A personalized drug treatment plan formulation system

Abstract

The present invention discloses a personalized drug treatment plan formulation system, which relates to the technical field of personalized treatment plan formulation. It includes a drug treatment data collection module, a drug efficacy evaluation module, a drug treatment plan formulation module, and a drug treatment plan output module. The drug treatment data collection module collects basic treatment data and gene detection data, and preprocesses the collected data. The drug efficacy evaluation module evaluates drug metabolism and drug efficacy based on the basic treatment data and gene detection data. The drug treatment plan output module formulates a personalized drug treatment plan table in combination with the output result of the drug treatment plan formulation module. The data collection technology, gene detection technology, neural network algorithm, linear regression algorithm, and convolutional neural network algorithm in the method of the present invention are closely combined with modern information technology, solving the problem that it is difficult for traditional technologies to formulate drug treatment plans personalized by combining multiple physiological factors.

Description

Technical Field

[0001] The present invention relates to the technical field of personalized treatment plan formulation, and particularly relates to a system for formulating a personalized drug treatment plan. Background Art

[0002] Traditional drug treatment follows a unified standard plan, formulating drug treatment plans based on common disease manifestations and past experience. However, this general approach has significant drawbacks. Since patients vary greatly in many physiological factors and have different reactions to the same drug, with the development of big data analysis and artificial intelligence algorithms, it has become a reality to efficiently process massive amounts of medical data. By integrating multi-dimensional data of patients and deeply mining and analyzing these multi-dimensional data, the efficacy and safety of drugs in different individuals can be accurately predicted. Nowadays, the requirement for precision in clinical treatment is continuously increasing, and the traditional model can no longer meet the needs. Against this background, a system for formulating a personalized drug treatment plan has emerged, providing strong support for realizing precision medicine.

[0003] Although the existing technology has made great progress in the direction of formulating drug treatment plans, there are still some problems to be optimized. In the process of formulating drug treatment plans, it is difficult for the existing technology to formulate drug treatment plans personalized by combining various factors, resulting in poor treatment effects of drug treatment plans. Therefore, how to combine basic treatment data and gene detection data to improve the precision of drug treatment plans is the problem we need to solve. For this reason, a system for formulating a personalized drug treatment plan is proposed herein. Summary of the Invention

[0004] To achieve the above objectives, the present invention is realized through the following technical solutions: A system for formulating a personalized drug treatment plan includes a drug treatment data collection module, a drug efficacy evaluation module, a drug treatment plan formulation module, and a drug treatment plan output module, wherein each module is communicatively connected.

[0005] The drug treatment data collection module collects basic treatment data and gene detection data, and preprocesses the collected data, providing data preparation for the realization of the functions of subsequent modules.

[0006] The drug efficacy evaluation module evaluates drug metabolism and drug efficacy based on the basic treatment data and gene detection data.

[0007] The drug treatment plan formulation module formulates a drug treatment plan based on the evaluation results of drug metabolism and drug efficacy.

[0008] The drug treatment plan output module formulates a personalized drug treatment plan table in combination with the output results of the drug treatment plan formulation module, solving the problem that in the process of formulating a drug treatment plan, it is difficult for the prior art to formulate a drug treatment plan personalized by combining multiple factors, resulting in poor treatment effects of the drug treatment plan.

[0009] A further improvement of the technical solution of the present invention lies in that: the drug efficacy evaluation module and the drug treatment plan formulation module specifically include:

[0010] The drug efficacy evaluation module is divided into a basic evaluation unit, a gene evaluation unit and a comprehensive efficacy evaluation unit. Among them, the basic evaluation unit obtains a drug basic metabolism index through basic treatment data; the gene evaluation unit obtains a drug gene metabolism index and a drug efficacy index respectively through gene detection data; the comprehensive efficacy evaluation unit combines the drug basic metabolism index, the drug gene metabolism index and the drug efficacy index to construct a drug treatment index evaluation model;

[0011] The drug treatment plan formulation module is divided into a drug dose allocation unit and a drug treatment plan formulation unit. Among them, the drug dose allocation unit obtains a drug dose allocation index by using the output result of the drug dose allocation model, and then constructs a drug dose allocation model; the drug treatment plan formulation unit combines the output result of the drug dose allocation model to obtain a drug treatment plan, and then constructs a drug treatment plan model.

[0012] A further improvement of the technical solution of the present invention lies in that: the drug treatment data collection module collects basic treatment data and gene detection data, and the process of preprocessing the collected data includes:

[0013] Deploy collection devices to collect basic treatment data and gene detection data. Among them, the collection devices include medical height and weight scales, gene sequencers, etc.;

[0014] The basic treatment data is the age, gender, height, weight and BMI value of the treatment object; the gene detection data is the metabolic enzyme gene and drug target gene of the treatment object;

[0015] Collect the age and gender of the treatment object through a questionnaire survey, collect the height, weight and BMI value of the treatment object by using a medical height and weight scale, and collect the metabolic enzyme gene and drug target gene of the treatment object by using a gene sequencer;

[0016] Clean and calibrate the data of the age, gender, height, weight and BMI value of the treated subject. Use the FastQC tool to evaluate the quality of the metabolic enzyme genes and drug target genes of the treated subject. Use the Cutadapt tool to remove the low-quality bases in the metabolic enzyme genes and drug target genes. Use the GATK BaseRecalibrator to correct the quality values of the bases in the metabolic enzyme genes and drug target genes.

