An interpretation system, method and storage medium for precise drug use in tumors

Abstract

The present invention relates to the field of precision medicine technology, specifically a system, method, and storage medium for interpreting precise tumor medication, including: a data acquisition module for collecting genetic data, clinical data, and drug response data of tumor patients from multiple data sources; a data standardization processing module for formatting and standardizing the data to ensure a consistent format and enable effective processing and analysis in subsequent modules; a genetic data analysis module for performing variation analysis based on the patient's genetic data, identifying gene mutation sites, and generating drug sensitivity analysis results; and a drug screening module for screening drugs that match the patient's gene mutation sites from a drug database based on the genetic data analysis results, and providing drug dosage recommendations. By adopting a technical solution that combines quantum optimization technology with genetic data analysis, the technical effect of significantly improving the accuracy of drug screening and dosage recommendations is achieved.

Description

Technical Field

[0001] The present invention relates to the field of precision medicine technology, and specifically to an interpretation system, method and storage medium for precise medication of tumors. Background Art

[0002] With the rapid development of genomics and precision medicine technologies, personalized cancer treatment has become a hot topic in medical research. Currently, many cancer treatments rely on clinicians' empirical judgment, selecting therapeutic drugs based on the patient's basic condition and traditional diagnostic methods. However, with the increasing complexity of tumors and individual variability among patients, traditional treatment plans often fail to accurately match drugs, resulting in some patients failing to achieve the expected therapeutic effect after medication and even experiencing serious side effects. Existing technologies mainly rely on classical computational methods, and drug selection is often based on universal pairings between drugs and tumor types. For example, genetic data analysis is often performed separately from clinical data analysis, lacking deep multi-dimensional integration, which can lead to the neglect of certain individual characteristics. Although some studies have attempted to integrate genetic data for drug screening, existing solutions still lack sufficient accuracy and adaptability to meet the increasingly complex needs of precision cancer treatment.

[0003] In the drug screening process, existing technologies primarily rely on traditional computational optimization algorithms. While these methods can optimize drug selection to a certain extent, they often suffer from computational inefficiencies when faced with high-dimensional drug databases and personalized patient genetic profiles. This is especially true when performing large-scale drug matching, where computational time and resource consumption become significant bottlenecks.

[0004] In addition, although some genomic studies have proposed precise treatment plans based on drug-gene interactions, existing technologies are still mostly limited to a single level of drug selection, and are unable to combine personalized recommendations for drug dosage with minimization of side effects, resulting in overly simple treatment plans and a lack of room for individualized adjustment.

[0005] Furthermore, most existing report generation modules rely on manual data input, making it impossible to automatically generate accurate reports. Drug recommendations and side effect assessments in reports often rely on the physician's subjective judgment, without incorporating the patient's specific genetic mutation information and medication response history. Consequently, treatment plans lack systematic, standardized guidance.

[0006] These shortcomings of existing technologies have, to a certain extent, limited the development of precision oncology medicine. Existing methods cannot efficiently solve complex drug screening and dosage recommendation problems, and they fail to effectively integrate multi-dimensional information such as patient genetic data, clinical history, and drug response, making it impossible to provide truly personalized treatment plans. Summary of the Invention

[0007] In response to the shortcomings of the existing technology, the present invention provides an interpretation system, method and storage medium for precision tumor drug use, which solves the problems of low efficiency in precision tumor drug screening, insufficient personalized treatment plans and inaccurate drug dosage recommendations in the existing technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an interpretation system for precise drug use in tumors, comprising:

[0009] A data acquisition module, which is used to collect genetic data, clinical data, and drug response data of cancer patients from multiple data sources;

[0010] A data standardization processing module is used to format and standardize the data to make the data format consistent so that it can be effectively processed and analyzed in subsequent modules;

[0011] Gene data analysis module, used to perform variation analysis based on patient genetic data, identify gene mutation sites and generate drug sensitivity analysis results;

[0012] A drug screening module is used to screen drugs that match the patient's gene mutation site from the drug database based on the genetic data analysis results and provide drug dosage recommendations;

[0013] The quantum optimization module is used to optimize the drug screening and dosage recommendation process, and improve the matching accuracy of drugs and genetic data through quantum algorithms;

[0014] The report generation module is used to generate personalized drug treatment reports based on the patient's clinical medical history and drug response data.

[0015] Preferably, the data acquisition module includes:

[0016] A gene data collection unit, used to automatically collect the patient's gene test data from the gene testing equipment, the data including gene mutation information, single nucleotide polymorphism sites and gene expression data;

[0017] Clinical data collection unit, used to obtain patients' basic information, medical history, treatment records, examination reports and previous medication use from the hospital database;

[0018] The drug reaction data collection unit is used to collect the patient's reaction data after previous drug use, including drug efficacy, side effects and drug resistance information.

[0019] Preferably, the data standardization processing module includes:

[0020] The formatting unit is used to convert data from different sources into a unified format and map data fields according to predetermined rules so that subsequent modules can process them;

[0021] Missing value filling unit, used to supplement missing patient data, using conventional filling methods such as mean filling, interpolation, or using regression models to predict missing values ​​based on existing data;

[0022] The redundant data removal unit is used to identify duplicate data by comparing the unique identifiers of patient data records and delete records that are duplicated with the original data to ensure that each patient data is unique and non-duplicate.

[0023] Preferably, the gene data analysis module includes:

[0024] A mutation identification unit is used to analyze patient genetic data, identify mutation sites, and generate drug sensitivity analysis reports based on these mutation sites;

[0025] The drug response prediction unit predicts the patient's response to a specific drug based on the gene variation results and the existing drug response data in the historical database, and generates a list of recommended drugs.

