Recognition system and method for precise tumor medication and storage medium

Abstract

The invention relates to the technical field of precision medical treatment, in particular to an interpretation system and method for tumor precision medication and a storage medium, and the system comprises a data collection module which is used for collecting gene data, clinical data and drug response data of tumor patients from a plurality of data sources; the data standardization processing module is used for carrying out formatting and standardization processing on the data, so that the data formats are consistent, and effective processing and analysis can be carried out in subsequent modules; the gene data analysis module is used for performing variation analysis according to the gene data of the patient, identifying a gene mutation site and generating a drug sensitivity analysis result; and the drug screening module is used for screening drugs matched with the gene variation sites of the patient from a drug database according to the gene data analysis result, and providing drug dosage recommendation. By adopting the technical scheme of combining a quantum optimization technology and gene data analysis, the technical effect of remarkably improving the drug screening and dose recommendation precision is achieved.

Description

Technical Field

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

[0002] With the rapid development of genomics and precision medicine technologies, personalized cancer treatment has gradually become a hot topic in medical research. At present, many cancer treatments rely on the experience and judgment of clinicians, and select therapeutic drugs based on the basic conditions of patients and traditional diagnostic methods. However, with the increasing complexity of tumors and individual differences among patients, traditional treatment plans often cannot achieve accurate drug matching, resulting in some patients failing to achieve the expected therapeutic effect after taking the medicine, and even serious side effects; in the existing technology, drug screening and dosage recommendations mainly rely on classical computational methods, and the selection of drugs is often based on the universal pairing of drugs and tumor types. For example, genetic data analysis is usually separated from clinical data analysis, lacking in-depth multi-dimensional integration, which may lead to the neglect of certain individual characteristics. Although some studies have tried to combine genetic data for drug screening, existing solutions still lack sufficient accuracy and adaptability, and it is difficult to meet the increasingly complex needs of precision cancer treatment.

[0003] In the process of drug screening, existing technologies also mainly rely on traditional computational optimization algorithms. Although these methods can optimize drug selection to a certain extent, they often show low computational efficiency when faced with high-dimensional drug databases and personalized patient genetic characteristics. Especially when performing large-scale drug matching, computing time and resource consumption become huge 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 single treatment plans and a lack of room for individualized adjustment.

[0005] Moreover, most existing report generation modules rely on manual data input and cannot achieve automated and accurate report generation. The drug recommendations and side effect assessments in the reports often rely on the doctor’s subjective judgment and do not combine the patient’s specific gene mutation information and drug response history. The treatment plan lacks systematic and standardized guidance.

[0006] The shortcomings of these existing technologies have, to a certain extent, limited the development of precision medicine for tumors. Existing methods cannot efficiently solve complex drug screening and dosage recommendation problems, and do not effectively integrate multi-dimensional information such as patient genetic data, clinical history, and drug response, and cannot provide truly personalized treatment plans. Summary of the invention

[0007] In view of the shortcomings of the prior art, 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 prior art.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an interpretation system for precise drug use for tumors, comprising: A data collection module, which is used to collect genetic data, clinical data, and drug response data of tumor 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, for screening drugs that match the patient's gene mutation site from a drug database based on the gene data analysis results, and providing drug dosage recommendations; Quantum optimization module, which is used to optimize the drug screening and dosage recommendation process, and improve the matching accuracy of drugs and genetic data through quantum algorithms; The report generation module is used to generate personalized drug treatment reports based on the patient's clinical medical history and drug response data.

[0009] Preferably, the data acquisition module includes: A gene data collection unit, used to automatically collect the patient's gene test data from the gene test equipment, the data including gene mutation information, single nucleotide polymorphism sites and gene expression data; Clinical data collection unit, used to obtain the patient's 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.

[0010] Preferably, 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 the patient data records and delete the records that are duplicated with the original data, so as to ensure that each patient data is unique and non-duplicate.