[0017] A further improvement of the technical solution of the present invention lies in that: the process of the basic evaluation unit for obtaining the drug basic metabolism index includes:

[0018] When the age of the treated subject is between 0 and 18 years old, an age coefficient of 1 is assigned; when the age of the treated subject is between 18 and 44 years old, an age coefficient of 0.95 is assigned; when the age of the treated subject is between 45 and 64 years old, an age coefficient of 0.9 is assigned; when the age of the treated subject is greater than 65 years old, an age coefficient of 0.8 is assigned;

[0019] When the treated subject is male, a gender coefficient of 1 is assigned; when the treated subject is female, a gender coefficient of 0.9 is assigned;

[0020] When the BMI value of the treated subject is between 18.5 and 24.9, a BMI coefficient of 1 is assigned; when the BMI value of the treated subject is between 25 and 29.9, a BMI coefficient of 1.1 is assigned; when the BMI value of the treated subject is greater than 29.9, a BMI coefficient of 1.2 is assigned;

[0021] Use the age, height and weight of the treated subject to calculate the BMR value of the treated subject according to the Mifflin-St Jeor equation. Obtain the drug basic metabolism index by calculating the product of the BMR value of the treated subject, the age coefficient of the treated subject, the gender coefficient of the treated subject and the BMI coefficient of the treated subject.

[0022] A further improvement of the technical solution of the present invention lies in that: the process of the gene evaluation unit for obtaining the drug gene metabolism index and the drug efficacy index includes:

[0023] Refer to the PharmGKB database to screen out the metabolic enzyme genes related to drug metabolism. According to the drug action mechanism, screen out the drug target genes of the treated subject related to drug efficacy. Use a nucleic acid extractor and a magnetic bead kit, and adopt the magnetic bead method to extract the mRNA in the screened metabolic enzyme genes and drug target genes respectively;

[0024] By fluorescence quantitative PCR, detect the mRNA in the metabolic enzyme gene and the mRNA in the drug target gene, and record the cycle numbers corresponding to the inflection points where the fluorescence signals of the mRNA in the metabolic enzyme gene and the mRNA in the drug target gene enter the exponential growth phase from the background. These cycle numbers are the Ct values of the metabolic enzyme gene and the drug target gene respectively;

[0025] Extract the reference gene data related to the metabolic enzyme gene and the drug target gene from the ICG database. Using the extracted reference gene data, calculate the Ct value of the reference gene. According to the Ct value of the reference gene, calculate the ratio of the Ct value of the metabolic enzyme gene to the Ct value of the reference gene and the ratio of the Ct value of the drug target gene to the Ct value of the reference gene respectively, and obtain the relative expression levels of the metabolic enzyme gene and the drug target gene;

[0026] Set the metabolic baseline value. When the relative expression level of the metabolic enzyme gene is lower than 0.5 times the metabolic baseline value, the drug gene metabolism index is 0.8, indicating weak metabolism of the drug by the metabolic enzyme gene; when the relative expression level of the metabolic enzyme gene is between 0.5 times and 1.5 times the metabolic baseline value, the drug gene metabolism index is 1.5, indicating moderate metabolism of the drug by the metabolic enzyme gene; when the relative expression level of the metabolic enzyme gene is higher than 1.5 times the metabolic baseline value, the drug gene metabolism index is 2.5, indicating strong metabolism of the drug by the metabolic enzyme gene, and obtain the drug gene metabolism index;

[0027] Set the efficacy baseline value. When the relative expression level of the drug target gene is lower than 0.5 times the efficacy baseline value, the drug efficacy index is 1, indicating low efficacy of the drug against the drug target gene; when the relative expression level of the drug target gene is between 0.5 times and 2 times the efficacy baseline value, the drug efficacy index is 2, indicating general efficacy of the drug against the drug target gene; when the relative expression level of the drug target gene is higher than 2 times the efficacy baseline value, the drug efficacy index is 3, indicating high efficacy of the drug against the drug target gene, and obtain the drug efficacy index.

