CN115662656A - Method and system for evaluating side effects of drugs and electronic equipment - Google Patents
Method and system for evaluating side effects of drugs and electronic equipment Download PDFInfo
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Abstract
The application provides a method, a system and electronic equipment for evaluating side effects of a medicine, relates to the technical field of information, and comprises the steps of constructing a side effect index model; acquiring gene detection data of a patient and the name of a drug to be detected; substituting gene detection data and the name of the drug to be detected into the side effect index model to obtain a side effect evaluation index of the drug to be detected; all side effect evaluation indexes related to the patient are gathered, quantitative analysis is carried out on different medicines of the same disease, a doctor can visually know the risk condition of the medicines conveniently, and the doctor is assisted in selecting specific medicines.
Description
Technical Field
The invention relates to the technical field of information, in particular to a method and a system for evaluating side effects of a medicament and electronic equipment.
Background
At present, the evaluation of the side effect of the medicine based on the pharmacogenomics is generally carried out according to the detection data of a preset gene locus and based on a preset judgment logic, corresponding genotype data are judged and classified manually, only part of potential adverse reactions and side effect types of the medicine are given, the evaluation and comparison of the side effect risk among different medicines are lacked, the overall evaluation of multiple side effect risks of a single medicine cannot be carried out, the quantitative analysis of different medicines cannot be carried out, and the help of the evaluation on the treatment scheme formulated by a doctor is limited.
Therefore, a method, a system and an electronic device for evaluating the side effect of the drug are provided.
Disclosure of Invention
The specification provides a method, a system and an electronic device for evaluating the side effect of a drug, wherein the side effect evaluation index of the drug to be tested is obtained by substituting gene detection data and the name of the drug to be tested into the side effect index model, and the specific drug is selected in an auxiliary manner.
The method for evaluating the side effect of the medicine adopts the following technical scheme that the method comprises the following steps:
constructing a side effect index model;
acquiring gene detection data of a patient and the name of a drug to be detected;
substituting gene detection data and the name of the drug to be detected into the side effect index model to obtain a side effect evaluation index of the drug to be detected;
and summarizing all side effect evaluation indexes related to the patients, and assisting in evaluating potential medication risks.
Optionally, substituting the gene detection data and the information of the drug to be detected into the side effect index model to obtain the side effect evaluation index of the drug to be detected, including:
calling a plurality of scoring parameters of the drug to be detected according to the gene detection data and the information of the drug to be detected;
and according to the plurality of scoring parameters, scoring the side effect of the drug to be detected to obtain a side effect evaluation index.
Optionally, the scoring the side effect of the drug to be tested according to the plurality of scoring parameters to obtain a side effect evaluation index includes:
the scoring parameters comprise one or more of adverse reaction weight alpha, adverse reaction rating beta and gene action position gamma;
and calculating the side effect evaluation Index of the drug to be tested according to Index = F (alpha, beta, gamma).
Optionally, the constructing of the side effect index model includes:
collecting follow-up data of the side effect of the drug;
calculating the incidence of the side effects and the severity of the side effects according to the occurrence of each side effect of the medicine in the follow-up data;
obtaining the adverse reaction weight alpha of the medicine by combining the expert comment result;
and establishing an incidence relation between the medicament and the adverse reaction weight alpha.
Optionally, the constructing of the side effect index model includes:
collecting and extracting the names of the side effects and corresponding side effect scores according to the clinical feedback data of the historical patients;
collecting gene testing data of the historical patients, and determining adverse reaction grading beta by combining the side effect grading and the gene testing data of the historical patients;
and establishing a correlation relationship between the side effect and the adverse reaction grade beta.
Optionally, the constructing of the side effect index model includes:
according to a pharmacogenomic document, matching a first gene locus related to the influence of a drug, and determining the gene action position gamma of a first genotype, wherein the first genotype is the genotype at the first gene locus;
and establishing the association relationship between the first genotype and the action position gamma of the gene.