[0011] Preferably, the gene data analysis module includes: A mutation identification unit is used to analyze the patient's genetic data, identify mutation sites, and generate a drug sensitivity analysis report 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.

[0012] Preferably, the drug screening module comprises: 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, wherein 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 dosage 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.

[0013] Preferably, the quantum optimization module includes: The quantum variational algorithm unit is used to optimize the drug screening process based on the patient's gene mutation data and drug response history data through quantum computing methods, specifically determining the best match between the drug and the gene data through the quantum state minimization process; 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, the matching relationship between drug dosage and patient gene mutation data is optimized.

[0014] Preferably, the report generating module comprises: Personalized treatment report generation unit, which is used to generate detailed personalized treatment plans based on the patient's genetic data, clinical history and drug response data. The report content 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.

[0015] 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.

[0016] The present invention also provides an interpretation method for precise drug use for tumors, comprising the following steps: Step 1: Collect genetic data, clinical data, and drug response data of tumor patients; Step 2: standardize the gene data, and compare and analyze the gene data based on a gene comparison algorithm; Step 3: Based on the gene variation analysis results, screening out drugs that match the gene variation site and recommending drug dosages; Step 4: Generate a personalized drug treatment report and provide drug selection, dosage recommendations, expected efficacy and side effect analysis.

[0017] 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.

[0018] The present invention provides an interpretation system, method and storage medium for precise drug use in tumors. It has the following beneficial effects: 1. The present invention achieves the technical effect of significantly improving the accuracy of drug screening and dosage recommendation by adopting a technical solution combining quantum optimization technology with genetic data analysis. Compared with the traditional drug screening method in the prior art, which relies on classical computer algorithms for single-target optimization, the present invention solves the high-dimensionality and computational efficiency bottlenecks in complex optimization problems through quantum computing, thereby achieving faster and more accurate drug matching and dosage optimization.

[0019] 2. The present invention uses multi-module collaborative work, especially through the seamless connection of modules such as data collection, standardized processing, genetic data analysis, and drug screening, to achieve the technical effect of improving system processing speed and data accuracy. Compared with the solutions in the prior art that require manual intervention or separate modules for data processing, the present invention uses automation and modular design to enable patient data to be fully analyzed and processed in a shorter time, greatly reducing the possibility of human errors and data omissions.

[0020] 3. The present invention provides a highly personalized treatment plan through a personalized report generation module, combined with gene mutation information, clinical data and drug response data. Different from the treatment plans in the prior art that generally rely on the subjective judgment of doctors, the personalized treatment reports automatically generated by the present invention are not only more standardized and objective, but also can adjust the recommended plan in real time according to the patient's genetic characteristics and drug response, significantly improving the accuracy and safety of treatment.

[0021] 4. The present invention adopts a hybrid optimization scheme of quantum computing and classical computing to achieve the technical effect of optimizing computing efficiency and reducing drug screening time. Compared with the solution of using pure classical algorithms to handle optimization problems in the prior art, the parallel processing capability introduced by quantum computing in the present invention greatly shortens the computing time in the drug screening process, enabling the system to process larger-scale patient data and provide faster treatment decision support for clinicians. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is the main framework diagram of the present invention; Figure 2 is a system flow chart of the present invention; Figure 3 It is a schematic diagram of the computer device structure of the present invention.

[0023] 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

[0024] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0025] Please refer to the attached Figure 1 -Attached Figure 3 The embodiment of the present invention provides an interpretation system for precise drug use for tumors, the system comprising: A data collection module, which is used to collect genetic data, clinical data, and drug response data of tumor patients from multiple data sources; 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; Gene data analysis module, used to perform variation analysis based on patient genetic data, identify gene mutation sites and generate drug sensitivity analysis results; The drug screening module is used to screen drugs that match the patient's gene mutation site from the drug database based on the results of gene data analysis and provide drug dosage recommendations; Quantum optimization module, which is used to optimize the drug screening and dosage recommendation process, and improve the matching accuracy of drugs and genetic data through quantum algorithms; The report generation module is used to generate personalized drug treatment reports based on the patient's clinical medical history and drug response data.