[0028] A further improvement of the technical solution of the present invention lies in: for the efficacy comprehensive evaluation unit, the construction process of the drug treatment index evaluation model includes:

[0029] According to the contributions of the drug basic metabolism index and the drug gene metabolism index to drug metabolism, assign weights to the drug basic metabolism index and the drug gene metabolism index respectively, and calculate the drug metabolism index by combining the weights. The calculation formula is , where U is the drug metabolism index, and are the weights of the drug basic metabolism index and the drug gene metabolism index respectively, and They are the drug basic metabolism index and the drug gene metabolism index respectively;

[0030] Take the basic treatment data, gene detection data, drug metabolism index and drug efficacy index as the first data set, and divide them into a training set and a test set according to the ratio of 7:3;

[0031] Combined with the training set data and the neural network algorithm, take the basic treatment data and gene detection data as inputs, take the drug metabolism index and drug efficacy index as outputs, learn the non-linear relationship between the basic treatment data, the metabolic enzyme genes of the treatment object and the drug metabolism index, and the non-linear relationship between the drug target genes and the drug efficacy index, and train the drug treatment index evaluation model;

[0032] Input the test set data into the drug treatment index evaluation model, use the MSE function to evaluate the error between the drug metabolism index output by the drug treatment index evaluation model and the actual drug metabolism index, and the error between the drug treatment index output by the drug treatment index evaluation model and the actual drug efficacy index. According to the evaluation results, adjust the parameters of the drug treatment index evaluation model, optimize the performance of the drug treatment index evaluation model, and deploy the optimized drug treatment index evaluation model into the system.

[0033] A further improvement of the technical solution of the present invention lies in: the process of the drug dose allocation unit obtaining the drug dose allocation index includes:

[0034] Input the basic treatment data and gene detection data into the drug treatment index evaluation model, and output the drug metabolism index through the drug treatment index evaluation model;

[0035] Take the drug metabolism index as the second data set, and divide it into a training set and a test set according to the ratio of 8:2;

[0036] Use the training set data and the linear regression algorithm, take the drug metabolism index as the input, take the drug dose allocation index as the output, adjust the intercept term and the regression coefficient of the drug metabolism index, learn the non-linear relationship between the drug metabolism index and the drug dose allocation index, and train the drug dose allocation model;

[0037] Input the test set data into the drug dose allocation model, evaluate the performance of the drug dose allocation model, adjust the intercept term and regression coefficient of the drug dose allocation model, optimize the drug dose allocation model, and deploy the optimized drug dose allocation model into the system. The expression of this drug dose allocation model is , where is the drug dose allocation index, is the intercept term, is the regression coefficient of the drug metabolism index, p is the drug metabolism index, is the error term.

[0038] A further improvement of the technical solution of the present invention lies in that: the process of the drug treatment plan formulation unit obtaining the drug treatment plan by combining the output result of the drug dosage allocation model includes:

[0039] Input the drug metabolism index into the drug dosage allocation model, and output the drug dosage allocation index through the drug dosage allocation model; input the gene detection data into the drug treatment index evaluation model, and output the drug efficacy index through the drug treatment index evaluation model;

[0040] When the drug dosage allocation index is between 0 and 3, allocate low-dose drugs to the treatment object; when the drug dosage allocation index is between 3 and 6, allocate conventional-dose drugs to the treatment object; when the drug dosage allocation index is between 6 and 10, allocate high-dose drugs to the treatment object;

[0041] When the drug efficacy index is between 0 and 2, it indicates that the corresponding drug has poor efficacy, and drug treatment should be replaced; when the drug efficacy index is between 2 and 6, it indicates that the corresponding drug has general efficacy, and combined treatment with similar drugs and other drugs should be carried out; when the drug efficacy index is between 6 and 10, it indicates that the corresponding drug has obvious efficacy, and continue to use similar drugs for treatment;

[0042] Combine the drug dosage allocation plan and the drug type allocation plan to obtain the drug treatment plan, and encode the drug treatment plan.

[0043] A further improvement of the technical solution of the present invention lies in that: the construction process of the drug treatment plan model of the drug treatment plan formulation unit includes:

[0044] Take the drug dosage allocation index, drug efficacy index, and drug treatment plan code as the third data set, and divide it into a training set and a test set according to a ratio of 8:2;

[0045] Using the training set data and the neural network algorithm, take the drug dosage allocation index and drug efficacy index as inputs, and the drug treatment plan code as the output, learn the non-linear relationship between the drug dosage allocation index, drug efficacy index, and drug treatment plan code, and train the drug treatment plan model;

[0046] Input the test set data into the drug treatment plan model, compare the actual drug treatment plan code with the drug treatment plan code output by the drug treatment plan model, evaluate the performance of the drug treatment plan model, use the Adam optimizer to update the parameters of the convolutional neural network model, optimize the drug treatment plan model, and deploy the optimized drug treatment plan model to the system.

[0047] A further improvement of the technical solution of the present invention lies in that: the process of formulating a personalized drug treatment plan table by the drug treatment plan formulation module in combination with the output result of the drug treatment plan formulation module includes:

[0048] Input the drug dose allocation index and the drug efficacy index into the drug treatment plan output model, output the drug treatment plan code through the drug treatment plan output model, match the corresponding drug treatment plan according to the drug treatment plan code, and formulate a personalized drug treatment plan table in combination with the drug dose allocation index and the drug efficacy index.