Optionally, the constructing a side effect index model further includes:
constructing a plurality of basic classifiers;
training the voting weight of each basic classifier according to the scoring parameters;
integration using weighted average for each base classifier
Wherein, w i Is the weight of the base classifier, and
the evaluation system for the side effect of the medicine provided by the application adopts the following technical scheme that the evaluation system comprises the following components:
the construction module is used for constructing a side effect index model;
the acquisition module is used for acquiring gene detection data of a patient and the name of a drug to be detected;
the evaluation module is used for substituting gene detection data and the name of the drug to be detected into the side effect index model to obtain a side effect evaluation index of the drug to be detected;
and the summarizing module is used for summarizing all side effect evaluation indexes related to the patients and assisting in evaluating the potential medication risk.
Optionally, the evaluation module includes:
the calling submodule is used for calling a plurality of scoring parameters of the to-be-detected medicine according to the gene detection data and the to-be-detected medicine information;
and the scoring submodule is used for scoring the side effect of the to-be-tested medicine according to the plurality of scoring parameters to obtain a side effect evaluation index.
Optionally, the scoring submodule includes:
the scoring parameters comprise one or more of adverse reaction weight alpha, adverse reaction rating beta and gene action position gamma;
and the evaluation unit is used for calculating the side effect evaluation Index of the drug to be tested according to Index = F (alpha, beta, gamma).
Optionally, the building module includes:
the first collecting submodule is used for collecting follow-up data of the side effect of the medicine;
the calculation submodule is used for calculating the incidence rate and the severity of the side effect according to the occurrence condition of each side effect of the medicaments in the follow-up data;
the weight determination submodule is used for obtaining the adverse reaction weight alpha of the medicine by combining the expert comment result;
and the first correlation submodule is used for establishing a correlation between the medicament and the adverse reaction weight alpha.
Optionally, the building module includes:
the second collecting submodule is used for collecting and extracting the names of the side effects and corresponding side effect scores according to the clinical feedback data of the historical patients;
a grade determination submodule for collecting gene detection data of the historic patients and determining an adverse reaction grade beta by combining the side effect score and the gene detection data of the historic patients;
and the second correlation submodule is used for establishing a correlation between the side effect and the adverse reaction grade beta.
Optionally, the building module includes:
the matching submodule is used for matching a first gene locus related to drug influence according to pharmacogenomics documents and determining the gene action position gamma of a first genotype, wherein the first genotype is the genotype on the first gene locus;
and the third correlation submodule is used for establishing the correlation between the first genotype and the gene action position gamma.
Optionally, the building module includes:
a classifier construction submodule for constructing a plurality of basic classifiers;
the voting weight training submodule is used for training the voting weight of each basic classifier according to the scoring parameters;
an integration calculation submodule for integrating each basic classifier by using a weighted average method
Wherein, w i Is the weight of the base classifier, and
the present specification also provides an electronic device, wherein the electronic device includes:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform any of the methods described above.
The present specification also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the above methods.
In the application, a side effect index model is constructed; acquiring gene detection data of a patient and the name of a drug to be detected; substituting gene detection data and the name of the drug to be detected into the side effect index model to obtain a side effect evaluation index of the drug to be detected; all side effect evaluation indexes related to the patient are gathered, quantitative analysis is carried out on different medicines of the same disease, doctors (professionals) can visually know the risk condition of the medicines conveniently, and the doctors (professionals) are assisted to select specific medicines.
Drawings
FIG. 1 is a schematic diagram illustrating the principle of a method for evaluating the side effects of a drug according to the embodiments of the present disclosure;
FIG. 2 is a schematic flow chart of a method for evaluating the side effect of a drug according to the embodiments of the present disclosure;
FIG. 3 is a schematic structural diagram of a system for evaluating side effects of a drug according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure;
fig. 5 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
Detailed Description
The following description is provided to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art. The basic principles of the invention, as defined in the following description, may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
The described features, structures, characteristics, or other details of the present invention are provided to enable those skilled in the art to fully understand the embodiments in the present specification. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flowcharts shown in the figures are illustrative only and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
The term "side effect" and "adverse reaction" as used herein mean a pharmacological effect other than the therapeutic purpose after application of a therapeutic amount of a drug, and the terms "side effect" and "adverse reaction" are equivalent to each other in the present specification.