[0026] The data acquisition module includes: A gene data collection unit, used to automatically collect the patient's gene test data from the gene test equipment, the data including gene mutation information, single nucleotide polymorphism sites and gene expression data; Clinical data collection unit, used to obtain the patient's 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.

[0027] 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 the patient data records and delete the records that are duplicated with the original data, so as to ensure that each patient data is unique and non-duplicate.

[0028] Gene data analysis modules include: A mutation identification unit is used to analyze the patient's genetic data, identify mutation sites, and generate a drug sensitivity analysis report 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.

[0029] The drug screening module includes: 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; The multi-objective optimization module is used to recommend drug dosage 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.

[0030] The quantum optimization module includes: The quantum variational algorithm unit is used to optimize the drug screening process based on the patient's gene mutation data and drug response history data through quantum computing methods, specifically determining the best match between the drug and the gene data through the quantum state minimization process; 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, the matching relationship between drug dosage and patient gene mutation data is optimized.

[0031] The report generation module includes: Personalized treatment report generation unit, which is used to generate detailed personalized treatment plans based on the patient's genetic data, clinical history and drug response data. The report content 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.

[0032] The system also includes: The patient feedback module is used to collect feedback data from patients after drug use, analyze the descriptions of drug efficacy, side effects and tolerability in the feedback data, and dynamically adjust drug screening and dosage recommendation plans based on the feedback data.

[0033] In this embodiment, the specific implementation of the data acquisition module is as follows: 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: The main task of the genetic data collection unit is to collect the patient's genetic test data from the genetic testing equipment. Generally, the genetic data includes the patient's gene mutation information, single nucleotide polymorphism (SNP) sites and gene expression data. In order to obtain data from the genetic testing equipment, the unit is usually connected to a genetic analysis instrument or a genome sequencing device to automatically transfer the sequencing data to the system for storage and further processing.

[0034] Specifically, the gene data collection unit can extract gene variation information from clinical gene test reports, including but not limited to: ; in, It refers to the single nucleotide polymorphism variation site, the insertion and deletion mutation involves the insertion or deletion of fragments in the gene sequence, and the copy number variation refers to the change in the copy number of the gene. Through this unit, the system can obtain multidimensional data of the patient's genome, providing data support for subsequent genetic analysis and drug sensitivity analysis.

[0035] 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, the 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: ; Among them, medical history includes the patient's past medical history, family medical history, etc.; treatment records include medications received, treatment plans, and efficacy feedback, etc.; examination results include imaging examinations, laboratory tests, etc.

[0036] In this implementation, the data collection module can be seamlessly connected with multiple hospital information management systems to achieve real-time data acquisition and ensure the efficiency and accuracy of the data collection process.

[0037] The main task of the drug response data collection unit is to collect the patient's response data after receiving specific drug treatment, including the drug's efficacy, side effects and drug resistance. By combining with the patient's follow-up system, this unit can continuously obtain the patient's side effects, drug resistance and efficacy information during the medication process. Through the patient's self-report or the doctor's feedback, the drug response data can be recorded in real time and uploaded to the system to provide feedback information for the drug screening module. This unit is particularly suitable for the collection of patient data for long-term follow-up to ensure that the patient's medication effect is fully monitored.

[0038] In a possible implementation, the data acquisition module also includes the following technical means: Data interface and protocol support: In order to achieve docking with various hospital information systems, genetic testing platforms and drug reaction databases, the data acquisition module supports a variety of data transmission protocols, such as HL7, FHIR and other standard data formats. These protocols allow efficient data exchange between the data acquisition module and different data sources. In the specific implementation, the data acquisition unit converts data in different formats into the system standard format through the API interface and data mapping mechanism to ensure data compatibility and operability.