[0049] The beneficial effects of the present invention are as follows: In the present invention, a personalized drug treatment plan formulation system, compared with the traditional personalized drug treatment plan formulation system, the data acquisition technology, gene detection technology, neural network algorithm, linear regression algorithm and convolutional neural network algorithm in the method of the present invention are closely combined with modern information technology, accurately capturing basic treatment data and gene detection data, and then obtaining the drug dose allocation index and the drug efficacy index, achieving real-time and comprehensive monitoring of the individual differences of patients, solving the problem that it is difficult for traditional technologies to formulate drug treatment plans personalized by combining multiple physiological factors, ensuring that the system in the present invention can refine the dynamic monitoring standard for a personalized drug treatment plan formulation system within a more accurate range, making the monitored data more accurate indicators under the same conditions. The research and application of this method significantly enhance the degree of intelligence in the process of formulating personalized drug treatment plans. Description of the Drawings

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0051] Figure 1 It is a block diagram of a personalized drug treatment plan formulation system of the present invention. Detailed Embodiments

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0053] Such as Figure 1As shown in the figure, the present invention provides a personalized drug treatment plan formulation system, including a drug treatment data collection module, a drug efficacy evaluation module, a drug treatment plan formulation module, and a drug treatment plan output module. Among them, each module is communicatively connected;

[0054] The drug treatment data collection module collects basic treatment data and gene detection data, preprocesses the collected data, and provides data preparation for the implementation of the subsequent module functions;

[0055] The drug efficacy evaluation module evaluates drug metabolism and drug efficacy based on the basic treatment data and gene detection data;

[0056] The drug treatment plan formulation module formulates a drug treatment plan based on the evaluation results of drug metabolism and drug efficacy;

[0057] The drug treatment plan output module combines the output results of the drug treatment plan formulation module to formulate a personalized drug treatment plan table, solving the problem that in the process of formulating a drug treatment plan, it is difficult for the prior art to formulate a drug treatment plan personalized by combining multiple factors, resulting in poor treatment effects of the drug treatment plan.

[0058] The drug efficacy evaluation module and the drug treatment plan formulation module specifically include:

[0059] The drug efficacy evaluation module is divided into a basic evaluation unit, a gene evaluation unit, and an efficacy comprehensive evaluation unit. Among them, the basic evaluation unit obtains the basic drug metabolism index through the basic treatment data; the gene evaluation unit obtains the drug gene metabolism index and the drug efficacy index respectively through the gene detection data; the efficacy comprehensive evaluation unit combines the basic drug metabolism index, the drug gene metabolism index, and the drug efficacy index to construct a drug treatment index evaluation model;

[0060] The drug treatment plan formulation module is divided into a drug dose allocation unit and a drug treatment plan formulation unit. Among them, the drug dose allocation unit obtains the drug dose allocation index by using the output result of the drug dose allocation model, and then constructs a drug dose allocation model; the drug treatment plan formulation unit combines the output result of the drug dose allocation model to obtain a drug treatment plan, and then constructs a drug treatment plan model.

[0061] Preferably, the process of the drug treatment data collection module collecting basic treatment data and gene detection data and preprocessing the collected data includes:

[0062] Deploy collection devices to collect basic treatment data and gene detection data. Among them, the collection devices include medical height and weight scales, gene sequencers, etc.;

[0063] Among them, the basic treatment data are the age, gender, height, weight and BMI value of the treatment subject; the gene detection data are the metabolic enzyme genes and drug target genes of the treatment subject;

[0064] Through a questionnaire survey, the age and gender of the treatment subject are collected. Using a medical height and weight scale, the height, weight and BMI value of the treatment subject are collected. Through a gene sequencer, the metabolic enzyme genes and drug target genes of the treatment subject are collected;

[0065] Data cleaning and data calibration are performed on the collected age, gender, height, weight and BMI value of the treatment subject. Using the FastQC tool, quality assessment is performed on the metabolic enzyme genes and drug target genes of the treatment subject. Through the Cutadapt tool, low-quality bases in the metabolic enzyme genes and drug target genes are removed. Using the GATK BaseRecalibrator, the quality values of the bases in the metabolic enzyme genes and drug target genes are corrected.

[0066] Preferably, the process of the basic evaluation unit for obtaining the drug basic metabolic index includes:

[0067] When the age of the treatment subject is between 0 and 18 years old, an age coefficient of 1 is assigned; when the age of the treatment subject is between 18 and 44 years old, an age coefficient of 0.95 is assigned; when the age of the treatment subject is between 45 and 64 years old, an age coefficient of 0.9 is assigned; when the age of the treatment subject is greater than 65 years old, an age coefficient of 0.8 is assigned;

[0068] When the treatment subject is male, a gender coefficient of 1 is assigned; when the treatment subject is female, a gender coefficient of 0.9 is assigned;

[0069] When the BMI value of the treatment subject is between 18.5 and 24.9, a BMI coefficient of 1 is assigned; when the BMI value of the treatment subject is between 25 and 29.9, a BMI coefficient of 1.1 is assigned; when the BMI value of the treatment subject is greater than 29.9, a BMI coefficient of 1.2 is assigned;

[0070] Using the age, height and weight of the treatment subject, according to the Mifflin-St Jeor equation, the BMR value of the treatment subject is calculated. By calculating the product of the BMR value of the treatment subject, the age coefficient of the treatment subject, the gender coefficient of the treatment subject and the BMI coefficient of the treatment subject, the drug basic metabolic index is obtained.

