Drug recommendation method, device, computer equipment and storage medium

Abstract

The present application belongs to the field of artificial intelligence and digital medicine, and relates to a drug recommendation method, including pre-processing medical record data and genetic data according to preset data standard specifications to obtain standard medical record data and standard genetic data, fusing the standard medical record data and standard genetic data through a health analysis model to make predictions to obtain health analysis results, and analyzing the health analysis results, standard medical record data and standard genetic data through a drug recommendation model to prepare a drug recommendation plan. The present application also provides a drug recommendation device, computer equipment and storage medium. In addition, the present application also involves blockchain technology, and medical record data and genetic data can be stored in the blockchain. The present application can improve the efficiency and accuracy of drug recommendations, significantly improve the personalization level of drug recommendations, make drug treatment more targeted and effective, and improve the safety of drug use for patients.

Description

Technical Field

[0001] The present application relates to the fields of artificial intelligence and digital medical technology, and in particular to a drug recommendation method, apparatus, computer equipment, and storage medium. Background Art

[0002] With the advancement of medical technology and bioinformatics, personalized medicine has gradually become an important means to improve treatment outcomes and patient satisfaction. Traditional drug recommendations rely primarily on physician experience and existing clinical guidelines, but this approach fails to fully consider each patient's unique circumstances. Genetic data and medical records can provide a wealth of individual information, but these data often suffer from heterogeneity and non-standardization. Due to the complexity and diversity of these data, the information dimensions they contain vary widely, making them difficult to directly apply to subsequent drug recommendation tasks. This, in turn, affects the comprehensive portrayal of the patient's health status, resulting in poor efficiency and accuracy in drug recommendations. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to propose a drug recommendation method, apparatus, computer device and storage medium to solve the technical problem of poor efficiency and accuracy of drug recommendation in the prior art.

[0004] In order to solve the above technical problems, the present invention provides a method for recommending drugs, which adopts the following technical solutions:

[0005] Obtaining medical record data and genetic data of a target patient, and preprocessing the medical record data and genetic data according to preset data standard specifications to obtain standard medical record data and standard genetic data;

[0006] Inputting the standard medical record data and the standard gene data into the trained health analysis model, and performing prediction by fusing the standard medical record data and the standard gene data to obtain a health analysis result;

[0007] The health analysis results, the standard medical record data and the standard gene data are input into the trained drug recommendation model to perform drug screening and drug effect prediction, and a drug recommendation plan is output.

[0008] Furthermore, the health analysis model includes a feature extraction layer, a medical record analysis layer, a gene analysis layer, and a fusion output layer. The step of inputting the standard medical record data and the standard gene data into the trained health analysis model and performing prediction by fusing the standard medical record data and the standard gene data to obtain a health analysis result includes:

[0009] Inputting the standard medical record data and the standard gene data into the feature extraction layer to perform feature extraction to obtain corresponding medical record data features and gene data features;

[0010] The medical record analysis layer fuses the medical record data features and the gene data features to perform analysis and prediction to obtain a disease analysis result;

[0011] The gene data features are analyzed and predicted by the gene analysis layer to obtain gene variation and drug effect results;

[0012] The disease analysis results and the gene variation and drug action results are input into the fusion output layer, and the health analysis results are obtained through the data fusion algorithm.

[0013] Furthermore, the gene analysis layer includes a gene identification sublayer and a drug efficacy analysis and prediction sublayer. The step of analyzing and predicting the gene data features by the gene analysis layer to obtain gene variation and drug effect results includes:

[0014] Comparing the gene sequences in the gene data features and identifying the mutation sites through the gene identification sublayer to obtain gene mutation information;

[0015] According to the gene variation information, obtaining drug response information corresponding to the gene variation information by querying a pre-built gene variation and drug response association database;

[0016] The gene variation information and the drug response information are input into the drug efficacy analysis prediction sublayer, and the gene variation information and the drug response information are jointly analyzed by a pre-trained large language model to obtain the gene variation and drug effect results.

[0017] Furthermore, the fusion output layer includes a feature fusion sublayer and a health prediction sublayer. The step of inputting the disease analysis results and the gene variation and drug effect results into the fusion output layer and obtaining the health analysis results through the data fusion algorithm includes:

[0018] The feature fusion sublayer uses multidimensional data fusion technology to fuse the disease analysis results and the gene variation and drug effect results to obtain fusion features;

[0019] The health prediction sublayer performs individual feature recognition and health analysis on the fusion features to obtain health analysis results.

[0020] Furthermore, the drug recommendation model includes a drug screening layer, a drug effect prediction layer, a recommendation result generation layer, and an explanation layer. The steps of inputting the health analysis results, the standard medical record data, and the standard gene data into the trained drug recommendation model for drug screening and drug effect prediction, and outputting a drug recommendation plan include:

[0021] The drug screening layer extracts key information and represents semantic vectors from the health analysis results, the standard medical record data, and the standard gene data to obtain drug semantic features, and obtains candidate drugs based on the drug semantic features;

[0022] By combining the candidate drugs with the drug effect prediction layer, the health analysis results, the standard medical record data and the standard gene data are used to extract key drug effect information to obtain drug efficacy and adverse reaction results;

[0023] The recommendation result generation layer is used to score the candidate drugs in combination with the drug efficacy and adverse reaction results to screen out the optimal drug combination;

[0024] The optimal drug combination is annotated with drug usage instructions through the explanation layer to obtain a drug recommendation plan.