[0026] Preferably, the drug screening module includes:

[0027] A drug target database, which is used to screen drugs that match the patient's genes from the drug database based on the known relationship between the gene mutation site and the drug target. The drug screening is based on the binding affinity between the gene mutation site and the drug target, the pharmacological properties of the drug, and the known clinical response;

[0028] The multi-objective optimization module is used to recommend drug dosages through optimization algorithms such as genetic algorithms and simulated annealing algorithms. During the optimization process, multiple factors such as drug dosage, efficacy, toxicity, and side effects are considered, and a mathematical model is used to determine the optimal drug dosage ratio.

[0029] Preferably, the quantum optimization module includes:

[0030] The quantum variational algorithm unit is used to optimize the drug screening process based on the patient's genetic mutation data and drug response history data through quantum computing methods. Specifically, it determines the best match between the drug and the genetic data through the quantum state minimization process;

[0031] The quantum approximate optimization algorithm unit is used to optimize drug dosage recommendations based on quantum algorithms in drug screening. By adjusting the state of quantum bits, it optimizes the matching relationship between drug dosage and patient gene mutation data.

[0032] Preferably, the report generation module includes:

[0033] A personalized treatment report generation unit is used to generate detailed personalized treatment plans based on the patient's genetic data, clinical history, and drug response data. The report includes drug recommendations, dosage suggestions, and efficacy predictions based on individual patient characteristics;

[0034] The side effect and efficacy evaluation unit is used to list the side effect warnings and efficacy evaluations of recommended drugs in detail in the report, and to propose a safety analysis of drug use based on the patient's previous medication history and genetic data.

[0035] Preferably, the report generation module is connected to a patient feedback module for collecting feedback data from patients after drug use, analyzing descriptions of drug efficacy, side effects and tolerability in the feedback data, and dynamically adjusting drug screening and dosage recommendation plans based on the feedback data.

[0036] The present invention also provides an interpretation method for precise drug use in tumors, comprising the following steps:

[0037] Step 1: Collect genetic data, clinical data, and drug response data of cancer patients;

[0038] Step 2: standardize the genetic data and perform genetic data comparison and variation analysis based on a genetic comparison algorithm;

[0039] Step 3: Based on the gene variation analysis results, screen out drugs that match the gene variation site and recommend drug dosages;

[0040] Step 4: Generate a personalized drug treatment report and provide drug selection, dosage recommendations, expected efficacy and side effect analysis.

[0041] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0042] The present invention provides an interpretation system, method, and storage medium for precise drug use in tumors. It has the following beneficial effects:

[0043] 1. This invention significantly improves the accuracy of drug screening and dosage recommendations by combining quantum optimization technology with genetic data analysis. Compared to conventional drug screening methods that rely on classical computer algorithms for single-objective optimization, this invention uses quantum computing to overcome the high dimensionality and computational efficiency bottlenecks in complex optimization problems, thereby achieving faster and more accurate drug matching and dosage optimization.

[0044] 2. This invention utilizes multi-module collaboration, specifically seamlessly integrating modules such as data acquisition, standardized processing, genetic data analysis, and drug screening, to achieve the technical effect of improving system processing speed and data accuracy. Compared to existing solutions that require manual intervention or separate modules for data processing, this invention, through automated and modular design, enables comprehensive analysis and processing of patient data in a much shorter timeframe, significantly reducing the potential for human error and data omissions.

[0045] 3. This invention provides highly personalized treatment plans through a personalized report generation module that combines gene mutation information, clinical data, and drug response data. Unlike existing treatment plans that generally rely on the subjective judgment of doctors, the personalized treatment reports automatically generated by this invention are not only more standardized and objective, but also enable real-time adjustment of recommended plans based on the patient's genetic characteristics and drug response, significantly improving the accuracy and safety of treatment.

[0046] 4. This invention utilizes a hybrid optimization approach combining quantum and classical computing to achieve the technical benefits of optimizing computational efficiency and reducing drug screening time. Compared to existing approaches that use purely classical algorithms to solve optimization problems, this invention significantly reduces computational time during drug screening by leveraging the parallel processing capabilities introduced by quantum computing. This enables the system to process larger amounts of patient data, providing faster support for clinical treatment decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is the main framework diagram of the present invention;

[0048] Figure 2 is a system flow chart of the present invention;

[0049] Figure 3 Schematic diagram of the computer device structure of the present invention.

[0050] Among them, 100, data acquisition module; 200, data standardization processing module; 300, gene data classification module; 400, drug screening module; 500, quantum optimization module; 600, report generation module; 40, computer equipment; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] Please see the attached Figure 1 -Attached Figure 3 The embodiment of the present invention provides an interpretation system for precise drug use in tumors, the system comprising:

[0053] A data acquisition module, which is used to collect genetic data, clinical data, and drug response data of cancer patients from multiple data sources;

[0054] The data standardization processing module is used to format and standardize data to make the data format consistent so that it can be effectively processed and analyzed in subsequent modules;

[0055] Gene data analysis module, used to perform variation analysis based on patient genetic data, identify gene mutation sites and generate drug sensitivity analysis results;

[0056] The drug screening module is used to screen drugs that match the patient's gene mutation sites from the drug database based on the results of genetic data analysis and provide drug dosage recommendations;

[0057] The quantum optimization module is used to optimize the drug screening and dosage recommendation process, and improve the matching accuracy of drugs and genetic data through quantum algorithms;

[0058] The report generation module is used to generate personalized drug treatment reports based on the patient's clinical medical history and drug response data.