[0071] Preferably, the process of the gene evaluation unit for obtaining the drug gene metabolic index and the drug efficacy index includes:

[0072] Referring to the PharmGKB database, metabolic enzyme genes related to drug metabolism were screened out. According to the drug action mechanism, drug target genes of the treatment subjects related to drug efficacy were screened out. Using a nucleic acid extractor and a magnetic bead kit, the magnetic bead method was adopted to extract the mRNA in the screened metabolic enzyme genes and drug target genes respectively;

[0073] Through fluorescence quantitative PCR, the mRNA in the metabolic enzyme genes and the mRNA in the drug target genes were detected. The cycle numbers corresponding to the inflection points where the fluorescence signals of the mRNA in the metabolic enzyme genes and the mRNA in the drug target genes entered the exponential growth phase from the background were recorded respectively. These cycle numbers were the Ct values of the metabolic enzyme genes and the Ct values of the drug target genes;

[0074] The internal reference gene data related to the metabolic enzyme genes and the drug target genes were extracted from the ICG database. Using the extracted internal reference gene data, the Ct values of the internal reference genes were calculated. According to the Ct values of the internal reference genes, the ratios of the Ct values of the metabolic enzyme genes to the Ct values of the internal reference genes and the ratios of the Ct values of the drug target genes to the Ct values of the internal reference genes were calculated respectively to obtain the relative expression levels of the metabolic enzyme genes and the relative expression levels of the drug target genes;

[0075] A metabolic baseline value was set. When the relative expression level of the metabolic enzyme gene was lower than 0.5 times the metabolic baseline value, the drug gene metabolism index was 0.8, indicating weak metabolism of the drug by the metabolic enzyme gene; when the relative expression level of the metabolic enzyme gene was between 0.5 times and 1.5 times the metabolic baseline value, the drug gene metabolism index was 1.5, indicating moderate metabolism of the drug by the metabolic enzyme gene; when the relative expression level of the metabolic enzyme gene was higher than 1.5 times the metabolic baseline value, the drug gene metabolism index was 2.5, indicating strong metabolism of the drug by the metabolic enzyme gene. The drug gene metabolism index was obtained;

[0076] A efficacy baseline value was set. When the relative expression level of the drug target gene was lower than 0.5 times the efficacy baseline value, the drug efficacy index was 1, indicating low efficacy of the drug on the drug target gene; when the relative expression level of the drug target gene was between 0.5 times and 2 times the efficacy baseline value, the drug efficacy index was 2, indicating general efficacy of the drug on the drug target gene; when the relative expression level of the drug target gene was higher than 2 times the efficacy baseline value, the drug efficacy index was 3, indicating high efficacy of the drug on the drug target gene. The drug efficacy index was obtained.

[0077] Preferably, for the efficacy comprehensive evaluation unit, the construction process of the drug treatment index evaluation model includes:

[0078] According to the contributions of the drug basic metabolism index and the drug gene metabolism index to drug metabolism, weights were assigned to the drug basic metabolism index and the drug gene metabolism index respectively, and the drug metabolism index was calculated by combining the weights. The calculation formula is , where U is the drug metabolism index, and are the weights of the drug basal metabolism index and the drug gene metabolism index respectively, and are the drug basal metabolism index and the drug gene metabolism index respectively;

[0079] Take the basic treatment data, gene detection data, drug metabolism index and drug efficacy index as the first data set, and divide it into a training set and a test set according to the ratio of 7:3;

[0080] Combined with the training set data and the neural network algorithm, take the basic treatment data and gene detection data as inputs, take the drug metabolism index and drug efficacy index as outputs, learn the non-linear relationship between the basic treatment data, the metabolic enzyme genes of the treatment object and the drug metabolism index, and the non-linear relationship between the drug target genes and the drug efficacy index, and train the drug treatment index evaluation model;

[0081] Input the test set data into the drug treatment index evaluation model, use the MSE function to evaluate the error between the drug metabolism index output by the drug treatment index evaluation model and the actual drug metabolism index, and the error between the drug treatment index output by the drug treatment index evaluation model and the actual drug efficacy index. According to the evaluation results, adjust the parameters of the drug treatment index evaluation model, optimize the performance of the drug treatment index evaluation model, and deploy the optimized drug treatment index evaluation model into the system.