[0025] Furthermore, the step of extracting key drug effect information from the health analysis results, the standard medical record data, and the standard gene data by combining the candidate drug with the drug effect prediction layer to obtain drug efficacy and adverse reaction results includes:

[0026] In the drug effect prediction layer, a condition semantic vector is obtained by extracting key features from the health analysis results, the standard medical record data, and the standard gene data;

[0027] Comparing the standard gene data with the gene database to obtain SNP information of key gene sites, and obtaining a drug effect feature vector based on the SNP information and the candidate drug;

[0028] The disease semantic vector and the drug effect feature vector are feature-fused to obtain drug efficacy and adverse reaction results.

[0029] Furthermore, after the step of outputting the drug recommendation plan, the method further includes:

[0030] Collecting feedback data from the target patient after using the recommended drug regimen, wherein the feedback data includes drug efficacy and adverse drug reactions;

[0031] The health analysis model and the drug recommendation model are updated according to the feedback data.

[0032] In order to solve the above technical problems, the present application also provides a drug recommendation device, which adopts the following technical solutions:

[0033] An acquisition module is used to obtain the medical record data and genetic data of the target patient, and pre-process the medical record data and genetic data according to preset data standard specifications to obtain standard medical record data and standard genetic data;

[0034] a health analysis module, configured to input the standard medical record data and the standard gene data into a trained health analysis model, and perform prediction by fusing the standard medical record data and the standard gene data to obtain a health analysis result;

[0035] The drug recommendation module is used to input the health analysis results, the standard medical record data and the standard gene data into the trained drug recommendation model to perform drug screening and drug effect prediction, and output a drug recommendation plan.

[0036] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0037] The computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the drug recommendation method described above when executing the computer-readable instructions.

[0038] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0039] The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the drug recommendation method described above.

[0040] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0041] The present application provides a drug recommendation method, which obtains standard medical record data and standard gene data by preprocessing medical record data and genetic data according to preset data standard specifications, which can reduce data noise and improve data quality. At the same time, the consistency of the data can speed up the calculation speed and improve the interpretability of data analysis; by fusing standard medical record data and standard gene data through a health analysis model for prediction, it can extract multi-dimensional data from different feature spaces, comprehensively obtain the characteristics that characterize the target patient, and at the same time, fuse data from different sources to eliminate redundancy and noise, and improve the accuracy and reliability of the analysis; by analyzing the health analysis results, standard medical record data and standard gene data through a drug recommendation model, it can improve the efficiency and accuracy of drug recommendations, significantly improve the personalization level of drug recommendations, make drug treatment more targeted and effective, and improve the safety of patient medication. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the solutions in this application, a brief introduction will be given below to the drawings required for use in the description of the embodiments of this application. Obviously, the drawings described below are some embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0043] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0044] Figure 2 is a flow chart of an embodiment of a drug recommendation method according to the present application;

[0045] Figure 3 yes Figure 2 A flowchart of a specific implementation of step S202;

[0046] Figure 4 is a schematic structural diagram of an embodiment of a drug recommendation device according to the present application;

[0047] Figure 5 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0049] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0050] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0051] like Figure 1 As shown, system architecture 100 may include a terminal device 101, a network 102, and a server 103. Terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. Network 102 is a medium for providing a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0052] The user can use the terminal device 101 to interact with the server 103 via the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0053] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer and a desktop computer, etc.

[0054] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0055] It should be noted that the drug recommendation method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the drug recommendation device is generally set in the server / terminal device.

[0056] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0057] Continue to refer Figure 2 , shows a flow chart of an embodiment of a drug recommendation method according to the present application, comprising the following steps:

[0058] Step S201 , obtaining the medical record data and gene data of the target patient, and preprocessing the medical record data and gene data according to preset data standard specifications to obtain standard medical record data and standard gene data.

[0059] In this embodiment, the electronic device (eg Figure 1 The server / terminal device shown in the figure can obtain the medical record data and genetic data of the target patient through a wired connection or a wireless connection. It should be noted that the above-mentioned wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0060] Based on the target patient's identity information, the target patient's detailed medical history data is obtained from the hospital's electronic medical record system, including past medical history, symptoms, diagnosis records, treatment records, medication use records, etc. Genetic data can be obtained from a standard gene database based on the target patient's identity information, or based on the target patient's gene sequencing data, the target patient's entire genome or specific gene sites are sequenced using high-throughput sequencing technology to obtain the corresponding genetic data. Among them, genetic data includes amino acid variation data, gene variation data, copy number variations (CNVs), and fusion structural variations. Amino acid variation data includes single nucleotide polymorphisms (SNPs), synonymous mutations, missense mutations, nonsense mutations, and stop codon mutations.

[0061] The acquired medical record data and genetic data are preprocessed according to preset data standard specifications, including: using a data cleaning algorithm to remove noise, outliers, missing values, etc. in the data to obtain cleaned medical record data and genetic data. For example, the Pandas library in Python is used to clean the data, remove features with missing values ​​exceeding 20%, and perform mean filling on the remaining missing values; according to the preset data standard specifications, the cleaned medical record data and genetic data are converted to convert unstructured data into structured data, and the data format is unified to obtain converted medical record data and genetic data; for the converted medical record data and genetic data, a data standardization algorithm is used to normalize the data to eliminate data dimension and data scale differences to obtain standardized medical record data and genetic data, i.e., standard medical record data and standard genetic data.

[0062] Specifically, outlier detection algorithms, such as the distance-based LOF algorithm, were used to remove noise and outliers from the data. Missing values ​​were interpolated using maximum likelihood estimation to obtain cleaned medical and genetic data. Data conversion was then performed on the cleaned medical and genetic data, converting the unstructured medical and genetic data into a structured XML format and the genetic data into a FASTA format. The converted medical and genetic data were then mapped to the [0, 1] interval using a min-max normalization algorithm to eliminate differences in data dimension and scale, resulting in standardized medical and genetic data.