Fig. 1 is a schematic diagram of a method for evaluating side effects of a drug according to an embodiment of the present disclosure, the method including:
s1, constructing a side effect index model;
s2, acquiring gene detection data of a patient and the name of a drug to be detected;
s3, substituting gene detection data and the name of the drug to be detected into the side effect index model to obtain a side effect evaluation index of the drug to be detected;
and S4, summarizing all side effect evaluation indexes related to the patient, and assisting in evaluating potential medication risks.
The existing evaluation method of the side effect of the medicine only has qualitative description aiming at the medicine generally, for example, aiming at the high/low risk of the specific side effect, the evaluation method is not only lack of the overall risk evaluation of the medicine, but also cannot carry out the transverse comparison of the side effect risk of various different medicines quantitatively, the side effect influence of the medicine mainly depends on the subjective evaluation of doctors, a unified standard does not exist, and the side effect influence of the medicine needs to be judged for a long time. Therefore, in order to meet the market demand of precise medical treatment and reduce the time for a doctor (professional) to evaluate the side effect of the medicine, the method for evaluating the side effect of the medicine is provided, which integrates clinical practice and doctor opinions and embodies the evaluation commonality of the doctor; based on the gene detection data of the drug to be detected and the patient, the side effect of the drug to be detected is qualitatively and quantitatively analyzed through the side effect index model, so that the side effect index of each drug to be detected is obtained, and the medication reference can be provided with high efficiency, high accuracy and low cost. Specifically, as shown in fig. 2, the method includes:
s1, constructing a side effect index model;
in the present application, as shown in fig. 2, the constructing of the side effect index model specifically includes:
s11, determining a grading parameter;
in one embodiment of the present specification, the scoring parameters include key parameters including one or more of adverse reaction weight α, adverse reaction rating β, and gene action position γ, and general parameters.
Specifically, S111 obtains an adverse reaction weight α, including:
collecting follow-up data of the side effect of the drug;
the follow-up data comprises the patient number, the name of the medicine taken by the patient and the specific side effect (adverse reaction) generated by the patient, wherein the patient number is classified according to different corresponding symptoms, and the patient number of each patient is unique.
A drug library is constructed that includes the names of the drugs that are currently published, as well as a unique drug number for each drug.
In one embodiment of the present specification, the follow-up data pattern is shown in Table 1:
(Table 1)
In table 1, no adverse reaction/adverse reaction is indicated by 0/1, that is, the no adverse reaction mark is 0, and the adverse reaction mark is 1, which is convenient for data recording and later calculation of the occurrence frequency and the occurrence rate of the side effect of the drug.
"adverse reaction 1", "adverse reaction 2" and the like are specific side effects (adverse reactions), such as vomiting, hypodynamia, lethargy and the like.
Calculating the incidence rate and the severity of the side effect according to the occurrence condition of each side effect of the medicine in the follow-up data, and obtaining the adverse reaction weight alpha of the medicine by combining the result of expert review;
in one embodiment of the present description, the expert review results are shown in table 2:
(Table 2)
And (3) the expert scores the adverse reaction occurrence probability level by 1-10 offline, wherein the higher the score is, the higher the adverse reaction occurrence probability is, namely 1 is the lowest occurrence probability, and 10 represents the highest occurrence probability.
The overall severity of the adverse reaction is scored by 1-10 under the expert line, the higher the score is, the higher the overall severity of the adverse reaction is, namely 1 is the slightest adverse reaction, and 10 represents the most severe adverse reaction.
In an embodiment of the present specification, based on a drug database and follow-up data of drug side effects, the types, severity, occurrence probability, and the like of side effects of different drugs in actual clinical use are sorted, the drugs are classified according to the comprehensive conditions of the side effects, the adverse reaction weight α corresponding to each drug is comprehensively calculated according to the expert review result, and the association relationship between the drug and the adverse reaction weight α is established, that is, the adverse reaction weight α corresponding to the drug can be found later by the drug name or the internal drug number.
S112, obtaining an adverse reaction rating β comprising:
collecting and extracting the names of the side effects and corresponding side effect scores according to the clinical feedback data of the historical patients;
in one embodiment of the present description, the clinical feedback data is shown in table 3:
(Table 3)
The clinical feedback data is the physician (professional) independent score, the higher the score, the more serious the side effect (adverse reaction), the higher the risk of the side effect (adverse reaction), and the higher the degree of attention received when specifying the clinical protocol. The severity is graded into a plurality of grades according to actual conditions, and the grades can be graded according to first-grade and second-grade \8230 \ 8230, equi-numeric words, and also graded according to mild, medium and severe \8230 \ 8230, equi-numeric words.
