example, the linear model is used twice, the target prescription is determined based on the linear model, and then the recommended symptoms related to the target prescription are determined based on the association coefficient in the linear model; in addition, the association coefficient can be directly obtained from the linear model, so that other resource storage or calculation of the association coefficient is not required, and the resource consumption in the electronic equipment can be saved.
In the second example of the first alternative embodiment, the recommended symptoms corresponding to the target prescription may be determined according to the target prescription and the symptom recommendation model obtained through training. The symptom recommendation model is a model obtained by training based on a plurality of training sample data. Each training sample data may include a plurality of prescriptions and information about the symptoms for which the prescriptions are intended. The training sample data can be obtained from a medical case database, wherein the medical case database comprises a large number of medical case documents, and each medical case document is recorded with user symptoms and prescriptions for treating the user symptoms. When the model is trained, the prescription is taken as the model input, and the symptom is taken as the model output. The symptom recommendation model in this example may be a model obtained by training with different basic models, such as the above-mentioned model using a linear model to obtain a prescription recommendation model, where a nonlinear model may be used to train to obtain a symptom recommendation model, that is, a target prescription is obtained based on the prescription recommendation model, then a recommended symptom related to the target prescription is determined based on the symptom recommendation model, and a final recommended symptom is output through a diversified model, so that the obtained result is more accurate, reliable and comprehensive.
In a second alternative embodiment, in step S103, the target prescription may be used as a target prescription set, then, for each preset symptom, a correlation coefficient between the target prescription set and the target prescription set is determined, and then, a recommended symptom is determined from a symptom set consisting of each preset symptom according to the correlation coefficient. Specifically, the target prescription can be input into a symptom recommendation linear model obtained through training, wherein the symptom recommendation linear model is a model obtained through training based on the linear model. Inputting the target prescription into the symptom recommendation linear model to obtain evaluation scores corresponding to the preset symptoms respectively, taking the evaluation scores as association coefficients between the corresponding preset symptoms and the target prescription set, and determining the recommended symptoms from the symptom set formed by the preset symptoms according to the association coefficients. For example, symptoms meeting the preset number can be screened out according to the sequence of the association coefficient from high to low, and recommended symptoms can be determined from the screened symptoms.
S104: the recommended symptoms are recommended to the user.
After recommending the recommended symptom to the user, a new user symptom re-entered by the user based on the recommended symptom may be received, and then symptom information confirmed by the user is transmitted to another user. For example, the information may be pushed to a doctor by an application program, and the doctor makes a diagnosis and treatment based on the information input by the user.
It should be noted that for some doctors with less skilled business technology, such as a doctor in the practice phase, it may not be possible to guide the patient to describe all of his symptoms in his or her progress. Therefore, in step S104, the recommended symptoms may be recommended to the doctor user, and the doctor may inquire the patient according to the recommended symptoms, so that the diagnosis and treatment efficiency of the doctor may be improved.
After determining the recommended symptoms which need to be recommended to the user, the recommended symptoms can be randomly sampled, and the recommended symptoms are divided into two groups according to whether the sampled recommended symptoms are sign information or not. One set of labels is named "related signs" containing all of the symptoms of the recommended symptoms, and the other set of labels is named "related symptoms" containing all of the non-symptoms of the symptoms. When a recommendation is made to the user, the "relevant sign" and "relevant symptom" labels, and the symptoms they each contain, are deduced, and in particular, see fig. 2.
The physical sign symptoms in the embodiment of the application refer to symptoms which can be objectively detected by other people, are objectively existing symptoms and are not changed along with subjective consciousness of patients. The other person may be any person other than the patient, for example, a doctor, a patient's attendant, or the like. The examination herein refers to a means of examination by means of indirect interrogation such as by naked eyes or touch. For example, the developer can set a corresponding label for the preset symptoms, for example, can set a sign label for the symptoms related to the body type to indicate that the symptoms belong to sign symptoms, and after determining the recommended symptoms, can determine whether the corresponding recommended symptoms are sign symptoms or non-sign symptoms according to the label conditions corresponding to the recommended symptoms.
The symptom recommending method based on the prescription provided by the embodiment of the application can recommend the symptoms with high relevance to the recommending result of the prescription recommending model to the user, can guide the user to confirm whether the recommended symptoms exist or not, can promote the comprehensiveness of the collected symptoms, and can further promote the accuracy of the diagnosis and treatment result.