[0059] The data acquisition module includes:

[0060] A gene data collection unit is used to automatically collect the patient's gene test data from the gene testing equipment, including gene mutation information, single nucleotide polymorphism sites and gene expression data;

[0061] Clinical data collection unit, used to obtain patients' basic information, medical history, treatment records, examination reports and previous medication use from the hospital database;

[0062] The drug reaction data collection unit is used to collect the patient's reaction data after previous drug use, including drug efficacy, side effects and drug resistance information.

[0063] The data standardization processing module includes:

[0064] The formatting unit is used to convert data from different sources into a unified format and map data fields according to predetermined rules so that subsequent modules can process them;

[0065] Missing value filling unit, used to supplement missing patient data, using conventional filling methods such as mean filling, interpolation, or using regression models to predict missing values ​​based on existing data;

[0066] The redundant data removal unit is used to identify duplicate data by comparing the unique identifiers of patient data records and delete records that are duplicated with the original data to ensure that each patient data is unique and non-duplicate.

[0067] Gene data analysis modules include:

[0068] A mutation identification unit is used to analyze patient genetic data, identify mutation sites, and generate drug sensitivity analysis reports based on these mutation sites;

[0069] The drug response prediction unit predicts the patient's response to a specific drug based on the gene variation results and the existing drug response data in the historical database, and generates a list of recommended drugs.

[0070] The drug screening module includes:

[0071] Drug target database, which is used to screen drugs that match the patient's genes from the drug database based on the known relationship between gene mutation sites and drug targets. Drug screening is based on the binding affinity between gene mutation sites and drug targets, the pharmacological properties of drugs, and known clinical responses;

[0072] The multi-objective optimization module is used to recommend drug dosages through optimization algorithms such as genetic algorithms and simulated annealing algorithms. During the optimization process, multiple factors such as drug dosage, efficacy, toxicity, and side effects are considered, and a mathematical model is used to determine the optimal drug dosage ratio.

[0073] The quantum optimization module includes:

[0074] The quantum variational algorithm unit is used to optimize the drug screening process based on the patient's genetic mutation data and drug response history data through quantum computing methods. Specifically, it determines the best match between the drug and the genetic data through the quantum state minimization process;

[0075] The quantum approximate optimization algorithm unit is used to optimize drug dosage recommendations based on quantum algorithms in drug screening. By adjusting the state of quantum bits, it optimizes the matching relationship between drug dosage and patient gene mutation data.

[0076] The report generation module includes:

[0077] A personalized treatment report generation unit is used to generate detailed personalized treatment plans based on the patient's genetic data, clinical history, and drug response data. The report includes drug recommendations, dosage suggestions, and efficacy predictions based on individual patient characteristics;

[0078] The side effect and efficacy evaluation unit is used to list the side effect warnings and efficacy evaluations of recommended drugs in detail in the report, and to propose a safety analysis of drug use based on the patient's previous medication history and genetic data.

[0079] The system also includes:

[0080] The patient feedback module is used to collect feedback data from patients after drug use, analyze the descriptions of the drug's efficacy, side effects and tolerability in the feedback data, and dynamically adjust the drug screening and dosage recommendation plans based on the feedback data.

[0081] In this embodiment, the specific implementation of the data acquisition module is as follows:

[0082] In this embodiment, the data collection module mainly collects multi-dimensional information of patients by interacting with multiple data sources. Specifically, the data collection module includes the following key units:

[0083] The primary task of the genetic data collection unit is to collect genetic test data from patients using genetic testing equipment. Typically, this genetic data includes information on gene mutations, single nucleotide polymorphisms (SNPs), and gene expression data. To obtain data from genetic testing equipment, the unit typically connects to a genetic analyzer or genome sequencing device, automatically transferring the sequencing data to the system for storage and further processing.

[0084] Specifically, the genetic data collection unit is capable of extracting genetic variation information from clinical genetic testing reports, including but not limited to:

[0085] ;

[0086] in, Single nucleotide polymorphisms (SNPs) are mutation sites. Indels involve insertions or deletions within a gene sequence, while copy number variations (CNVs) refer to changes in the number of copies of a gene. Through this unit, the system can obtain multidimensional data on the patient's genome, providing data support for subsequent genetic analysis and drug sensitivity analysis.

[0087] The main task of the clinical data collection unit is to obtain the patient's clinical history, examination results, treatment records, and other information from the hospital's electronic medical record system (EMR). As an option, this unit can automatically extract the patient's basic information, medical history, examination reports, treatment records, etc. by connecting to the hospital information system (HIS). Specifically, the patient's clinical data includes but is not limited to:

[0088] ;

[0089] Among them, medical history includes the patient's past medical history, family medical history, etc.; treatment records include the drug treatment received, treatment plan and efficacy feedback, etc.; examination results include imaging examinations, laboratory tests, etc.

[0090] In this implementation, the data collection module can seamlessly connect with multiple hospital information management systems to achieve real-time data acquisition and ensure the efficiency and accuracy of the data collection process.

[0091] The primary task of the Drug Response Data Collection Unit is to collect patient response data after receiving specific drug treatment, including efficacy, side effects, and drug resistance. Integrating with the patient follow-up system, this unit continuously captures information on side effects, drug resistance, and efficacy during medication use. Through patient self-reports or physician feedback, drug response data can be recorded in real time and uploaded to the system, providing feedback for the drug screening module. This unit is particularly suitable for collecting data from long-term follow-up patients, ensuring comprehensive monitoring of medication effectiveness.

[0092] In one possible implementation, the data acquisition module further includes the following technical means:

[0093] Data Interface and Protocol Support: To enable integration with various hospital information systems, genetic testing platforms, and drug response databases, the data acquisition module supports multiple data transmission protocols, such as HL7 and FHIR, among other standard data formats. These protocols enable efficient data exchange between the data acquisition module and diverse data sources. In practice, the data acquisition unit utilizes APIs and data mapping mechanisms to convert data in various formats into standard system formats, ensuring data compatibility and operability.