[0039] Data encryption and privacy protection: Since it involves sensitive patient data, the data collection module needs to provide strict data encryption and privacy protection measures. During data transmission, the SSL / TLS encryption protocol is used to ensure the security of data during transmission. When storing data, high-intensity encryption algorithms such as AES-256 are used to encrypt patients' genetic data, clinical records, etc. to prevent data leakage.

[0040] Data cleaning and verification: During the data collection process, you may encounter missing data or inconsistent data formats. To this end, the data collection module has designed an automated verification mechanism to perform real-time checks on input data to ensure data integrity and consistency. For example, when missing sites are detected in genetic data, the system will automatically mark and prompt to ensure the quality of data collection.

[0041] The data collection module ensures the efficient operation of the entire precision medication interpretation system by automating and standardizing the collection of multi-dimensional data of patients. While realizing automated data collection, it greatly improves the accuracy and real-time nature of data collection by connecting with the hospital information system, genetic testing platform and drug response database, providing strong data support for subsequent genetic analysis and drug screening.

[0042] In this embodiment, the specific implementation of the data standardization processing module is as follows: In some embodiments, the data standardization processing module includes the following key units, which are responsible for different types of data processing and conversion tasks: Formatting unit: Generally, different data sources will 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 through a predetermined format, converting the original formats such as CSV, JSON, or XML into a unified standard data format within the system. For example, genetic data is usually stored in VCF format (variation call format) or FASTA format (gene sequence data format), and 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.

[0043] Specifically, the gene data fields include but are not limited to: gene name, mutation type, mutation site, mutation frequency, etc.; clinical data fields may include: patient name, age, gender, medical history, medication use record, etc.; drug reaction data fields may include: drug name, usage time, efficacy score, side effect record, etc. Through this module, all collected data can be stored in a unified format to ensure data availability.

[0044] Missing value filling unit: In some embodiments, the missing value filling unit is used to process missing parts in the data. Usually in genetic data or clinical data, some data may be missing due to experimental or recording problems. For this reason, the missing value filling unit uses different methods to fill. For example, the missing information in the genetic data may be supplemented by techniques such as mean filling, regression model prediction, or interpolation.

[0045] 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 " ", using regression analysis to fill it, you can use the following formula: ; in, is a regression function that fills in missing values ​​based on the existing genetic data and the genetic background of the patient. Similarly, when clinical data are missing, the mean filling method can be used: ; in, represents the clinical data record of a patient, is the data sample size, and the missing data are finally filled by the mean.

[0046] Redundant Data Removal Unit: During the data collection process, especially when multiple data sources are collected simultaneously, the existence of redundant data may affect the accuracy of the analysis results. The Redundant Data Removal Unit identifies and removes duplicate data by comparing the patient's unique identifier (such as patient ID, medical number, etc.).

[0047] For example, duplicate records may be generated between patient genetic data, clinical data, and drug response data due to system synchronization or multiple collections. At this time, 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: ; Ultimately, after redundant data is removed, only valid and unique patient records are saved in the system.

[0048] 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.

[0049] Specifically, if a record of a certain item in the clinical data (such as the 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 abnormal value. For example, body temperature data exceeding 42°C or below 28°C can be considered an abnormal value and needs to be processed or deleted. Abnormal value detection is achieved through the following formula: ; In a possible implementation, the data standardization processing module further includes the following features: Batch data processing: In some implementations, when a large amount of data needs to be processed, the data standardization processing module provides batch data processing functions. This means that when a large amount of genetic data, clinical data, and drug response data are collected into the system, formatting, missing value filling, redundant data removal, and other processing can be completed efficiently in batches, thereby avoiding manual operation errors and improving the automation and accuracy of data processing.

[0050] 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 the potential relationship between various types of data based on standardized data, and provide more information for subsequent precision drug use analysis.