Embodiment two:
an embodiment of the present application provides a symptom recommendation device based on a prescription, please refer to fig. 3, including:
an acquisition module 301, configured to acquire user information; the user information includes user symptoms entered by the user.
A first determination module 302 is configured to determine a target prescription for treating the user's symptoms.
A second determining module 303, configured to determine recommended symptoms according to the target prescription.
And a recommendation module 304, configured to recommend the recommendation symptoms to the user.
In an exemplary embodiment, the prescription recommendation model is a linear model trained based on a plurality of training sample data; each training sample data comprises information of a plurality of user symptoms and information of prescriptions for treating the user symptoms.
In an exemplary embodiment, the linear model is:
P j ＝W j0 +W j1 S 1 +W j2 S 2 +W j3 S 3 +…+W jn S n ；
wherein W is ji Representing the correlation coefficient between the jth preset prescription and the ith preset symptom, S i Indicating the ith preset symptom, P j An evaluation score representing the jth preset prescription, i=1, 2 … n, j=1 … m, n representing the total number of preset symptoms, m representing the total number of preset prescriptions, W j0 The preset intercept of the linear equation corresponding to the jth preset prescription is represented, and the target prescription is a prescription in a prescription set formed by the preset prescriptions.
The first determining module 302 is configured to input the user symptoms into the linear model to obtain evaluation scores corresponding to each preset prescription; and determining the target prescription from the prescription set according to the evaluation score.
In an exemplary embodiment, the second determining module 303 is configured to determine a correlation coefficient between each target prescription and each preset symptom, and determine the recommended symptom from the symptom set consisting of each preset symptom according to the correlation coefficient.
In an exemplary embodiment, the second determining module 303 is configured to obtain, from the linear model, a correlation coefficient between each target prescription and each preset symptom.
In an exemplary embodiment, the second determining module 303 is configured to, for each target prescription, screen out symptoms satisfying a preset number from the symptom set according to the order of the correlation coefficient between the target prescription and each preset symptom from the large to the small, and determine the recommended symptom from the screened symptoms.
In an exemplary embodiment, the second determining module 303 is configured to take the target prescription as a target prescription set, then determine, for each preset symptom, a correlation coefficient between the target prescription set and the target prescription set, and then determine, according to the correlation coefficient, a recommended symptom from a symptom set composed of the preset symptoms.
It should be understood that, for simplicity of description, the descriptions in the first embodiment are omitted in this embodiment.
Embodiment III:
based on the same inventive concept, an embodiment of the present application provides an electronic device, please refer to fig. 4, which includes a processor 401 and a memory 402, wherein a computer program is stored in the memory 402, and the processor 401 executes the computer program to implement the steps of the method in the first embodiment, which is not described herein.
It will be appreciated that the configuration shown in fig. 4 is merely illustrative, and that the apparatus may also include more or fewer components than shown in fig. 4, or have a different configuration than shown in fig. 4.
The processor 401 may be an integrated circuit chip having signal processing capabilities. The processor 401 may be a general-purpose processor including a Central Processing Unit (CPU), a Network Processor (NP), etc.; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. Which may implement or perform the various methods, steps, and logical blocks disclosed in embodiments of the application.
Memory 402 may include, but is not limited to, random Access Memory (RAM), read Only Memory (ROM), programmable Read Only Memory (PROM), erasable read only memory (EPROM), electrically erasable read only memory (EEPROM), and the like.