In one embodiment of the present description, the severity rating is divided into three levels, the severity rating comprising: mild, moderate and severe, scored on a percent scale, with severity grade 1-19 being mild, severity grade 20-49 being moderate and severity grade 50-100 being severe. In another embodiment of the present description, the severity rating is divided into four levels, the severity rating comprising: the first-level, the second-level, the third-level and the fourth-level are graded by adopting a percentile system, wherein the severity grade corresponding to 0-25 is the first-level, the severity grade corresponding to 26-50 is the second-level, the severity grade corresponding to 51-75 is the first-level, and the severity grade corresponding to 76-100 is the fourth-level.
Collecting gene detection data of the historical patients, and determining an adverse reaction rating beta by combining the side effect score and the gene detection data of the historical patients;
in one embodiment of the present specification, the pattern of the gene testing data for the historic patients is shown in table 4:
(Table 4)
Wherein the historical patients are patients who have previously been diagnosed and have received medication.
In one embodiment of the present specification, based on clinical feedback data of a doctor (professional) and a gene detection database of a historical patient, side effects related to pharmacogenomics are ranked or scored from the perspective of the doctor and the patient, and by combining expert opinions, severity ratings β of various adverse reactions are obtained comprehensively, and an association relationship between the side effects and the adverse reaction ratings β is established.
In one embodiment of the present description, to ensure that the data are consistent with clinical practice and medication-related experience, the data of tables 3 and 4 are subjected to comprehensive weighted calculation, and cross-validation and error calibration are performed in combination with the adverse reaction weight α and the adverse reaction rating β.
S113 obtaining a gene action position gamma, comprising:
according to pharmacogenomic literature, matching a first gene locus related to the side effect influence of the drug, and determining the gene action position gamma of a first genotype by combining gene detection data of historical patients using the same drug, wherein the first genotype is the genotype on the first gene locus;
specifically, according to the pharmacogenomic literature, the name of a first gene and/or the name of a first gene locus related to a drug is determined, and the influence of the gene corresponding to the first gene and/or the first gene locus on the side effect of the drug is weighted.
Table 5 is a gene status literature questionnaire based on pharmacogenomic literature collation:
(Table 5)
Wherein the first genetic locus is a genetic locus for which there is sufficient clinical evidence to indicate the presence of a correlation with a particular drug/disease, based on pharmacogenomics. In one embodiment of the present specification, the number of the first loci is one or more, and the genetic action position γ is calculated by linear weighted integration according to the influence weight of each first locus and the influence weight of the corresponding first genotype of each locus.
In an embodiment of the present disclosure, the pharmacogenomic document includes drug side effects such as drug specifications, experimental records, sider database, published papers, and the like, and on the basis of related genes/loci corresponding to pharmacogenomics involved in S112, the influence of each first locus on the side effects of each type of drug is determined through the pharmacogenomic document, and the influence of a specific first genotype on the first locus on the side effects of each type of drug is obtained to obtain a genetic action position γ, so as to establish an association relationship between the first genotype and the genetic action position γ.
It should be noted that, for the same drug, different genotypes at the same locus may have the same magnitude of side effect influence on the drug; the same genotype at different loci may have different side effects on the drug, and thus the site of action γ is related to the particular genotype at the locus.
In the construction process of the model, adverse reaction weight alpha, adverse reaction rating beta and gene action position gamma enable the association of treatment medication and gene detection data of patients with potential side effect risks.
S12, training the side effect index model.
Specifically, a plurality of basic classifiers are constructed by machine learning methods such as ensemble learning;
each basic classifier is independently learned, and different learning modeling methods are used, such as learning through linear regression, random forests, SVM (support vector machine) and the like;
training the voting weight of each basic classifier according to the obtained scoring parameters;
integration using weighted average for each base classifier
Wherein, w i Is the weight of the base classifier, and
in one embodiment of the present specification, a portion of the clinical feedback data is used as a test set to verify the accuracy and validity of the training. Preferably, the side effect evaluation index obtained by calculation can be audited by off-line experts to assist in verifying the accuracy and reliability of the side effect evaluation index.