[0082] Preferably, the process of the drug dose allocation unit obtaining the drug dose allocation index includes:

[0083] Input the basic treatment data and gene detection data into the drug treatment index evaluation model, and output the drug metabolism index through the drug treatment index evaluation model;

[0084] Take the drug metabolism index as the second data set, and divide it into a training set and a test set according to the ratio of 8:2;

[0085] Use the training set data and the linear regression algorithm, take the drug metabolism index as the input, take the drug dose allocation index as the output, adjust the intercept term and the regression coefficient of the drug metabolism index, learn the non-linear relationship between the drug metabolism index and the drug dose allocation index, and train the drug dose allocation model;

[0086] Input the test set data into the drug dose allocation model, evaluate the performance of the drug dose allocation model, adjust the intercept term and regression coefficient of the drug dose allocation model, optimize the drug dose allocation model, and deploy the optimized drug dose allocation model into the system. The expression of this drug dose allocation model is , where, is the drug dosage allocation index, is the intercept term, is the regression coefficient of the drug metabolism index, p is the drug metabolism index, is the error term.

[0087] Preferably, the process of the drug treatment plan formulation unit obtaining the drug treatment plan in combination with the output result of the drug dosage allocation model includes:

[0088] Input the drug metabolism index into the drug dosage allocation model, and output the drug dosage allocation index through the drug dosage allocation model; input the gene detection data into the drug treatment index evaluation model, and output the drug efficacy index through the drug treatment index evaluation model;

[0089] When the drug dosage allocation index is between 0 and 3, allocate low-dose drugs to the treatment object; when the drug dosage allocation index is between 3 and 6, allocate conventional-dose drugs to the treatment object; when the drug dosage allocation index is between 6 and 10, allocate high-dose drugs to the treatment object;

[0090] When the drug efficacy index is between 0 and 2, it indicates that the corresponding drug has poor efficacy, and the drug treatment is replaced; when the drug efficacy index is between 2 and 6, it indicates that the corresponding drug has general efficacy, and the treatment is combined with similar drugs and other drugs; when the drug efficacy index is between 6 and 10, it indicates that the corresponding drug has obvious efficacy, and continue to use the similar drug for treatment;

[0091] Combine the drug dosage allocation plan and the drug type allocation plan to obtain the drug treatment plan, and encode the drug treatment plan.

[0092] Preferably, the construction process of the drug treatment plan model of the drug treatment plan formulation unit includes:

[0093] Take the drug dosage allocation index, the drug efficacy index, and the drug treatment plan code as the third data set, and divide it into a training set and a test set according to a ratio of 8:2;

[0094] Using the training set data and the neural network algorithm, take the drug dosage allocation index and the drug efficacy index as inputs, and the drug treatment plan code as the output, learn the non-linear relationship between the drug dosage allocation index, the drug efficacy index and the drug treatment plan code, and train the drug treatment plan model;

[0095] Input the test set data into the drug treatment plan model, compare the actual drug treatment plan code with the drug treatment plan code output by the drug treatment plan model, evaluate the performance of the drug treatment plan model, use the Adam optimizer to update the parameters of the convolutional neural network model, optimize the drug treatment plan model, and deploy the optimized drug treatment plan model to the system.

[0096] Preferably, in the process of formulating a personalized drug treatment plan table by the drug treatment plan formulating module in combination with the output result of the drug treatment plan formulating module, the steps are as follows:

[0097] Input the drug dose allocation index and the drug efficacy index into the drug treatment plan output model. The drug treatment plan output model outputs a drug treatment plan code. According to the drug treatment plan code, match the corresponding drug treatment plan, and in combination with the drug dose allocation index and the drug efficacy index, formulate a personalized drug treatment plan table.

[0098] First, collect basic treatment data by combining a questionnaire survey and a medical height and weight scale; use a gene sequencer to collect gene detection data and preprocess the collected data. Secondly, according to the age, gender, and BMI value of the treatment object, assign an age coefficient, a gender coefficient, and a BMI coefficient to the treatment object respectively. Use the age, height, and weight of the treatment object to calculate the BMR value of the treatment object according to the Mifflin-St Jeor equation, and then obtain the drug basal metabolic index. Immediately afterwards, use a nucleic acid extractor and a magnetic bead kit, and adopt the magnetic bead method to extract the mRNA in the screened metabolic enzyme gene and drug target gene respectively. Through real-time fluorescence quantitative PCR, obtain the relative expression level of the metabolic enzyme gene and the relative expression level of the drug target gene, and then obtain the drug gene metabolism index and the drug efficacy index. Then, use the drug basal metabolic index and the drug gene metabolism index to calculate the drug metabolism index, and construct a drug treatment index evaluation model through a neural network algorithm. Then, input the basic treatment data and the gene detection data into the drug treatment index evaluation model. The drug treatment index evaluation model outputs the drug metabolism index, use a linear regression algorithm to obtain the drug dose allocation index, and according to the drug dose allocation index and the drug efficacy index, obtain the drug treatment plan and its code. Immediately afterwards, use a convolutional neural network algorithm to construct a drug treatment plan model to realize that when the drug dose allocation index and the drug efficacy index are input, the drug treatment plan model automatically outputs the drug treatment plan. Finally, in combination with the personalized drug treatment plan output model, formulate a personalized drug treatment plan table.