[0063] It should be emphasized that in order to further ensure the privacy and security of medical records and genetic data, the above medical records and genetic data can also be stored in a blockchain node.

[0064] The blockchain referred to in this application is a new application model for computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Blockchain is essentially a decentralized database, a series of data blocks generated using cryptographic methods. Each data block contains information about a batch of network transactions, which is used to verify the validity of this information (to prevent counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, the platform product service layer, and the application service layer.

[0065] Step S202: input the standard medical record data and the standard gene data into the trained health analysis model, and perform prediction by fusing the standard medical record data and the standard gene data to obtain the health analysis result.

[0066] In this embodiment, the trained health analysis model extracts features from the standard medical record data and the standard gene data respectively to obtain medical record data features that characterize the clinical characteristics of the target patient and gene data features of key gene sites and gene expression patterns related to the health status of the target patient; the medical record data features and the gene data features are spliced ​​and fused to obtain health features that characterize the health of the target patient; by analyzing key information in the health features, such as disease type, course, severity, etc., the target patient's condition analysis results are obtained, which include clinical symptoms and signs, laboratory test results, treatment response data (including drug usage, dosage, treatment duration), treatment effect and treatment needs, etc.; Based on the characteristics, gene sequence alignment and variation detection are performed to identify disease-related gene variations. Based on known gene-drug interaction databases (such as PharmGKB and DrugBank), the impact of gene variations on drug response is analyzed to obtain drug response data. Combining gene variation and drug response data, the model reasoning capability is used to predict the target patient's sensitivity to different drugs and potential adverse reactions, namely, the results of gene variation and drug action. Finally, a large language model is used to integrate the disease analysis results and gene variation and drug action results to identify the overall health status and individual characteristics of the target patient, namely, the health analysis results. The health analysis results are used to illustrate potential health problems that may exist in the target patient and provide early warning information.

[0067] In some optional implementations, the health analysis model includes a feature extraction layer, a medical record analysis layer, a gene analysis layer, and a fusion output layer. Figure 3 As shown, the steps of inputting the standard medical record data and the standard gene data into the trained health analysis model and performing prediction by fusing the standard medical record data and the standard gene data to obtain the health analysis results include:

[0068] Step S301: input the standard medical record data and the standard gene data into the feature extraction layer for feature extraction to obtain corresponding medical record data features and gene data features.

[0069] Among them, the feature extraction layer includes a medical record feature extraction sublayer for extracting features from medical record data and a gene feature extraction sublayer for extracting features from gene data. The standard medical record data is input into the medical record feature extraction sublayer, and the standard gene data is input into the gene feature extraction sublayer for feature extraction to obtain the corresponding medical record data features and gene data features.

[0070] Specifically, the medical record feature extraction sublayer adopts a BERT model pre-trained based on data in the medical field, such as the BioBERT model or the ClinicalBERT model. For standard medical record data, it uses named entity recognition technology combined with medical dictionaries and knowledge graphs (such as UMLS and SNOMED CT) to identify and label key medical information such as symptoms, signs, examination results, and condition descriptions. By analyzing and extracting the labeled results, the specific content and attribute information of entities such as symptoms, signs, examination results, and condition descriptions are obtained, and the medical record data features can be obtained as output.

[0071] The gene feature extraction sublayer uses a sequence autoencoder based on a recursive neural network model, such as a long short-term memory network (LSTM) or a bidirectional long short-term memory network (Bi-LSTM), to extract gene data features through recursion and attention mechanisms.

[0072] Step S302: The medical record analysis layer fuses the medical record data features and the gene data features to perform analysis and prediction to obtain the disease analysis results.

[0073] In this embodiment, the medical record analysis layer includes a feature fusion sublayer and an analysis and prediction sublayer. The feature fusion sublayer uses a bidirectional long short-term memory network (Bi-LSTM) to capture the temporal dependency between medical record data features and genetic data features through LSTM units in both forward and backward directions to obtain a medical record analysis vector; the condition analysis prediction sublayer uses a pre-trained large language model (such as GPT-4, BERT) with its powerful language understanding and generation capabilities to analyze the medical record analysis vector, predict the target patient's condition development trend, and output the target patient's condition analysis results.

[0074] Step S303: Analyze and predict the gene data features through the gene analysis layer to obtain gene variation and drug effect results.

[0075] Among them, the gene analysis layer includes a gene recognition sublayer and a drug efficacy analysis prediction sublayer. Specifically, the gene recognition sublayer compares the gene sequences in the gene data features and identifies the mutation sites to obtain gene variation information; based on the gene variation information, the pre-constructed gene variation and drug response association database is queried to obtain the drug response information corresponding to the gene variation information; the gene variation information and drug response information are input into the drug efficacy analysis prediction sublayer, and the gene variation information and drug response information are jointly analyzed through the pre-trained large language model to obtain the gene variation and drug action results.

[0076] In this embodiment, the gene identification sublayer can use the GWAS analysis (Genome wide association study, whole genome association analysis) method to perform differential expression analysis at the whole genome level to determine the key SNP sites and disease-related genes in the genetic data characteristics that are related to the health status of the target patient, and obtain gene variation information.

[0077] Specifically, the BWA tool in the GWAS analysis method is used to align the sequencing reads in the genetic data features to the reference genome. The GATK tool in the GWAS analysis method is then used to detect variants, identify the gene mutations carried by the target patient, and obtain gene variation information. Based on a pre-constructed database containing 500 common gene variants and drug responses, drug response information corresponding to the gene variation information is obtained. Among them, the gene variation and drug response association database refers to a database that collects and integrates gene variation information and drug response data. These databases generally contain detailed information on how gene variation affects the pharmacokinetics and pharmacodynamics of drugs.