[0051] In this embodiment, the specific implementation of the gene data analysis module is as follows: In some embodiments, the genetic data analysis module mainly includes two submodules: a variant identification unit and a drug sensitivity analysis unit. Specifically: Variant identification unit: The core of the genetic data analysis module is the variant identification unit. In general, this unit identifies mutation sites in the patient's genes by comparing them with a standard reference genome. This unit supports a variety of alignment algorithms, such as BWA (Burrows-Wheeler Aligner), Bowtie2, STAR, etc., to ensure efficient and accurate completion of the alignment task. Specifically, the sequences in the genetic data will be aligned to the reference genome, and the algorithm will identify the mutation types, such as single nucleotide polymorphisms (SNPs), insertion and deletion mutations (Indels), etc.

[0052] In one possible implementation, the gene data analysis module uses a gene alignment algorithm to mark variant sites and generate a variant list: ; in, Represents each mutation site in the gene data, each mutation site may correspond to different types of mutations, such as SNP, Indel, etc. Further, these mutation information will be converted into a standard data structure for subsequent analysis and used by the drug response prediction module.

[0053] 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, PharmGKB, etc., and combines known gene mutations and drug response data to evaluate the effectiveness and possible side effects of different drugs for patients. Through the relationship between gene mutation sites and drug targets, the drug sensitivity analysis unit can predict the patient's response to a specific drug.

[0054] Specifically, during the analysis process, the drug's target and the mutation site of the patient's gene are matched. If a drug's target is highly matched with the patient's mutation site, the drug is likely to be effective for the patient. The target match of the drug is calculated using the following formula: ; Among them, the higher the matching value, the better the match between the drug and the patient's gene mutation site, which may produce better therapeutic effects.

[0055] In some embodiments, the drug sensitivity analysis unit will also conduct a comprehensive assessment of the efficacy and side effects of the drug through historical clinical data to provide more accurate suggestions for the drug screening module.

[0056] Gene-drug reaction prediction: In addition to gene mutation information, the efficacy of drugs is also affected by other factors, such as the interaction between drugs and patients' metabolic genes. To this end, the genetic data analysis module will also combine the patient's metabolic genotype to predict the metabolic efficiency of drugs in the patient's body. This part of the analysis is based on the association between metabolic enzyme genes (such as CYP450 genes) and drug metabolic pathways. Through the analysis of metabolic pathways, the system can evaluate the metabolic rate of drugs and predict whether patients are likely to experience drug accumulation, overreaction, or drug ineffectiveness.

[0057] Specifically, the metabolic efficiency of a drug can be calculated by the following formula: ; 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.

[0058] In a possible implementation, the gene data analysis module further includes the following features: Multiple gene analysis function: In some embodiments, the gene data analysis module not only supports the analysis of a single gene, but also can handle the joint analysis of multiple genes. For example, the system can identify the "gene pathway" involving multiple mutation sites in the patient's genome and evaluate the efficacy of the entire pathway and the efficacy of the drug. For example, certain gene pathways (such as the PI3K / AKT pathway, the MAPK pathway, etc.) have an important impact on the proliferation and drug resistance of tumor cells. The system can evaluate the sensitivity of these pathways to drugs based on the patient's mutation information and provide support for subsequent drug screening.

[0059] Analysis of high-throughput genomic data: For the processing of genomic data, the gene data analysis module also supports batch processing of high-throughput genomic data. Through efficient computing architecture and optimization algorithms, the system can quickly process large-scale data from genome sequencing platforms, support whole-genome mutation analysis, and help doctors identify potential drug targets.

[0060] Result visualization: To facilitate clinicians to understand and use the results of genetic data analysis, the genetic data analysis module provides data visualization. Through charts, heat maps, etc., the results such as gene mutation sites and drug sensitivity scores are visualized to help doctors intuitively understand the relationship between the patient's genetic characteristics and drug response.

[0061] In this embodiment, the specific implementation of the drug screening module is as follows: In some embodiments, the drug screening module includes a drug target database, a drug screening algorithm, and a multi-objective optimization module. Specifically: Drug target database: In general, the drug screening module screens out drugs that match the patient's gene mutation site by docking with the drug target database. The drug target database contains a large amount of known drugs and their target information, such as DrugBank, PharmGKB and other databases, which record the known interactions between drugs and gene mutation sites.