[0094] Data encryption and privacy protection: Because sensitive patient data is involved, the data collection module must implement strict data encryption and privacy protection measures. SSL / TLS encryption protocols are used during data transmission to ensure data security. High-strength encryption algorithms such as AES-256 are used during data storage to encrypt patient genetic data and clinical records to prevent data leakage.

[0095] Data cleaning and verification: During the data collection process, missing data or inconsistent data formats may be encountered. To address this, the data collection module has designed an automated verification mechanism that performs real-time checks on input data to ensure data integrity and consistency. For example, if missing sites are detected in genetic data, the system will automatically flag and provide a prompt to ensure data quality.

[0096] The data collection module ensures the efficient operation of the entire precision medication interpretation system by automating and standardizing the collection of multi-dimensional patient data. While automating data collection, it also significantly improves the accuracy and real-time nature of data collection through integration with hospital information systems, genetic testing platforms, and drug response databases, providing strong data support for subsequent genetic analysis and drug screening.

[0097] In this embodiment, the specific implementation of the data standardization processing module is as follows:

[0098] In some embodiments, the data normalization processing module includes the following key units, each of which is responsible for different types of data processing and conversion tasks:

[0099] Formatting unit: Generally, different data sources have different data formats. To ensure data consistency, the formatting unit converts various types of data into a unified format. In some implementations, genetic data, clinical data, and drug response data are standardized using a predetermined format, converting raw formats such as CSV, JSON, or XML into a standard data format that is unified within the system. For example, genetic data is typically stored in VCF format (variation call format) or FASTA format (gene sequence data format), while clinical data is often formatted using HL7 or FHIR standards. After formatting, all data is converted into structured data suitable for subsequent analysis by the system.

[0100] Specifically, genetic data fields include, but are not limited to, gene name, variant type, mutation site, and variant frequency; clinical data fields may include patient name, age, gender, medical history, and medication use history; and drug response data fields may include drug name, duration of use, efficacy score, and side effect records. Through this module, all collected data can be stored in a unified format, ensuring data availability.

[0101] Missing Value Filling Unit: In some embodiments, the missing value filling unit is used to address missing data. Genetic or clinical data often contain missing data due to experimental or recording issues. To address this, the missing value filling unit employs various methods. For example, missing information in genetic data may be supplemented using techniques such as mean filling, regression model prediction, or interpolation.

[0102] In the specific implementation, the choice of missing value filling method depends on the type and distribution of the data. Assume that the type of missing part of the gene data is " ", and use regression analysis to fill it, you can use the following formula:

[0103] ;

[0104] in, is a regression function that fills missing values ​​based on the existing genetic data and the patient's genetic background. Similarly, when clinical data are missing, the mean filling method can be used:

[0105] ;

[0106] in, represents a patient's clinical data record, is the data sample size, and the missing data are finally filled by the mean.

[0107] Redundant Data Removal Unit: During data collection, especially when collecting data simultaneously from multiple data sources, the presence of redundant data can affect the accuracy of analytical results. The Redundant Data Removal Unit identifies and removes duplicate data by comparing unique patient identifiers (e.g., patient ID, visit number, etc.).

[0108] For example, duplicate records may be generated between patient genetic data, clinical data, and drug response data due to system synchronization or multiple collections. In this case, the redundant data removal unit will determine whether these records belong to the same patient based on the unique identifier. If so, the latest record will be retained and the duplicate data will be deleted. For example, if there are redundant records between genetic data and drug response data, using the patient ID To compare:

[0109] ;

[0110] Ultimately, after redundant data is removed, only valid and unique patient records are saved in the system.

[0111] Data cleaning and outlier detection unit: During the data collection process, data anomalies or noise may occur due to input errors or equipment problems. The data cleaning and outlier detection unit identifies and removes abnormal data that does not meet expectations by setting thresholds.

[0112] Specifically, if a record of a certain item in clinical data (such as a patient's body temperature) is significantly higher or lower than the normal range, a threshold can be set to determine whether the data is an outlier. For example, if the body temperature exceeds 42°C or is lower than 28°C, it can be considered an outlier and needs to be processed or deleted. Outlier detection is achieved using the following formula:

[0113] ;

[0114] In one possible implementation, the data normalization processing module further includes the following features:

[0115] Batch Data Processing: In some implementations, the data standardization module provides batch data processing capabilities when large amounts of data need to be processed. This means that when large amounts of genetic, clinical, and drug response data are collected into the system, formatting, missing value filling, and redundant data removal can all be efficiently performed in batches, thus avoiding manual errors and improving the automation and accuracy of data processing.

[0116] Correlation analysis of multi-dimensional data: In addition to simple format conversion and data cleaning, the data standardization processing module also supports correlation analysis of multi-dimensional data. For example, correlation analysis of genetic data, clinical data, and drug response data can help the system discover potential relationships between various data types based on standardized data, providing more information for subsequent precision medication analysis.

[0117] In this embodiment, the specific implementation of the gene data analysis module is as follows:

[0118] In some embodiments, the genetic data analysis module mainly includes two submodules: a variation identification unit and a drug sensitivity analysis unit. Specifically: Variation identification unit: The core of the genetic data analysis module is the variation identification unit. Generally, this unit identifies the mutation sites in the patient's genes by comparing with the standard reference genome. This unit supports a variety of alignment algorithms, such as BWA (Burrows-Wheeler Aligner), Bowtie2, STAR, etc., to ensure that the alignment task is completed efficiently and accurately. Specifically, the sequences in the genetic data will be aligned to the reference genome, and the algorithm will identify the mutation type, such as single nucleotide polymorphisms (SNPs), insertion and deletion mutations (Indels), etc.