[0078] The drug efficacy analysis prediction sublayer utilizes a pre-trained large language model. This model is trained based on collected genetic variation data and corresponding drug response data, including drug pharmacokinetic and pharmacodynamic properties. During training, the model is continuously trained to identify patterns and associations between genetic variation and drug response. The resulting trained large language model is capable of reasoning about unknown genetic variation data and predicting its impact on drug response, ultimately deriving the association between genetic variation and drug action.

[0079] By jointly analyzing gene variation information and drug response information, combining gene variation with drug response can help doctors select drugs that are more likely to be effective in patients and avoid using drugs that may cause serious adverse reactions. It can also predict adverse reactions, take preventive measures in advance, and reduce patient risks, thereby reducing medical costs and improving treatment efficiency.

[0080] In step S304, the disease analysis results, gene variation, and drug action results are input into the fusion output layer, and the health analysis results are obtained through the data fusion algorithm.

[0081] In this embodiment, the fusion output layer includes a feature fusion sublayer and a health prediction sublayer. The feature fusion sublayer uses multidimensional data fusion technology to fuse disease analysis results, gene mutations, and drug effects to generate fused features. The health prediction sublayer then performs individual feature recognition and health analysis on the fused features to generate health analysis results.

[0082] Among them, the multidimensional data fusion technology can adopt convolutional neural network (CNN), encoder fusion method and attention mechanism-based fusion method, among which the encoder in the encoder fusion method can use BERT encoder or LSTM encoder.

[0083] The health prediction sublayer can use the BERT series model to analyze the fusion features and extract key features related to health prediction. These key features can reflect the health status of the individual; and analyze these key features to form a comprehensive health portrait of the target patient. Based on the comprehensive health portrait, the health status of the target patient is predicted in real time to obtain the health analysis results. The health analysis results include the overall health status and individual health status of the target patient. Among them, the overall health status can be a health score, disease risk prediction or other forms of analysis results. The individual health status is a personalized health indicator that characterizes the health status of the target patient, including physiological health indicators, living habits, nutritional status, chronic disease status, genetic and family medical history, immune status, drug sensitivity, etc.

[0084] In some optional implementations of this embodiment, the health analysis results are generated into a health analysis report and pushed to doctors and patients through the hospital's mobile APP. Doctors can view the patient's health status change curve and important gene site information to provide a reference for subsequent treatment plans; patients can view their own health risk levels and improvement suggestions, adjust their daily diet and living habits, and improve their health awareness.

[0085] By integrating the results of disease analysis with genetic variations and drug action results, it is possible to comprehensively analyze information from different data sources and dimensions, provide a more comprehensive health status assessment, improve diagnostic accuracy, help develop personalized treatment plans, and better manage patients' health status; it can predict possible diseases or health problems in the future, thereby achieving early intervention, reducing the need for expensive treatment and long-term care, and thus reducing overall medical costs.

[0086] This application uses a health analysis model to fuse standard medical record data and standard genetic data for prediction, which can extract multi-dimensional data from different feature spaces to obtain comprehensive characteristics of the target patients. At the same time, it fuses data from different sources to eliminate redundancy and noise, thereby improving the accuracy and reliability of health analysis results.

[0087] In step S203 , the health analysis results, standard medical record data, and standard gene data are input into the trained drug recommendation model to perform drug screening and drug effect prediction, and a drug recommendation plan is output.

[0088] In this embodiment, the drug recommendation model is based on a large language model, and uses natural language processing technology and deep learning algorithms to conduct in-depth analysis of health analysis results, standard medical record data, and standard genetic data to obtain a drug recommendation plan.

[0089] In some optional implementations, the drug recommendation model includes a drug screening layer, a drug effect prediction layer, a recommendation result generation layer, and an explanation layer. The steps of inputting the health analysis results, standard medical record data, and standard genetic data into the trained drug recommendation model for drug screening and drug effect prediction, and outputting a drug recommendation plan include:

[0090] Through the drug screening layer, key information is extracted and semantic vectorized from health analysis results, standard medical record data, and standard gene data to obtain drug semantic features, and candidate drugs are obtained based on drug semantic features;

[0091] By combining candidate drugs with the drug effect prediction layer, key information on drug effects is extracted from health analysis results, standard medical record data, and standard genetic data to obtain drug efficacy and adverse reaction results;

[0092] The recommendation result generation layer combines drug efficacy and adverse reaction results to score candidate drugs and screen out the optimal drug combination;

[0093] The explanation layer is used to mark the optimal drug combination with drug usage instructions to obtain a drug recommendation plan.

[0094] In this embodiment, the drug screening layer is used to extract disease-related drug semantic features based on health analysis results, standard medical record data, and standard genetic data. Matching candidate drugs are then screened from existing drug databases (such as DrugBank, PharmGKB, and RxNorm) based on these semantic features. Drug databases are online resources for storing and retrieving drug-related information, typically containing detailed information such as drug name, chemical structure, pharmacological action, indications, side effects, dosage, drug interactions, and drug category.

[0095] The drug effect prediction layer uses a large language model and machine learning algorithms to predict drug efficacy and adverse drug reactions based on candidate drugs, health analysis results, standard medical record data, and standard genetic data. Among them, drug efficacy includes therapeutic effect, therapeutic range, onset time, duration of efficacy, efficacy intensity, efficacy stability, etc.; adverse drug reactions include side effects, toxic effects, sequelae, allergic reactions, specific reactions, and drug interactions. In this embodiment, drug efficacy prediction can be predicted using an intelligent decision-making algorithm, and drug adverse reaction prediction can be predicted using a drug adverse reaction prediction model, which is trained based on an adverse drug reaction database (such as SIDER).