[0099] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A personalized drug treatment plan formulation system, comprising a drug treatment data collection module, a drug efficacy evaluation module, a drug treatment plan formulation module, and a drug treatment plan output module, wherein, Each module is communicatively connected, characterized in that: The drug treatment data acquisition module acquires basic treatment data and gene detection data, and preprocesses the acquired data; The drug efficacy evaluation module evaluates drug metabolism and drug efficacy based on the basic treatment data and gene detection data; the gene detection data are the metabolic enzyme genes and drug target genes of the treatment subject; The drug treatment plan formulation module formulates a drug treatment plan based on the evaluation results of drug metabolism and drug efficacy; The drug treatment plan output module formulates a personalized drug treatment plan table in combination with the output result of the drug treatment plan formulation module; The drug efficacy evaluation module and the drug treatment plan formulation module specifically include: The drug efficacy evaluation module is divided into a basic evaluation unit, a gene evaluation unit, and an efficacy comprehensive evaluation unit. Among them, the basic evaluation unit obtains the drug basic metabolism index through the basic treatment data; the gene evaluation unit obtains the drug gene metabolism index and the drug efficacy index respectively through the gene detection data; the efficacy comprehensive evaluation unit combines the drug basic metabolism index, the drug gene metabolism index, and the drug efficacy index to construct a drug treatment index evaluation model; The drug treatment plan formulation module is divided into a drug dose allocation unit and a drug treatment plan formulation unit. Among them, the drug dose allocation unit uses the drug dose allocation model to input the drug metabolism index to obtain the drug dose allocation index, and the drug dose allocation model is trained and constructed by the linear regression algorithm; the drug treatment plan formulation unit obtains the drug treatment plan in combination with the output result of the drug dose allocation model, and then constructs a drug treatment plan model; The process by which the basic evaluation unit obtains the drug basic metabolism index includes: using the age, height, and weight of the treatment subject, calculating the BMR value of the treatment subject according to the Mifflin-St Jeor equation, and obtaining the drug basic metabolism index by calculating the product of the BMR value of the treatment subject, the age coefficient of the treatment subject, the gender coefficient of the treatment subject, and the BMI coefficient of the treatment subject; The process by which the gene evaluation unit obtains the drug gene metabolism index and the drug efficacy index includes: Referring to the PharmGKB database, screening out the metabolic enzyme genes related to drug metabolism, screening out the drug target genes of the treatment subject related to drug efficacy according to the drug action mechanism, and using a nucleic acid extractor and a magnetic bead kit, and adopting the magnetic bead method to extract the mRNA in the screened metabolic enzyme genes and drug target genes respectively; By fluorescence quantitative PCR, detecting the mRNA in the metabolic enzyme gene and the mRNA in the drug target gene, and respectively recording the cycle numbers corresponding to the inflection points at which the fluorescence signals of the mRNA in the metabolic enzyme gene and the mRNA in the drug target gene enter the exponential growth stage from the background. These cycle numbers are the Ct values of the metabolic enzyme gene and the drug target gene respectively; Extract reference gene data related to metabolic enzyme genes and drug target genes from the ICG database. Using the extracted reference gene data, calculate the Ct values of the reference genes. According to the Ct values of the reference genes, calculate the ratios of the Ct values of the metabolic enzyme genes to the Ct values of the reference genes and the ratios of the Ct values of the drug target genes to the Ct values of the reference genes respectively, and obtain the relative expression levels of the metabolic enzyme genes and the relative expression levels of the drug target genes; Set the metabolic baseline value. When the relative expression level of the metabolic enzyme gene is lower than 0.5 times the metabolic baseline value, the drug gene metabolism index is 0.8; when the relative expression level of the metabolic enzyme gene is between 0.5 times and 1.5 times the metabolic baseline value, the drug gene metabolism index is 1.5; when the relative expression level of the metabolic enzyme gene is higher than 1.5 times the metabolic baseline value, the drug gene metabolism index is 2.5; Set the efficacy baseline value. When the relative expression level of the drug target gene is lower than 0.5 times the efficacy baseline value, the drug efficacy index is 1; when the relative expression level of the drug target gene is between 0.5 times and 2 times the efficacy baseline value, the drug efficacy index is 2; when the relative expression level of the drug target gene is higher than 2 times the efficacy baseline value, the drug efficacy index is 3; For the efficacy comprehensive evaluation unit, the construction process of the drug treatment index evaluation model includes: According to the contributions of the drug basic metabolism index and the drug gene metabolism index to drug metabolism, assign weights to the drug basic metabolism index and the drug gene metabolism index respectively, and calculate the drug metabolism index by combining the weights; Take the basic treatment data, gene detection data, drug metabolism index and drug efficacy index as the first data set, and divide it into a training set and a test set according to the ratio of 7:3; Combined with the training set data and the neural network algorithm, take the basic treatment data and gene detection data as inputs, and the drug metabolism index and drug efficacy index as outputs, learn the non-linear relationship between the basic treatment data, the metabolic enzyme genes of the treatment object and the drug metabolism index, and the non-linear relationship between the drug target genes and the drug efficacy index, and train the drug treatment index evaluation model; Input the test set data into the drug treatment index evaluation model, use the MSE function to evaluate the error between the drug metabolism index output by the drug treatment index evaluation model and the actual drug metabolism index, and the error between the drug treatment index output by the drug treatment index evaluation model and the actual drug efficacy index. According to the evaluation results, adjust the parameters of the drug treatment index evaluation model, optimize the performance of the drug treatment index evaluation model, and deploy the optimized drug treatment index evaluation model to the system.