[0119] In one possible implementation, the gene data analysis module uses a gene alignment algorithm to mark variant sites and generate a variant list:

[0120] ;

[0121] in, Represents each mutation site in the genetic data. Each mutation site may correspond to different types of mutations, such as SNPs, Indels, etc. Further, this mutation information will be converted into a standard data structure for subsequent analysis and used by the drug response prediction module.

[0122] Drug Sensitivity Analysis Unit: After variant identification, the Drug Sensitivity Analysis Unit further analyzes the identified mutation sites. This unit relies on existing drug-gene interaction databases, such as DrugBank and PharmGKB, and combines known gene mutations with drug response data to assess the effectiveness and potential side effects of different drugs for patients. By analyzing the relationship between gene mutation sites and drug targets, the Drug Sensitivity Analysis Unit can predict a patient's response to a specific drug.

[0123] Specifically, during the analysis process, the drug's target site and the patient's gene mutation site are matched. If a drug's target site closely matches the patient's mutation site, the drug is likely to be effective for the patient. The degree of target site matching is calculated using the following formula:

[0124] ;

[0125] Among them, the higher the matching value, the better the match between the drug and the patient's gene mutation site, and the better the therapeutic effect may be.

[0126] In some embodiments, the drug sensitivity analysis unit also conducts a comprehensive evaluation of the efficacy and side effects of the drug through historical clinical data to provide more accurate recommendations for the drug screening module.

[0127] Gene-drug response prediction: In addition to gene mutation information, drug efficacy is also influenced by other factors, such as interactions between the drug and the patient's metabolic genes. To this end, the genetic data analysis module also combines the patient's metabolic genotype to predict the drug's metabolic efficiency in the body. This analysis is based on the correlation between metabolic enzyme genes (such as CYP450 genes) and the drug's metabolic pathways. By analyzing the metabolic pathways, the system can assess the drug's metabolic rate and predict whether the patient is likely to experience drug accumulation, excessive reaction, or drug ineffectiveness.

[0128] Specifically, the metabolic efficiency of a drug can be calculated using the following formula:

[0129] ;

[0130] in, is the metabolic function, The metabolic genotype of the patient is used, and the drug dosage is the treatment plan calculated based on clinical data.

[0131] In one possible implementation, the genetic data analysis module further includes the following features:

[0132] Multiple gene analysis capabilities: In some embodiments, the genetic data analysis module not only supports single gene analysis but also handles the combined analysis of multiple genes. For example, the system can identify "gene pathways" involving multiple mutation sites in a patient's genome and evaluate the efficacy of the entire pathway and the effectiveness of the drug. For example, certain gene pathways (such as the PI3K / AKT pathway and the MAPK pathway) have a significant impact on tumor cell proliferation and drug resistance. Based on the patient's mutation information, the system can assess the sensitivity of these pathways to drugs and provide support for subsequent drug screening.

[0133] High-throughput genomic data analysis: The Gene Data Analysis module also supports batch processing of high-throughput genomic data. Through efficient computing architecture and optimized algorithms, the system can rapidly process large amounts of data from genomic sequencing platforms, supporting whole-genome mutation analysis and helping physicians identify potential drug targets.

[0134] Results Visualization: To help clinicians understand and use genetic data analysis results, the Gene Data Analysis module provides data visualization. Through charts and heat maps, results such as gene mutation sites and drug sensitivity scores are visualized, helping doctors intuitively understand the relationship between a patient's genetic profile and drug response.

[0135] In this embodiment, the specific implementation of the drug screening module is as follows:

[0136] In some embodiments, the drug screening module includes a drug target database, a drug screening algorithm, and a multi-objective optimization module. Specifically:

[0137] Drug Target Database: Generally, the drug screening module identifies drugs that match the patient's genetic variant by connecting to a drug target database. Drug target databases, such as DrugBank and PharmGKB, contain information on a large number of known drugs and their targets, which record known interactions between drugs and genetic variants.

[0138] In a specific implementation, the drug screening module extracts drug information related to the patient's gene mutation from the drug target database. Assuming the patient has a mutation in the EGFR gene, the screening algorithm will search the database for anticancer drugs that target EGFR. The drug target match can be calculated using the following formula:

[0139] ;

[0140] in, Indicates the The matching degree between the drug target and the patient's gene mutation site, is the number of drug targets involved in the match. By calculating the matching degree, the system can screen out drugs that match the patient's gene mutation site.

[0141] Drug screening algorithms, in some embodiments, not only consider the matching relationship between gene mutations and drug targets, but also factors such as the drug's pharmacological properties, clinical efficacy, and side effects. As an option, the drug screening algorithm uses a multi-factor weighted algorithm to comprehensively evaluate the drug's efficacy, drug resistance, side effects, and the patient's personal characteristics. Specifically, the drug screening algorithm calculates the expected efficacy of the drug in the patient, which can be calculated using the following formula:

[0142] ;

[0143] in, Indicates the score of a drug on a certain characteristic (such as efficacy, side effects, etc.). is the weight of the feature, This is the number of features considered during drug screening. By combining the scores of each feature, the algorithm generates an efficacy score for each drug and ultimately recommends the most appropriate drug based on the score.

[0144] Multi-objective optimization module: In some embodiments, the drug screening module also includes a multi-objective optimization module. This module uses optimization methods such as genetic algorithms and simulated annealing algorithms to optimize drug dosage recommendations based on multiple objectives, including drug efficacy, side effects, drug metabolic pathways, and patient genotype. The multi-objective optimization module considers multiple factors and uses optimization algorithms to select the optimal solution within a set of drug candidates.