In order to improve the accuracy of the side effect evaluation index and enable the side effect evaluation index to accord with the latest clinical medical treatment, experiment and scientific research results, the clinical data and related literature data are regularly updated so as to update scoring parameters such as adverse reaction weight alpha, adverse reaction rating beta and gene action status gamma, and model iteration and optimization are carried out. Because the model only needs to apply the relevant machine learning algorithm for retraining, and does not need to manually modify partial conclusions and add or delete the existing logics and architectures, the iterative optimization space is large, and the maintenance cost is effectively saved.
S2, acquiring gene detection data of a patient and the name of a drug to be detected;
the gene detection data comprises gene loci and genotypes, and the gene loci correspond to the genotypes one by one.
In order to improve the data security, the gene detection data of the patient is stored in a database system passing the national information security level protection third-level authentication.
Because the gene detection data directly influences the evaluation result in the later period, after the gene detection data is extracted, quality control treatment is carried out to ensure the integrity and the compliance of the gene locus and the genotype data, and each gene locus accords with the preset effective data and can be analyzed and read in the downstream, thereby improving the accuracy of the evaluation index of the side effect.
The drug to be tested is a potentially compliant drug (possibly drug) corresponding to the patient's disease. In one embodiment of the present specification, the drug to be tested may be a drug directly determined by a doctor (professional) according to the condition of the patient.
In another embodiment of the present specification, basic information of the patient is acquired, the basic information including the etiology of the patient, and all drugs for treating the disease are screened in the drug database according to the etiology as drugs to be tested.
S3, substituting the gene detection data and the name of the drug to be detected into the side effect index model to obtain a side effect evaluation index of the drug to be detected;
specifically, S31 calls a plurality of scoring parameters of the drug to be detected according to the gene detection data and the information of the drug to be detected;
in one embodiment of the present specification, according to the association relationship between the drug and the adverse reaction weight α, the adverse reaction weight α corresponding to the drug is searched for through the information of the drug to be tested of the patient, and each drug to be tested corresponds to one adverse reaction weight α;
according to the incidence relation between the side effect and the adverse reaction grade beta, searching the adverse reaction grade beta corresponding to the side effect through the gene detection data of the patient;
according to the incidence relation between the first gene locus and the gene action locus gamma, the corresponding first gene locus is searched through the information of the drug to be detected of the patient, and the corresponding gene action locus gamma is determined according to the first genotype corresponding to the first gene locus in the gene detection data of the patient.
After obtaining the adverse reaction weight alpha, the adverse reaction rating beta and the gene action position gamma corresponding to the patient and the drug to be tested, S32 scores the side effect of the drug to be tested according to a plurality of the rating parameters to obtain a side effect evaluation index.
In one embodiment of the present specification, the side effect evaluation Index of the test drug is calculated from the side effect evaluation Index = F (α, β, γ). Where F (α, β, γ) is a function determined manually. In one embodiment of the present specification, the side effect evaluation Index is in percent, and the higher the score, the smaller the combined side effect is.
The side effect evaluation index of the drug to be tested can be obtained through the side effect index model, and the time and labor cost of doctors (professionals) are saved.
In one embodiment of the present specification, by using the side effect index model, according to the information of the drug to be tested, the side effect with high risk related to the drug to be tested can be output to assist the decision of the doctor (professional).
And S4, summarizing all side effect evaluation indexes related to the patient, and assisting in evaluating potential medication risks.
Aiming at the same patient, one drug to be tested corresponds to one side effect evaluation index, namely n drugs to be tested correspond to n side effect evaluation indexes (n is more than or equal to 1). In order to realize the transverse comparison of different medicines aiming at the same disease, the intuitive medication risks of various potential symptomatic treatment medicines based on the pharmacogenomics related theory and practice are provided, and n side effect evaluation indexes related to the patient are summarized.
Since the higher the side effect evaluation index of a plurality of drugs to be tested for the same disease, the lower the side effect of the corresponding drug to be tested, the n drugs to be tested are ranked from high to low based on the n side effect evaluation indexes in order to facilitate rapid selection of the drug adapted to the patient.