[0096] The recommendation result generation layer comprehensively scores each candidate drug based on the drug efficacy and adverse reaction results to obtain a scoring result. The optimal drug combination is selected based on the scoring result to ensure the effectiveness and safety of the recommended solution. For example, the scoring criteria and weights of various indicators such as drug efficacy, adverse drug reactions, and drug interactions are determined, where the distribution of weights should be determined based on clinical importance and treatment goals; based on the drug efficacy data, such as clinical trial results and drug concentration-effect relationships, the drug efficacy is evaluated and converted into a quantitative score to obtain a drug efficacy score; based on the drug adverse reaction data, including the type, frequency, and severity of adverse reactions, a quantitative score is performed to obtain an adverse reaction score; the possible interactions between candidate drugs or between candidate drugs and food, as well as the potential impact of these interactions on patient health, are evaluated and scored to obtain a drug effect score; based on the corresponding weights, the drug efficacy score, adverse reaction score, and drug effect score are weighted and summed to obtain the score result for each candidate drug.

[0097] The explanation layer can use a large language model to label the optimal drug combination with drug instructions, and generate detailed drug instructions and potential risk warnings for drug use. The drug instructions include the drug name, dosage, usage method, precautions, etc.; the potential risk warnings for drug use include possible adverse reactions, drug interactions, etc.

[0098] The generated drug recommendation plan is displayed visually to facilitate doctors and patients to understand and use the recommendation plan. Detailed information on drug recommendations and real-time consultation functions can also be provided through an interactive interface.

[0099] By generating drug recommendation plans through drug recommendation models, personalized treatment can be provided based on the patient's specific condition, genotype, past treatment response and other personalized factors, making drug treatment more targeted and effective; by accurately matching drugs and patients, the treatment effect can be improved, ineffective treatment and drug waste can be reduced, thereby reducing medical costs; at the same time, the occurrence of adverse reactions can be reduced, and the overall quality and efficiency of medical services can be improved.

[0100] In some optional implementations of this embodiment, the steps of extracting key drug effect information from health analysis results, standard medical record data, and standard gene data by combining the drug effect prediction layer with candidate drugs to obtain drug efficacy and adverse reaction results include:

[0101] In the drug effect prediction layer, key features are extracted from health analysis results, standard medical record data, and standard genetic data to obtain the disease semantic vector;

[0102] Compare standard gene data with gene databases to obtain SNP information of key gene sites, and obtain drug effect feature vectors based on SNP information and candidate drugs;

[0103] The disease semantic vector and drug effect feature vector are fused to obtain drug efficacy and adverse reaction results.

[0104] Specifically, key features such as medical history, symptoms, signs, auxiliary examinations, diagnosis, and treatment are extracted from health analysis results, standard medical records, and standard genetic data. These key features are converted into semantic vectors using word embedding algorithms such as Word2Vec to obtain a condition semantic vector. Simultaneously, the standard genetic data is compared with a standard genetic database to extract SNP information from 50 key gene loci related to drug metabolism, drug targets, and adverse drug reactions. These SNPs are then converted into a drug effect feature vector using one-hot encoding. Finally, the condition semantic vector and the drug effect feature vector are fused in a cascaded manner to obtain a patient feature vector. This patient feature vector is then input into a three-layer fully connected neural network model (FCNN), consisting of an input layer, a hidden layer, and an output layer. The FCNN predicts the target patient's response to each candidate drug based on the patient's characteristics. The response includes the efficacy and adverse reaction risk of each candidate drug for the patient, thereby obtaining the drug efficacy and adverse reaction results.

[0105] By predicting drug efficacy and adverse reactions, we can provide patients with more personalized treatment plans, improve drug treatment effects and medication safety, and reduce the risk of adverse reactions.

[0106] This application obtains standard medical record data and standard genetic data by pre-processing medical record data and genetic data according to preset data standard specifications, which can reduce data noise and improve data quality. At the same time, the consistency of data can speed up the calculation and improve the interpretability of data analysis. By fusing standard medical record data and standard genetic data through the health analysis model for prediction, multi-dimensional data from different feature spaces can be extracted to obtain comprehensive characteristics of the target patients. At the same time, data from different sources can be fused to eliminate redundancy and noise, thereby improving the accuracy and reliability of the analysis. By analyzing the health analysis results, standard medical record data and standard genetic data through the drug recommendation model, the personalization level of drug recommendations can be significantly improved, making drug treatment more targeted and effective, and improving the safety of drug use for patients.

[0107] In some optional implementations, after the above step of outputting the drug recommendation plan, the following steps are further included:

[0108] Collect feedback data from target patients after using the recommended drug regimen, including drug efficacy and adverse drug reactions;

[0109] Update health analysis models and drug recommendation models based on feedback data.

[0110] In this embodiment, after taking medication according to the recommended medication plan, the patient will provide feedback data to the hospital in real time. Based on the feedback data, the health analysis model and drug recommendation model are retrained and optimized to continuously improve the accuracy and personalization of the recommendations.