[0062] In the specific implementation, the drug screening module extracts drug information related to the patient's gene mutation from the drug target database. Assuming that the patient has a mutation in the EGFR gene, the screening algorithm will search for anticancer drugs targeting EGFR from the database. The matching degree of the drug target can be calculated by the following formula: ; in, Indicates 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.

[0063] Drug screening algorithm, in some embodiments, the drug screening algorithm not only considers the matching relationship between gene mutations and drug targets, but also takes into account factors such as the pharmacological properties, clinical efficacy and side effects of the drug. 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's body, which can be calculated by the following formula: ; in, Indicates the score of a drug on a certain characteristic (such as efficacy, side effects, etc.). is the weight of the feature, The number of features considered in drug screening. By combining the scores of various features, the algorithm generates an efficacy score for each drug and ultimately recommends the most appropriate drug based on the score.

[0064] 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 based on multiple objectives such as drug efficacy, side effects, drug metabolism pathways, and patient genotype. The multi-objective optimization module considers multiple factors and selects the best solution from the drug candidate set through an optimization algorithm.

[0065] For example, suppose drug A scores higher in efficacy but has greater side effects, while drug B has fewer side effects but slightly worse efficacy. The multi-objective optimization module uses weighted calculations to ultimately select the balance point between side effects and efficacy and recommend the appropriate drug dosage for the patient. The optimization module can optimize the drug dosage using the following formula: ; in, , , They are the weights of efficacy, side effects and metabolic scores respectively. The system will adjust the weights according to the patient's actual situation to ensure that the most suitable drug and dosage are selected.

[0066] In a possible implementation, the drug screening module further includes the following features: Update and maintenance of the drug database: In some embodiments, the drug screening module supports real-time update and maintenance of the drug database. As new drug development and clinical trial data continue to increase, the drug information in the database will be updated regularly 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 them to the database, and update the screening results in a timely manner.

[0067] 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 the relevant parameters in the drug screening process according to the patient's basic characteristics to provide more accurate drug recommendations.

[0068] Feedback mechanism for historical drug response data: In some embodiments, the drug screening module also accesses the patient's historical drug response data. The historical drug response data can be updated in real time through the patient's follow-up information or drug feedback mechanism. By analyzing the patient's historical drug use data, the system can provide feedback on the efficacy and side effects of the drug to further optimize the accuracy of drug screening. The system will use the feedback data to adjust the drug screening algorithm and drug dosage recommendations to ensure that a dynamically optimized treatment plan is provided to the patient.

[0069] In this embodiment, the specific implementation of the quantum optimization module is as follows: In some embodiments, the quantum optimization module mainly optimizes the process of drug screening and dosage recommendation through quantum variational algorithm (VQE) and quantum approximate optimization algorithm (QAOA). Specifically: Quantum variational algorithm (VQE): Generally speaking, the quantum variational algorithm (VQE) is used to optimize the complex parameters in the drug screening process and minimize the matching error between drugs and genetic data through quantum computing. In the quantum optimization module, the VQE algorithm represents the characteristics of drugs and genetic data through quantum bits, and solves the optimal solution through iterative calculations. Specifically, the VQE algorithm will use quantum states to represent the patient's genetic mutation data and the drug's target, and then perform quantum calculations through quantum circuits to find the drug with the lowest matching degree and the optimal dose.

[0070] The optimization process of the quantum variational algorithm can be expressed as: ; in, represents the quantum Hamiltonian, is the quantum variational parameter, and the goal of minimizing is the error in matching genetic data with drugs. Through multiple iterations, the quantum circuit will give the optimal matching result between drugs and patient genetic data.