The present embodiment also provides a computer readable storage medium, such as a floppy disk, an optical disk, a hard disk, a flash memory, a usb disk, a Secure Digital (SD) card, a multimedia (MMC) card, etc., in which one or more programs for implementing the above steps are stored, where the one or more programs may be executed by one or more processors, so as to implement the steps of the method in the above embodiments, which is not described herein again.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, or may be implemented by hardware, but in many cases the former is a preferred embodiment.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (5)
1. A prescription-based symptom recommendation method, comprising:
acquiring user information; the user information includes user symptoms entered by a user;
determining a target prescription for treating the symptoms of the user, comprising:
determining a target prescription for treating the user symptom based on the prescription recommendation model obtained through training; the prescription recommendation model is a linear model obtained by training based on a plurality of training sample data; each training sample data comprises information of a plurality of user symptoms and information of prescriptions for treating the user symptoms; the linear model is as follows: p (P) j ＝ j0 + j1 S 1 + j2 S 2 + j3 S 3 +…+W jn S n The method comprises the steps of carrying out a first treatment on the surface of the Wherein W is ji Representing the correlation coefficient between the jth preset prescription and the ith preset symptom, S i Indicating the ith preset symptom, P j An evaluation score representing the jth preset prescription, i=1, 2 … n, j=1 …, n representing the total number of preset symptoms, m representing the total number of preset prescriptions, W j0 Representing the preset intercept of a linear equation corresponding to the jth preset prescription, wherein the target prescription is a prescription in a prescription set formed by the preset prescriptions; the determining a target prescription for treating the user symptom based on the prescription recommendation model obtained through training comprises the following steps: inputting the user symptoms into the linear model to obtain evaluation scores corresponding to the preset prescriptions respectively; determining a target prescription from the prescription set according to the evaluation score;
determining recommended symptoms from the target prescription, comprising: determining a correlation coefficient between each target prescription and each preset symptom; determining recommended symptoms from a symptom set consisting of each preset symptom according to the association coefficient;
the determining the association coefficient between each target prescription and each preset symptom comprises the following steps:
obtaining association coefficients between each target prescription and each preset symptom from the linear model;
recommending the recommended symptoms to the user.
2. The prescription-based symptom recommendation method as claimed in claim 1, wherein said determining recommended symptoms from a symptom set consisting of each of said preset symptoms according to said association coefficient comprises:
aiming at each target prescription, according to the sequence from big to small of the association coefficient between the target prescription and each preset symptom, the symptoms meeting the preset quantity are screened out from the symptom set, and the recommended symptoms are determined from the screened symptoms.
3. A prescription-based symptom recommendation device, comprising:
the acquisition module is used for acquiring user information; the user information includes user symptoms entered by a user;
a first determination module for determining a target prescription for treating the user's symptoms; the method is particularly used for determining a target prescription for treating the user symptoms based on a prescription recommendation model obtained through training; the prescription recommendation model is a linear model obtained by training based on a plurality of training sample data; each training sample data comprises information of a plurality of user symptoms and information of prescriptions for treating the user symptoms; the linear model is as follows: p (P) j ＝ j0 + j1 S 1 + j2 S 2 + j3 S 3 +…+W jn S n The method comprises the steps of carrying out a first treatment on the surface of the Wherein W is ji Representing the correlation coefficient between the jth preset prescription and the ith preset symptom, S i Indicating the ith preset symptom, P j An evaluation score representing the jth preset prescription, i=1, 2 … n, j=1 …, n representing the total number of preset symptoms, m representing the total number of preset prescriptions, W j0 Representing the preset intercept of a linear equation corresponding to the jth preset prescription, wherein the target prescription is a prescription in a prescription set formed by the preset prescriptions; the method is particularly used for inputting the user symptoms into the linear model to obtain evaluation scores corresponding to the preset prescriptions respectively; determining a target prescription from the prescription set according to the evaluation score;
the second determining module is used for determining recommended symptoms according to the target prescription; the method is specifically used for determining the association coefficient between each target prescription and each preset symptom; determining recommended symptoms from a symptom set consisting of each preset symptom according to the association coefficient; the method is particularly used for acquiring the association coefficient between each target prescription and each preset symptom from the linear model;
and the recommending module is used for recommending the recommending symptoms to the user.
4. An electronic device comprising a processor and a memory, the memory having stored therein a computer program, the processor executing the computer program to implement the method of any of claims 1-2.
5. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by at least one processor, implements the method according to any of claims 1-2.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210710939.1A CN115101192B (en) | 2022-06-22 | 2022-06-22 | Symptom recommendation method, device, equipment and storage medium based on prescription |
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| CN202210710939.1A CN115101192B (en) | 2022-06-22 | 2022-06-22 | Symptom recommendation method, device, equipment and storage medium based on prescription |
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| CN115101192A CN115101192A (en) | 2022-09-23 |
| CN115101192B true CN115101192B (en) | 2023-09-01 |
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| CN112015917A (en) * | 2020-09-07 | 2020-12-01 | 平安科技（深圳）有限公司 | Data processing method and device based on knowledge graph and computer equipment |
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