[0111] In this embodiment, the patient's historical medical record data and historical genetic data are collected, the historical medical record data are preprocessed to obtain standard historical medical record data, the historical genetic data are preprocessed, encoded and standardized, and low-quality and repeated sequences are removed to obtain standard historical genetic data; natural language processing technologies such as regular expressions and bag-of-words models are used to extract key medical concepts and clinical events from the standard historical medical record data to obtain medical record data features, and structured representation is performed to construct the patient's electronic medical record knowledge graph; sequence alignment and variation detection are performed on the standard historical genetic data to identify mutation sites and variation types in the patient's genome to obtain genetic variation data; the electronic medical record knowledge graph and genetic variation data are integrated to construct health feature data for each patient; the pre-constructed health analysis model and drug recommendation model are trained using the health feature data to obtain the final health analysis model and drug recommendation model that have been trained and meet the requirements.

[0112] To verify whether the model meets the requirements, the three evaluation indicators commonly used in machine learning can be used to evaluate the model's prediction results: precision P (Precision), recall R (Recall) and F-score. When the precision P, recall R and F-score meet the preset thresholds, it means that the model meets the requirements; otherwise, the health analysis model and drug recommendation model are retrained.

[0113] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0114] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0115] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware via computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0116] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0117] Further references Figure 4 , as a response to the above Figure 2In order to realize the method shown in the figure, the present application provides an embodiment of a drug recommendation device. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0118] like Figure 4 As shown, the drug recommendation device 400 of this embodiment includes: an acquisition module 401, a health analysis module 402 and a drug recommendation module 403.

[0119] The acquisition module 401 is used to obtain the medical record data and gene data of the target patient, and pre-process the medical record data and gene data according to the preset data standard specification to obtain standard medical record data and standard gene data;

[0120] The health analysis module 402 is used to input the standard medical record data and the standard gene data into the trained health analysis model, and perform prediction by fusing the standard medical record data and the standard gene data to obtain a health analysis result;

[0121] The drug recommendation module 403 is used to input the health analysis results, the standard medical record data and the standard gene data into the trained drug recommendation model to perform drug screening and drug effect prediction, and output a drug recommendation plan.

[0122] It should be emphasized that in order to further ensure the privacy and security of medical records and genetic data, the above medical records and genetic data can also be stored in a blockchain node.

[0123] Based on the above-mentioned drug recommendation device 400, by pre-processing the medical record data and genetic data according to the preset data standard specifications, standard medical record data and standard genetic data are obtained, which can reduce data noise and improve data quality. At the same time, the consistency of the data can speed up the calculation speed and improve the interpretability of data analysis; by fusing the standard medical record data and standard genetic data through the health analysis model for prediction, multi-dimensional data from different feature spaces can be extracted to obtain comprehensive characteristics of the target patients. At the same time, data from different sources are fused to eliminate redundancy and noise, thereby improving the accuracy and reliability of the analysis; by analyzing the health analysis results, standard medical record data and standard genetic data through the drug recommendation model, the personalization level of drug recommendations can be significantly improved, making drug treatment more targeted and effective, and improving the safety of patient medication.

[0124] In some optional implementations, the health analysis model includes a feature extraction layer, a medical record analysis layer, a gene analysis layer, and a fusion output layer. The health analysis module 402 includes:

[0125] A feature extraction submodule is used to input the standard medical record data and the standard gene data into the feature extraction layer to perform feature extraction, thereby obtaining corresponding medical record data features and gene data features;

[0126] The condition analysis submodule is used to analyze and predict the condition by fusing the medical record data features and the gene data features through the medical record analysis layer to obtain the condition analysis results;

[0127] A gene efficacy prediction submodule is used to analyze and predict the gene data characteristics through the gene analysis layer to obtain gene variation and drug effect results;

[0128] The fusion output submodule is used to input the disease analysis results and the gene variation and drug action results into the fusion output layer, and obtain the health analysis results through the data fusion algorithm.

[0129] By integrating standard medical record data and standard genetic data for prediction through health analysis models, we can extract multi-dimensional data from different feature spaces and obtain comprehensive characteristics of the target patients. At the same time, we can fuse data from different sources to eliminate redundancy and noise, and improve the accuracy and reliability of health analysis results.

[0130] In some optional implementations of this embodiment, the gene analysis layer includes a gene identification sublayer and a drug efficacy analysis and prediction sublayer, and the gene drug efficacy prediction submodule is further used to:

[0131] Comparing the gene sequences in the gene data features and identifying the mutation sites through the gene identification sublayer to obtain gene mutation information;

[0132] According to the gene variation information, obtaining drug response information corresponding to the gene variation information by querying a pre-built gene variation and drug response association database;

[0133] The gene variation information and the drug response information are input into the drug efficacy analysis prediction sublayer, and the gene variation information and the drug response information are jointly analyzed by a pre-trained large language model to obtain the gene variation and drug effect results.

[0134] By jointly analyzing gene variation information and drug response information, combining gene variation with drug response can help doctors select drugs that are more likely to be effective in patients and avoid using drugs that may cause serious adverse reactions. It can also predict adverse reactions, take preventive measures in advance, and reduce patient risks, thereby reducing medical costs and improving treatment efficiency.

[0135] In some optional implementations of this embodiment, the fusion output layer includes a feature fusion sublayer and a health prediction sublayer, and the fusion output submodule is further used to:

[0136] The feature fusion sublayer uses multidimensional data fusion technology to fuse the disease analysis results and the gene variation and drug effect results to obtain fusion features;

[0137] The health prediction sublayer performs individual feature recognition and health analysis on the fusion features to obtain health analysis results.

[0138] By integrating the results of disease analysis with genetic variations and drug action results, it is possible to comprehensively analyze information from different data sources and dimensions, provide a more comprehensive health status assessment, improve diagnostic accuracy, help develop personalized treatment plans, and better manage patients' health status; it can predict possible diseases or health problems in the future, thereby achieving early intervention, reducing the need for expensive treatment and long-term care, and thus reducing overall medical costs.