[0071] Quantum Approximate Optimization Algorithm (QAOA): The Quantum Approximate Optimization Algorithm (QAOA) is suitable for solving multi-objective optimization problems. The drug screening and dosage recommendation process involves the balance of multiple objectives, such as drug efficacy, side effects, metabolic rate, etc. The QAOA algorithm optimizes the dosage ratio of the drug by adjusting the state of quantum bits, so that multiple objectives are balanced. Specifically, QAOA constructs quantum circuits and uses the superposition state and interference effect of quantum bits to comprehensively optimize multiple drug objectives.

[0072] The optimization objective of QAOA can be expressed as: ; in, Indicates 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.

[0073] Calculation process of quantum optimization module: In some embodiments, the quantum optimization module converts the task of drug screening and dosage recommendation into a state update problem of quantum bits through multiple rounds of quantum calculations. During the calculation process, the quantum circuit continuously adjusts the parameters by combining with the classical computing algorithm to finally obtain the optimal drug and dosage matching result. The introduction of quantum computing effectively accelerates the process of drug screening and dosage recommendation, reduces the time required for calculation, and improves the accuracy of the results.

[0074] 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: ; in, Indicates The loss function of the optimization objective is 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.

[0075] In one possible implementation, the quantum optimization module also includes the following features: Allocation and scheduling of quantum computing resources: In some embodiments, the quantum optimization module uses dynamic allocation and scheduling technology of quantum computing resources to cope with complex drug screening and dosage optimization tasks. The allocation of quantum computing resources not only considers the computing power of quantum computing, but also dynamically adjusts the configuration of computing resources according to the complexity of the task and the number of optimization targets. Through flexible scheduling, the quantum optimization module can maximize the efficiency of quantum computing and ensure that the calculation is completed in the shortest time.

[0076] Quantum-classical hybrid computing: In general, although quantum computing has significant advantages in some aspects, there are still many computing problems that require the assistance of classical computers. Therefore, in some embodiments, the quantum optimization module adopts a quantum-classical hybrid computing method to combine quantum computing with classical computing. Classical computers are responsible for processing the parts that cannot be processed efficiently in quantum computing, while quantum computers are responsible for processing complex optimization problems. Through this hybrid computing method, the system can give full play to the respective advantages of quantum computing and classical computing, and improve computing efficiency and optimization accuracy.

[0077] In some embodiments, the report generation module generates a personalized treatment report mainly through the following functional units: Personalized treatment plan generation unit: In general, 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 that targets EGFR mutations and recommends a specific dose, the report will list the drug's dose, usage cycle and possible side effects in detail.

[0078] Specifically, the formula for generating a personalized treatment plan can be expressed as: ; 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 predictions are based on drug-gene matching, and side effect analysis integrates the known side effects of the drug and the patient's tolerance.

[0079] Side Effect and Efficacy Evaluation Unit: In some embodiments, the Side Effect and Efficacy Evaluation 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 records. Specifically, the side effects of a drug may be affected by factors such as gene mutations, age, and weight. Therefore, the unit generates a safety analysis of drug use by reviewing the patient's historical drug response data and combining it with a database of known side effects of the drug (such as the FDA drug side effect database).

[0080] For example, if a patient has a known allergic reaction to a drug, the unit will automatically flag this and offer an appropriate alternative medication. The results of the side effect analysis are expressed by the following formula: ; in, is the side effect risk scoring function, in which drug dose, patient genotype, and historical drug response data jointly affect the assessment of side effect risk.

[0081] Data visualization unit: In some embodiments, in order to help doctors quickly understand the drug recommendation results and treatment plans, the report generation module includes a data visualization unit. Through charts, heat maps, bar charts, etc., information such as treatment plans, drug side effects, and efficacy predictions are presented in a more intuitive way. For example, the side effects of a drug can be displayed through a heat map to show the effect of the drug on different gene mutations, and the efficacy of the drug can be displayed through 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.

[0082] Dynamic update unit for treatment plans; As an option, the report generation module also supports dynamic updates of treatment plans. Based on the patient's feedback data during the treatment process, the system can update the treatment plan in real time and adjust the drug dosage or replace the drug through an automated mechanism. For example, in the patient's feedback on the efficacy, if the drug is not effective or has serious side effects, the system will automatically update the recommended plan based on the feedback data and generate a new treatment report in time for the doctor's reference. The treatment plan update formula is as follows: ; 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.