When a doctor (professional) prescribes a prescription, not only the side effect of a drug but also the conditions of drug metabolism, drug response and the like need to be considered, so that the drug with the highest side effect evaluation index is not necessarily the most suitable drug for the patient; however, drugs with low side effect evaluation indexes are often unsuitable for the patient, and preferably, the drugs to be detected are classified according to the side effect evaluation indexes, so that decision interference caused by index values is reduced.
Specifically, the side effect evaluation grades are divided according to the intervals to which the side effect evaluation indexes belong, and then the drugs to be tested are classified based on the side effect evaluation grades. The side effect evaluation grade and the corresponding side effect evaluation index belonging interval can be configured according to the actual situation.
In one embodiment of the specification, the side effect evaluation scale includes: "recommended regular medication", "use after doctor's assessment", "cautious use or change of other medicine" and "not recommended use, change of other medicine" specifically:
the evaluation index interval of the side effect corresponding to the 'recommended conventional medication' is 76-100;
the corresponding side effect evaluation index interval of 'used after doctor evaluation' is 51-75;
the interval of the evaluation index of the side effect corresponding to the 'cautious use or replacement of other medicines' is 26-50;
the interval of the evaluation index of the side effect corresponding to 'not recommending to use and recommending to replace other medicines' is 0-25.
Through classifying the drugs to be tested, on one hand, a quantitative reference index can be provided for a doctor to simply and directly recognize the side effect risks of various drugs, and on the other hand, a personalized accurate drug use risk guidance instruction can be provided for a patient according to the detection result of the pharmacogenomics.
Moreover, the side effect index model in the application not only has qualitative description aiming at the high/low level of the risk of the specific side effect, but also comprises the overall side effect evaluation index of the medicine, so that the side effect risks of different medicines can be quantitatively and transversely compared, errors possibly existing in subjective analysis are reduced, and the repeatability and the reliability are improved.
In order to facilitate a doctor (professional) to visually check a data result, a potential medication risk evaluation table of a patient is generated according to the name of a drug to be tested, high-risk side effects, a side effect evaluation index and a side effect evaluation grade, as shown in table 6:
(Table 6)
Fig. 3 is a schematic structural diagram of a system for evaluating side effects of a drug, provided by an embodiment of the present disclosure, the system including:
a construction module 301 for constructing a side effect index model;
an obtaining module 302, configured to obtain gene detection data of a patient and a name of a drug to be detected;
the evaluation module 303 is configured to substitute the gene detection data and the name of the drug to be detected into the side effect index model to obtain a side effect evaluation index of the drug to be detected;
and an aggregation module 304 for aggregating all the side effect evaluation indexes related to the patients to assist in evaluating the potential medication risk.
Optionally, the evaluation module 303 includes:
the calling submodule is used for calling a plurality of scoring parameters of the to-be-tested medicine according to the gene detection data and the to-be-tested medicine information;
and the scoring submodule is used for scoring the side effect of the to-be-detected medicine according to the plurality of scoring parameters to obtain a side effect evaluation index.
Optionally, the scoring submodule includes:
the scoring parameters comprise one or more of adverse reaction weight alpha, adverse reaction rating beta and gene action position gamma;
and the evaluation unit is used for calculating the side effect evaluation Index of the drug to be tested according to Index = F (alpha, beta, gamma).
Optionally, the building module 301 includes:
the first collection submodule is used for collecting follow-up data of the side effect of the medicine;
the calculation submodule is used for calculating the incidence rate of the side effect and the severity of the side effect according to the occurrence condition of each side effect of the medicine in the follow-up data;
the weight determination submodule is used for obtaining the adverse reaction weight alpha of the medicine by combining the expert comment result;
the first correlation submodule is used for establishing a correlation between the medicament and the adverse reaction weight alpha.
Optionally, the building module 301 includes:
the second collecting submodule is used for collecting and extracting the names of the side effects and corresponding side effect scores according to the clinical feedback data of the historical patients;
a grade determination submodule for collecting gene detection data of the historic patients and determining an adverse reaction grade beta by combining the side effect score and the gene detection data of the historic patients;
and the second correlation submodule is used for establishing a correlation between the side effect and the adverse reaction grade beta.