[0139] In some optional implementations, the drug recommendation model includes a drug screening layer, a drug effect prediction layer, a recommendation result generation layer, and an explanation layer. The drug recommendation module 403 includes:

[0140] a drug screening submodule, configured to extract key information and perform semantic vector representation on the health analysis results, the standard medical record data, and the standard gene data through the drug screening layer to obtain drug semantic features, and obtain candidate drugs based on the drug semantic features;

[0141] A drug effect prediction submodule is configured to extract key drug effect information from the health analysis results, the standard medical record data, and the standard gene data by combining the candidate drug with the drug effect prediction layer to obtain drug efficacy and adverse reaction results;

[0142] A scoring submodule is used to score the candidate drugs by combining the drug efficacy and adverse reaction results through the recommendation result generation layer to screen out the optimal drug combination;

[0143] The marking submodule is used to mark the optimal drug combination with drug usage instructions through the explanation layer to obtain a drug recommendation plan.

[0144] By generating drug recommendation plans through drug recommendation models, personalized treatment can be provided based on the patient's specific condition, genotype, past treatment response and other personalized factors, making drug treatment more targeted and effective; by accurately matching drugs and patients, the treatment effect can be improved, ineffective treatment and drug waste can be reduced, thereby reducing medical costs; at the same time, the occurrence of adverse reactions can be reduced, and the overall quality and efficiency of medical services can be improved.

[0145] In some optional implementations of this embodiment, the drug effect prediction submodule is further configured to:

[0146] In the drug effect prediction layer, a condition semantic vector is obtained by extracting key features from the health analysis results, the standard medical record data, and the standard gene data;

[0147] Comparing the standard gene data with the gene database to obtain SNP information of key gene sites, and obtaining a drug effect feature vector based on the SNP information and the candidate drug;

[0148] The disease semantic vector and the drug effect feature vector are feature-fused to obtain drug efficacy and adverse reaction results.

[0149] By predicting drug efficacy and adverse reactions, we can provide patients with more personalized treatment plans, improve drug treatment effects and medication safety, and reduce the risk of adverse reactions.

[0150] In some optional implementations, the drug recommendation device 400 further includes an updating module configured to:

[0151] Collecting feedback data from the target patient after using the recommended drug regimen, wherein the feedback data includes drug efficacy and adverse drug reactions;

[0152] The health analysis model and the drug recommendation model are updated according to the feedback data.

[0153] Based on feedback data, the health analysis model and drug recommendation model are retrained and optimized to continuously improve the accuracy and personalization of recommendations.

[0154] To solve the above technical problems, the present application also provides a computer device. Figure 5 , Figure 5 This is a basic structural block diagram of the computer device in this embodiment.

[0155] The computer device 5 includes a memory 51, a processor 52, and a network interface 53 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 5 with a memory 51, a processor 52, and a network interface 53, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0156] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0157] The memory 51 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 51 can be an internal storage unit of the computer device 5, such as the hard disk or memory of the computer device 5. In other embodiments, the memory 51 can also be an external storage device of the computer device 5, such as a plug-in hard disk equipped on the computer device 5, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. Of course, the memory 51 can also include both the internal storage unit of the computer device 5 and its external storage device. In this embodiment, the memory 51 is generally used to store the operating system and various application software installed on the computer device 5, such as computer-readable instructions for the drug recommendation method. In addition, the memory 51 can also be used to temporarily store various types of data that have been output or are to be output.

[0158] In some embodiments, the processor 52 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 52 is generally used to control the overall operation of the computer device 5. In this embodiment, the processor 52 is used to execute computer-readable instructions or process data stored in the memory 51, such as computer-readable instructions for executing the drug recommendation method.

[0159] The network interface 53 may include a wireless network interface or a wired network interface. The network interface 53 is generally used to establish a communication connection between the computer device 5 and other electronic devices.

[0160] By preprocessing medical record data and genetic data according to preset data standards and specifications, standard medical record data and standard genetic data are obtained, which can reduce data noise and improve data quality. At the same time, the consistency of the data can speed up the calculation and improve the interpretability of data analysis. By integrating standard medical record data and standard genetic data into the health analysis model for prediction, multi-dimensional data from different feature spaces can be extracted to obtain comprehensive characteristics of the target patients. At the same time, data from different sources can be integrated to eliminate redundancy and noise, thereby improving the accuracy and reliability of the analysis. By analyzing the health analysis results, standard medical record data and standard genetic data through the drug recommendation model, the level of personalization of drug recommendations can be significantly improved, making drug treatment more targeted and effective, and improving the safety of drug use for patients.

[0161] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the drug recommendation method as described above.

[0162] By preprocessing medical record data and genetic data according to preset data standards and specifications, standard medical record data and standard genetic data are obtained, which can reduce data noise and improve data quality. At the same time, the consistency of the data can speed up the calculation and improve the interpretability of data analysis. By integrating standard medical record data and standard genetic data into the health analysis model for prediction, multi-dimensional data from different feature spaces can be extracted to obtain comprehensive characteristics of the target patients. At the same time, data from different sources can be integrated to eliminate redundancy and noise, thereby improving the accuracy and reliability of the analysis. By analyzing the health analysis results, standard medical record data and standard genetic data through the drug recommendation model, the level of personalization of drug recommendations can be significantly improved, making drug treatment more targeted and effective, and improving the safety of drug use for patients.