[0083] In a possible implementation, the report generation module further includes the following features: Intelligent document generation and automation: In general, the report generation module also has intelligent document generation and automatic typesetting functions. The system can automatically generate standardized treatment reports based on preset templates and customize them according to the patient's specific data. For example, the drug recommendation section in the report will automatically display the drug that matches the patient, and present detailed information such as the drug name, dosage, medication time, efficacy prediction and side effects in a table. The automated generation of reports reduces human intervention and improves the efficiency of report generation.

[0084] Report multi-platform compatibility: In some embodiments, the report generation module can generate reports compatible with multiple platforms and formats, such as PDF, HTML, or Word format. This enables doctors and patients to view and print reports on different devices and operating systems. In addition, the system also supports the integration of treatment reports with the patient's electronic medical record (EMR) system or hospital information system (HIS) to ensure that reports can be easily stored, shared, and accessed.

[0085] 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.

[0086] Please refer to the attached Figure 2 The present invention also provides a method for interpreting precise drug use for tumors, comprising: Step 1: Collect genetic data, clinical data, and drug response data of tumor patients; Step 2: Standardize the genetic data, and compare and analyze the genetic data based on the genetic comparison algorithm; Step 3: Based on the results of gene variation analysis, 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 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.

[0087] 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 red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0088] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An interpretation system for precise drug use in tumors, characterized in that: include: A data collection module, which is used to collect genetic data, clinical data, and drug response data of tumor 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, for screening drugs that match the patient's gene mutation site from a drug database based on the gene data analysis results, and providing drug dosage recommendations; Quantum optimization module, which is used to optimize the drug screening and dosage recommendation process, and improve the matching accuracy of drugs and genetic data through quantum algorithms; The report generation module is used to generate personalized drug treatment reports based on the patient's clinical medical history and drug response data.

2. The interpretation system for accurate tumor medication according to claim 1, characterized in that: The data acquisition module comprises: A gene data collection unit, used to automatically collect the patient's gene test data from the gene test equipment, the data including gene mutation information, single nucleotide polymorphism sites and gene expression data; Clinical data collection unit, used to obtain the patient's 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 accurate tumor medication 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 the patient data records and delete the records that are duplicated with the original data, so as to ensure that each patient data is unique and non-duplicate.

4. The interpretation system for accurate tumor medication according to claim 1, characterized in that: The gene data analysis module includes: A mutation identification unit is used to analyze the patient's genetic data, identify mutation sites, and generate a drug sensitivity analysis report 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 accurate tumor medication according to claim 1, characterized in that: The drug screening module comprises: 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, wherein 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 dosage 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 accurate tumor medication according to claim 1, characterized in that: The quantum optimization module includes: The quantum variational algorithm unit is used to optimize the drug screening process based on the patient's gene mutation data and drug response history data through quantum computing methods, specifically determining the best match between the drug and the gene data through the quantum state minimization process; 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, the matching relationship between drug dosage and patient gene mutation data is optimized.

7. The interpretation system for accurate tumor medication according to claim 1, characterized in that: The report generation module includes: Personalized treatment report generation unit, which is used to generate detailed personalized treatment plans based on the patient's genetic data, clinical history and drug response data. The report content 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.

8. The interpretation system for accurate tumor medication 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.

9. A method for interpreting precise tumor medication, used to implement an interpreting system for precise tumor medication as claimed in any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Collect genetic data, clinical data, and drug response data of tumor patients; Step 2: standardize the gene data, and compare and analyze the gene data based on a gene comparison algorithm; Step 3: Based on the gene variation analysis results, screening out drugs that match the gene variation site and recommending drug dosages; Step 4: Generate a personalized drug treatment report and provide drug selection, dosage recommendations, expected efficacy and side effect analysis.

10. 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 drug use as described in claim 9.

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