[0145] For example, suppose drug A scores highly for efficacy but has significant side effects, while drug B has fewer side effects but slightly less efficacy. The multi-objective optimization module uses weighted calculations to ultimately find the balance between side effects and efficacy and recommend an appropriate drug dosage for the patient. This optimization module can optimize drug dosage using the following formula:

[0146] ;

[0147] in, , , These are the weights of efficacy, side effects, and metabolic scores. The system will adjust the weights based on the patient's actual situation to ensure that the most suitable drug and dosage are selected.

[0148] In one possible implementation, the drug screening module further includes the following features:

[0149] Updating and maintaining the drug database: In some embodiments, the drug screening module supports real-time updating and maintenance of the drug database. As new drug development and clinical trial data continue to increase, the drug information in the database is regularly updated to ensure that the drug screening module provides the latest and most accurate drug recommendations. For example, when the FDA approves a new drug, the drug screening module can obtain the drug's pharmacological information, targets, indications, and side effect data in real time, add it to the database, and promptly update the screening results.

[0150] Personalized adjustment of patient characteristics: In some embodiments, the drug screening module can perform personalized drug screening based on individual characteristics of the patient, such as age, gender, weight, and medical history. The patient's weight and metabolic characteristics will affect the recommended drug dosage. For example, elderly patients may metabolize drugs more slowly, so the drug dosage should be appropriately reduced. The drug screening module can automatically adjust relevant parameters in the drug screening process based on the patient's basic characteristics, providing more accurate drug recommendations.

[0151] Feedback mechanism for historical drug response data: In some embodiments, the drug screening module also accesses the patient's historical drug response data. This historical drug response data can be updated in real time through patient follow-up information or drug feedback mechanisms. By analyzing a patient's historical drug usage data, the system can provide feedback on drug efficacy and side effects, further optimizing drug screening accuracy. The system uses this feedback data to adjust drug screening algorithms and drug dosage recommendations, ensuring a dynamically optimized treatment plan for the patient.

[0152] In this embodiment, the specific implementation of the quantum optimization module is as follows:

[0153] In some embodiments, the quantum optimization module mainly optimizes the drug screening and dosage recommendation process through the quantum variational algorithm (VQE) and the quantum approximate optimization algorithm (QAOA). Specifically:

[0154] Quantum Variational Estimation (VQE): Quantum Variational Estimation (VQE) is generally used to optimize complex parameters in the drug screening process, minimizing the mismatch between drug and genetic data through quantum computing. In the quantum optimization module, the VQE algorithm uses quantum bits to represent the characteristics of drug and genetic data and iterates to find the optimal solution. Specifically, the VQE algorithm uses quantum states to represent the patient's genetic mutation data and the drug's target. It then performs quantum computing using quantum circuits to find the drug with the lowest possible match and the optimal dosage.

[0155] The optimization process of the quantum variational algorithm can be expressed as:

[0156] ;

[0157] in, represents the quantum Hamiltonian, is the quantum variational parameter, and the goal of minimizing the error in matching genetic data with drugs is to minimize the error. Through multiple iterations, the quantum circuit will produce the optimal matching result between the drug and the patient's genetic data.

[0158] Quantum Approximate Optimization (QAOA): The Quantum Approximate Optimization (QAOA) algorithm is suitable for solving multi-objective optimization problems. Drug screening and dosage recommendation processes involve balancing multiple objectives, such as efficacy, side effects, and metabolic rate. The QAOA algorithm optimizes drug dosage ratios by adjusting the state of quantum bits (qubits), achieving a balance between these multiple objectives. Specifically, QAOA constructs quantum circuits and leverages the superposition and interference effects of quantum bits to comprehensively optimize multiple drug objectives.

[0159] The optimization objective of QAOA can be expressed as:

[0160] ;

[0161] in, Indicates the The optimization function of the objective, is the weight of the target, is the number of optimization targets considered. Through QAOA, the quantum optimization module can recommend the most appropriate dosage and regimen for each drug.

[0162] The quantum optimization module's computational process: In some embodiments, the quantum optimization module transforms the drug screening and dosage recommendation tasks into quantum bit state updates through multiple rounds of quantum computation. During the computational process, quantum circuits, combined with classical computing algorithms, continuously adjust parameters to ultimately achieve the optimal drug and dosage matching result. The introduction of quantum computing effectively accelerates the drug screening and dosage recommendation process, reducing computational time and improving the accuracy of the results.

[0163] The quantum optimization module uses quantum bits to represent all possible combinations of drug screening and dosage recommendations. The calculation path of the quantum circuit will automatically explore all combinations and gradually find the optimal solution. The optimization process can be expressed as:

[0164] ;

[0165] in, Indicates the The loss function of the optimization objective, is the number of objective functions. Through quantum computing, the optimal solution can be obtained in a very short time, and the error of the optimization process is greatly reduced.

[0166] In one possible implementation, the quantum optimization module also includes the following features:

[0167] Allocation and Scheduling of Quantum Computing Resources: In some embodiments, the quantum optimization module utilizes dynamic allocation and scheduling of quantum computing resources to address complex drug screening and dosage optimization tasks. This allocation not only considers the computational power of quantum computing but also dynamically adjusts resource allocation based on task complexity and the number of optimization objectives. Through flexible scheduling, the quantum optimization module maximizes the efficiency of quantum computing and ensures computations are completed in the shortest possible time.

[0168] Quantum-classical hybrid computing: While quantum computing offers significant advantages in certain areas, many computational problems still require the assistance of classical computers. Therefore, in some embodiments, the quantum optimization module employs a quantum-classical hybrid computing approach, combining quantum and classical computing. Classical computers handle the parts of quantum computing that cannot be efficiently processed, while quantum computers handle the complex optimization problems. This hybrid computing approach allows the system to fully leverage the strengths of both quantum and classical computing, improving computational efficiency and optimization accuracy.