Optionally, the building module 301 includes:
the matching submodule is used for matching a first gene locus related to drug influence according to pharmacogenomics documents and determining the gene action position gamma of a first genotype, wherein the first genotype is the genotype on the first gene locus;
and the third correlation module is used for establishing the correlation between the first genotype and the gene action position gamma.
Optionally, the building module 301 includes:
a classifier construction submodule for constructing a plurality of basic classifiers;
the voting weight training submodule is used for training the voting weight of each basic classifier according to the adverse reaction weight alpha, the adverse reaction rating beta and the gene action status gamma;
an integration calculation submodule for integrating each basic classifier by using a weighted average method
Wherein, w i Is the weight of the base classifier, and
the functions of the apparatus in the embodiment of the present invention have been described in the above method embodiments, so that reference may be made to the related descriptions in the foregoing embodiments for details that are not described in the present embodiment, and further details are not described herein.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for evaluating a side effect of a drug, comprising:
constructing a side effect index model;
acquiring gene detection data of a patient and the name of a drug to be detected;
substituting gene detection data and the name of the drug to be detected into the side effect index model to obtain a side effect evaluation index of the drug to be detected;
and summarizing all side effect evaluation indexes related to the patients, and assisting in evaluating potential medication risks.
2. The method of claim 1, wherein the step of substituting the gene detection data and the drug to be tested information into a side effect index model to obtain the side effect evaluation index of the drug to be tested comprises:
calling a plurality of scoring parameters of the drug to be detected according to the gene detection data and the information of the drug to be detected;
and according to the plurality of scoring parameters, scoring the side effect of the drug to be detected to obtain a side effect evaluation index.
3. The method of claim 2, wherein said scoring the side effects of the test drug according to a plurality of said scoring parameters to obtain a side effect evaluation index comprises:
the scoring parameters comprise one or more of adverse reaction weight alpha, adverse reaction rating beta and gene action position gamma;
and calculating the side effect evaluation Index of the drug to be tested according to Index = F (alpha, beta, gamma).
4. The method of claim 3, wherein constructing the side effect index model comprises:
collecting follow-up data of the side effect of the drug;
calculating the incidence of the side effects and the severity of the side effects according to the occurrence of each side effect of the medicine in the follow-up data;
combining with the expert comment result to obtain the adverse reaction weight alpha of the medicine;
and establishing the incidence relation between the medicament and the adverse reaction weight alpha.
5. The method of claim 3, wherein constructing the side effect index model comprises:
collecting and extracting the names of the side effects and corresponding side effect scores according to the clinical feedback data of the historical patients;
collecting gene detection data of the historical patients, and determining an adverse reaction rating beta by combining the side effect score and the gene detection data of the historical patients;
and establishing an incidence relation between the side effect and the adverse reaction rating beta.
6. The method of claim 3, wherein constructing the side effect index model comprises:
according to pharmacogenomic literature, matching a first gene locus related to drug influence, and determining the gene action position gamma of a first genotype, wherein the first genotype is the genotype on the first gene locus;
and establishing the association relationship between the first genotype and the action position gamma of the gene.
7. The method of claim 2, wherein constructing the side effect index model further comprises:
constructing a plurality of basic classifiers;
training the voting weight of each basic classifier according to the scoring parameters;
integration using weighted average for each base classifier
Wherein, w i Is the weight of the base classifier, and
8. a system for evaluating a side effect of a drug, comprising:
the construction module is used for constructing a side effect index model;
the acquisition module is used for acquiring gene detection data of a patient and the name of a drug to be detected;
the evaluation module is used for substituting the gene detection data and the name of the drug to be detected into the side effect index model to obtain the side effect evaluation index of the drug to be detected;
and the summarizing module is used for summarizing all side effect evaluation indexes related to the patients and assisting in evaluating the potential medication risk.
9. An electronic device, wherein the electronic device comprises:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-7.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-7.
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Denomination of invention: A method, system, and electronic device for evaluating drug side effects Granted publication date: 20231107 Pledgee: China Construction Bank Corporation Shanghai Xuhui sub branch Pledgor: SHANGHAI KANGLI MEDICAL LABORATORY Co.,Ltd. Registration number: Y2024310000231 |