2. The personalized drug treatment plan formulation system according to claim 1, characterized in that: For the drug treatment data acquisition module, the process of collecting basic treatment data and gene detection data and preprocessing the collected data includes: Deploy collection devices to collect basic treatment data and gene detection data. Among them, the collection devices include a medical height and weight scale and a gene sequencer; The basic treatment data are the age, gender, height, weight and BMI value of the treatment object; Perform data cleaning and data calibration on the age, gender, height, weight, and BMI values of the treated subjects. Use the FastQC tool to perform quality assessment on the metabolic enzyme genes and drug target genes of the treated subjects. Use the Cutadapt tool to remove low-quality bases in the metabolic enzyme genes and drug target genes. Use GATK BaseRecalibrator to correct the quality values of the bases in the metabolic enzyme genes and drug target genes.

3. The personalized drug treatment plan formulation system according to claim 2, characterized in that: The process by which the basic evaluation unit obtains the basic drug metabolism index includes: Assign an age coefficient to the treated subjects in the corresponding age range according to the age range of the treated subjects, assign a gender coefficient to the treated subjects in the corresponding gender type according to the gender type of the treated subjects, and assign a BMI coefficient to the treated subjects in the corresponding BMI value range according to the BMI value range of the treated subjects.

4. The personalized drug treatment plan formulation system according to claim 3, characterized in that: The process by which the drug dosage allocation unit obtains the drug dosage allocation index includes: Input the basic treatment data and gene detection data into the drug treatment index evaluation model, and output the drug metabolism index through the drug treatment index evaluation model; Use the drug metabolism index as the second data set and divide it into a training set and a test set according to a ratio of 8:2; Use the training set data and the linear regression algorithm, use the drug metabolism index as the input, use the drug dosage allocation index as the output, adjust the intercept term and the regression coefficient of the drug metabolism index, learn the non-linear relationship between the drug metabolism index and the drug dosage allocation index, and train the drug dosage allocation model; Input the test set data into the drug dosage allocation model, evaluate the performance of the drug dosage allocation model, adjust the intercept term and the regression coefficient of the drug dosage allocation model, optimize the drug dosage allocation model, and deploy the optimized drug dosage allocation model to the system.

5. The personalized drug treatment plan formulation system according to claim 4, characterized in that: The process by which the drug treatment plan formulation unit obtains the drug treatment plan by combining the output results of the drug dosage allocation model includes: Input the drug metabolism index into the drug dosage allocation model, and output the drug dosage allocation index through the drug dosage allocation model; input the gene detection data into the drug treatment index evaluation model, and output the drug efficacy index through the drug treatment index evaluation model; Assign a drug dosage allocation plan to the treated subjects according to the drug dosage allocation index. The drug dosage allocation plan includes low-dose allocation, conventional-dose allocation, and high-dose allocation; Assign a drug type allocation plan to the treated subjects according to the drug efficacy index. The drug type allocation plan includes changing drug treatment, combining the same type of drug with other drugs for treatment, and continuing to use the same type of drug for treatment; Combine the drug dosage allocation plan and the drug type allocation plan to obtain a drug treatment plan, and encode the drug treatment plan.

6. The personalized drug treatment plan formulation system according to claim 5, wherein: The process of constructing the drug treatment plan model by the drug treatment plan formulation unit includes: Use the drug dosage allocation index, the drug efficacy index, and the drug treatment plan code as the third data set and divide it into a training set and a test set according to a ratio of 8:2; Using the training set data and neural network algorithms, taking the drug dosage allocation index and the drug efficacy index as inputs, and taking the drug treatment plan encoding as the output, to learn the non-linear relationship between the drug dosage allocation index and the drug efficacy index and the drug treatment plan encoding, and training the drug treatment plan model; Inputting the test set data into the drug treatment plan model, comparing the actual drug treatment plan encoding with the drug treatment plan encoding output by the drug treatment plan model, evaluating the performance of the drug treatment plan model, updating the parameters of the convolutional neural network model, optimizing the drug treatment plan model, and deploying the optimized drug treatment plan model into the system.

7. The personalized drug treatment plan formulation system according to claim 6, wherein: The process of formulating a personalized drug treatment plan table by the drug treatment plan formulation module in combination with the output result of the drug treatment plan formulation module includes: Inputting the drug dosage allocation index and the drug efficacy index into the drug treatment plan output model, outputting the drug treatment plan encoding through the drug treatment plan output model, matching the corresponding drug treatment plan according to the drug treatment plan encoding, and formulating a personalized drug treatment plan table in combination with the drug dosage allocation index and the drug efficacy index.

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