[0169] In some embodiments, the report generation module generates personalized treatment reports mainly through the following functional units:

[0170] Personalized treatment plan generation unit: Generally, the report generation module will first combine the patient's genetic data, clinical history, drug response data, and the output results of the drug screening module to generate a personalized drug treatment plan. Specifically, the treatment plan includes drug recommendations, dose adjustments, and efficacy predictions. Based on the drug and dose recommendations provided by the drug screening module and the quantum optimization module, the personalized treatment plan generation unit will generate a detailed drug use guide. For example, if the system recommends a drug targeting EGFR mutations and recommends a specific dose, the report will list in detail the drug's dose, usage cycle, and possible side effects.

[0171] Specifically, the formula for generating personalized treatment plans can be expressed as:

[0172] ;

[0173] Among them, drug recommendations are based on gene-drug matching and drug screening results, dosage recommendations are personalized according to factors such as the patient's weight, age, and drug metabolism, efficacy prediction is based on the matching between drugs and genes, and side effect analysis comprehensively considers the known side effects of the drug and the patient's tolerance.

[0174] Side Effects and Efficacy Assessment Unit: In some embodiments, the Side Effects and Efficacy Assessment Unit is specifically responsible for analyzing the side effects and efficacy of each recommended drug based on the patient's genetic data, drug response data, and historical treatment history. Specifically, drug side effects may be affected by factors such as genetic mutations, age, and weight. Therefore, this unit reviews the patient's historical drug response data and combines it with a database of known drug side effects (such as the FDA's drug side effect database) to generate a safety analysis of drug use.

[0175] For example, if a patient has a known allergic reaction to a drug, the unit will automatically flag this and offer an appropriate alternative. The results of the side effect analysis are expressed as follows:

[0176] ;

[0177] in, is a side effect risk scoring function, in which drug dose, patient genotype, and historical drug response data jointly affect the assessment of side effect risk.

[0178] Data visualization unit: In some embodiments, to help doctors quickly understand drug recommendation results and treatment plans, the report generation module includes a data visualization unit. Through charts, heat maps, bar charts, and other forms, information such as treatment plans, drug side effects, and efficacy predictions are presented in a more intuitive manner. For example, the side effects of a drug can be displayed using a heat map to show the effect of the drug on different gene mutations, and the efficacy of the drug can be displayed using a bar chart to compare the treatment effects at different doses. Specifically, through data visualization, doctors can quickly evaluate the applicability of the drug during treatment and adjust the treatment plan based on patient feedback.

[0179] Dynamic update unit for treatment plans; As an option, the report generation module also supports dynamic updates of treatment plans. Based on patient feedback during treatment, the system can update the treatment plan in real time and adjust drug dosages or replace drugs through automated mechanisms. For example, if the patient's feedback on the efficacy of the drug is poor or the side effects are severe, the system will automatically update the recommended plan based on the feedback data and generate a new treatment report for the doctor's reference in a timely manner. The treatment plan update formula is as follows:

[0180] ;

[0181] Through this mechanism, the report generation module can respond to the patient's treatment changes in real time and provide doctors with the latest treatment recommendations.

[0182] In one possible implementation, the report generation module further includes the following features:

[0183] Intelligent Document Generation and Automation: Generally, the report generation module also features intelligent document generation and automated typesetting. The system automatically generates standardized treatment reports based on pre-set templates and customizes them based on the patient's specific data. For example, the medication recommendation section of the report automatically displays the patient's matching medications and presents detailed information such as the drug name, dosage, duration of use, predicted efficacy, and side effects in a table format. Automated report generation reduces manual intervention and improves report generation efficiency.

[0184] Report compatibility across multiple platforms: In some embodiments, the report generation module can generate reports compatible with multiple platforms and formats, such as PDF, HTML, or Word. This allows doctors and patients to view and print reports on different devices and operating systems. Furthermore, the system supports integration of treatment reports with the patient's electronic medical record (EMR) or hospital information system (HIS), ensuring that reports can be easily stored, shared, and accessed.

[0185] The interpretation method for precision medicine use in tumors described below and the interpretation system method for precision medicine use in tumors described above can be referenced to each other.

[0186] Please see the attached Figure 2 The present invention also provides a method for interpreting tumor precision medication, comprising:

[0187] Step 1: Collect genetic data, clinical data, and drug response data of cancer patients;

[0188] Step 2: Standardize the genetic data and perform genetic data comparison and variation analysis based on the genetic comparison algorithm;

[0189] Step 3: Based on the results of gene variation analysis, screen out drugs that match the gene variation site and recommend drug dosages;

[0190] Step 4: Generate a personalized drug treatment report and provide drug selection, dosage recommendations, expected efficacy, and side effect analysis

[0191] The present invention further provides a storage medium 43 on which a computer program is stored. When the computer program is run by the processor 41 , the above method is executed.

[0192] Among them, the storage medium 43 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0193] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An interpretation system for precise drug use in tumors, characterized by: include: A data acquisition module, which is used to collect genetic data, clinical data, and drug response data of cancer patients from multiple data sources; A data standardization processing module is used to format and standardize the data to make the data format consistent so that it can be effectively processed and analyzed in subsequent modules; Gene data analysis module, used to perform variation analysis based on patient genetic data, identify gene mutation sites and generate drug sensitivity analysis results; A drug screening module is used to screen drugs that match the patient's gene mutation site from the drug database based on the genetic data analysis results and provide drug dosage recommendations; The quantum optimization module is used to optimize the drug screening and dosage recommendation process, and improve the matching accuracy of drugs and genetic data through quantum algorithms; A report generation module is used to generate personalized drug treatment reports based on the patient's clinical medical history and drug response data; The quantum optimization module includes: The quantum variational algorithm unit is used to optimize the drug screening process based on the patient's genetic mutation data and drug response history data through quantum computing methods. Specifically, it determines the best match between the drug and the genetic data through the process of quantum state minimization; The quantum approximate optimization algorithm unit is used to optimize drug dosage recommendations based on quantum algorithms in drug screening. By adjusting the state of quantum bits, it optimizes the matching relationship between drug dosage and patient gene mutation data; The quantum variational algorithm (VQE) is used to optimize complex parameters in the drug screening process, minimizing the matching error between drug and genetic data through quantum computing. In the quantum optimization module, the VQE algorithm uses quantum bits to represent the characteristics of drug and genetic data and uses iterative calculations to find the optimal solution. The VQE algorithm uses quantum states to represent the patient's genetic mutation data and the drug's target. Then, quantum computing is performed using quantum circuits to find the drug with the lowest matching degree and the optimal dosage. The optimization process of the quantum variational algorithm can be expressed as: bestmatch = argmin(H(θ)); Where H(θ) represents the quantum Hamiltonian, θ is the quantum variational parameter, and the goal of minimization is the error in matching genetic data with drugs; Quantum Approximate Optimization Algorithm (QAOA): The Quantum Approximate Optimization Algorithm (QAOA) is suitable for solving multi-objective optimization problems. Drug screening and dosage recommendation processes involve balancing multiple objectives, including drug efficacy, side effects, and metabolic rate. The QAOA algorithm optimizes drug dosage ratios by adjusting the state of quantum bits to achieve a balance between these multiple objectives. QAOA constructs quantum circuits and utilizes the superposition state and interference effects of quantum bits to comprehensively optimize multiple drug objectives. The optimization objective of QAOA can be expressed as: Among them, the objective function i represents the optimization function of the i-th objective, w i is the weight of the goal, and n is the number of optimization goals considered. The quantum optimization module uses quantum bits to represent all possible combinations of drug screening and dosage recommendations. The calculation path of the quantum circuit will automatically explore all combinations and gradually find the optimal solution. The optimization process can be expressed as: Among them, loss j ; Among them, loss j represents the loss function of the jth optimization objective, and k is the number of objective functions.

2. The interpretation system for precise drug use in tumors according to claim 1, characterized in that: The data acquisition module includes: A gene data collection unit, used to automatically collect the patient's gene test data from the gene testing equipment, the data including gene mutation information, single nucleotide polymorphism sites and gene expression data; Clinical data collection unit, used to obtain patients' basic information, medical history, treatment records, examination reports and previous medication use from the hospital database; The drug reaction data collection unit is used to collect the patient's reaction data after previous drug use, including drug efficacy, side effects and drug resistance information.

3. The interpretation system for precise drug use in tumors according to claim 1, characterized in that: The data standardization processing module includes: The formatting unit is used to convert data from different sources into a unified format and map data fields according to predetermined rules so that subsequent modules can process them; Missing value filling unit, used to supplement missing patient data, using conventional filling methods such as mean filling, interpolation, or using regression models to predict missing values ​​based on existing data; The redundant data removal unit is used to identify duplicate data by comparing the unique identifiers of patient data records and delete records that are duplicated with the original data to ensure that each patient data is unique and non-duplicate.

4. The interpretation system for precise tumor medication according to claim 1, characterized in that: The gene data analysis module includes: A mutation identification unit is used to analyze patient genetic data, identify mutation sites, and generate drug sensitivity analysis reports based on these mutation sites; The drug response prediction unit predicts the patient's response to a specific drug based on the gene variation results and the existing drug response data in the historical database, and generates a list of recommended drugs.

5. The interpretation system for precise tumor medication according to claim 1, characterized in that: The drug screening module includes: A drug target database, which is used to screen drugs that match the patient's genes from the drug database based on the known relationship between the gene mutation site and the drug target. The drug screening is based on the binding affinity between the gene mutation site and the drug target, the pharmacological properties of the drug, and the known clinical response; The multi-objective optimization module is used to recommend drug dosages through optimization algorithms such as genetic algorithms and simulated annealing algorithms. During the optimization process, multiple factors such as drug dosage, efficacy, toxicity, and side effects are considered, and a mathematical model is used to determine the optimal drug dosage ratio.

6. The interpretation system for precise tumor medication according to claim 1, characterized in that: The report generation module includes: A personalized treatment report generation unit is used to generate detailed personalized treatment plans based on the patient's genetic data, clinical history, and drug response data. The report includes drug recommendations, dosage suggestions, and efficacy predictions based on individual patient characteristics; The side effect and efficacy evaluation unit is used to list the side effect warnings and efficacy evaluations of recommended drugs in detail in the report, and to propose a safety analysis of drug use based on the patient's previous medication history and genetic data.

7. The interpretation system for precise drug use in tumors according to claim 1, characterized in that: The report generation module is connected to a patient feedback module for collecting feedback data from patients after drug use, analyzing descriptions of drug efficacy, side effects, and tolerability in the feedback data, and dynamically adjusting drug screening and dosage recommendation plans based on the feedback data.

8. A method for interpreting precise drug use for tumors, used to implement the system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: Collect genetic data, clinical data, and drug response data of cancer patients; Step 2: standardize the genetic data and perform genetic data comparison and variation analysis based on a genetic comparison algorithm; Step 3: Based on the gene variation analysis results, screen out drugs that match the gene variation site and recommend drug dosages; Step 4: Generate a personalized drug treatment report and provide drug selection, dosage recommendations, expected efficacy and side effect analysis.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the interpretation method for precise tumor medication as described in claim 8.

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