[0163] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0164] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. A drug recommendation method, characterized in that: The steps include: Obtaining medical record data and genetic data of a target patient, and preprocessing the medical record data and genetic data according to preset data standard specifications to obtain standard medical record data and standard genetic data; Inputting the standard medical record data and the standard gene data into a trained health analysis model, and performing prediction by fusing the standard medical record data and the standard gene data to obtain a health analysis result; the health analysis model includes a feature extraction layer, a medical record analysis layer, a gene analysis layer, and a fusion output layer; Inputting the health analysis results, the standard medical record data, and the standard gene data into the trained drug recommendation model to perform drug screening and drug effect prediction, and outputting a drug recommendation plan; The step of inputting the standard medical record data and the standard gene data into the trained health analysis model, and performing prediction by fusing the standard medical record data and the standard gene data to obtain a health analysis result comprises: Inputting the standard medical record data and the standard gene data into the feature extraction layer to perform feature extraction to obtain corresponding medical record data features and gene data features; The medical record analysis layer fuses the medical record data features and the gene data features to perform analysis and prediction to obtain a disease analysis result; The gene data features are analyzed and predicted by the gene analysis layer to obtain gene variation and drug effect results; The disease analysis results and the gene variation and drug action results are input into the fusion output layer, and the health analysis results are obtained through the data fusion algorithm.

2. The drug recommendation method according to claim 1, characterized in that: The gene analysis layer includes a gene identification sublayer and a drug efficacy analysis and prediction sublayer. The steps of analyzing and predicting the gene data features by the gene analysis layer to obtain gene variation and drug effect results include: Comparing the gene sequences in the gene data features and identifying the mutation sites through the gene identification sublayer to obtain gene mutation information; According to the gene variation information, obtaining drug response information corresponding to the gene variation information by querying a pre-built gene variation and drug response association database; The gene variation information and the drug response information are input into the drug efficacy analysis prediction sublayer, and the gene variation information and the drug response information are jointly analyzed by a pre-trained large language model to obtain the gene variation and drug effect results.

3. The drug recommendation method according to claim 1, characterized in that: The fusion output layer includes a feature fusion sublayer and a health prediction sublayer. The step of inputting the disease analysis results and the gene variation and drug effect results into the fusion output layer and obtaining the health analysis results through the data fusion algorithm includes: The feature fusion sublayer uses multidimensional data fusion technology to fuse the disease analysis results and the gene variation and drug effect results to obtain fusion features; The health prediction sublayer performs individual feature recognition and health analysis on the fusion features to obtain health analysis results.

4. The drug recommendation method according to claim 1, characterized in that: The drug recommendation model includes a drug screening layer, a drug effect prediction layer, a recommendation result generation layer, and an explanation layer. The steps of inputting the health analysis results, the standard medical record data, and the standard gene data into the trained drug recommendation model for drug screening and drug effect prediction, and outputting a drug recommendation plan include: The drug screening layer extracts key information and represents semantic vectors from the health analysis results, the standard medical record data, and the standard gene data to obtain drug semantic features, and obtains candidate drugs based on the drug semantic features; By combining the candidate drugs with the drug effect prediction layer, the health analysis results, the standard medical record data and the standard gene data are used to extract key drug effect information to obtain drug efficacy and adverse reaction results; The recommendation result generation layer is used to score the candidate drugs in combination with the drug efficacy and adverse reaction results to screen out the optimal drug combination; The optimal drug combination is annotated with drug usage instructions through the explanation layer to obtain a drug recommendation plan.

5. The drug recommendation method according to claim 4, characterized in that: The step of extracting key drug effect information from the health analysis results, the standard medical record data, and the standard gene data by combining the candidate drug with the drug effect prediction layer to obtain drug efficacy and adverse reaction results includes: In the drug effect prediction layer, a condition semantic vector is obtained by extracting key features from the health analysis results, the standard medical record data, and the standard gene data; Comparing the standard gene data with the gene database to obtain SNP information of key gene sites, and obtaining a drug effect feature vector based on the SNP information and the candidate drug; The disease semantic vector and the drug effect feature vector are feature-fused to obtain drug efficacy and adverse reaction results.

6. The drug recommendation method according to claim 1, characterized in that: After the step of outputting the drug recommendation scheme, the method further includes: Collecting feedback data from the target patient after using the recommended drug regimen, wherein the feedback data includes drug efficacy and adverse drug reactions; The health analysis model and the drug recommendation model are updated according to the feedback data.

7. A drug recommendation device, characterized in that: include: An acquisition module is used to obtain the medical record data and genetic data of the target patient, and pre-process the medical record data and genetic data according to preset data standard specifications to obtain standard medical record data and standard genetic data; A health analysis module, configured to input the standard medical record data and the standard gene data into a trained health analysis model, and to perform prediction by fusing the standard medical record data and the standard gene data to obtain a health analysis result; the health analysis model comprises a feature extraction layer, a medical record analysis layer, a gene analysis layer, and a fusion output layer; A drug recommendation module is used to input the health analysis results, the standard medical record data, and the standard gene data into the trained drug recommendation model to perform drug screening and drug effect prediction, and output a drug recommendation plan; The health analysis module includes: A feature extraction submodule is used to input the standard medical record data and the standard gene data into the feature extraction layer to perform feature extraction, thereby obtaining corresponding medical record data features and gene data features; The condition analysis submodule is used to analyze and predict the condition by fusing the medical record data features and the gene data features through the medical record analysis layer to obtain the condition analysis results; A gene efficacy prediction submodule is used to analyze and predict the gene data characteristics through the gene analysis layer to obtain gene variation and drug effect results; The fusion output submodule is used to input the disease analysis results and the gene variation and drug action results into the fusion output layer, and obtain the health analysis results through the data fusion algorithm.

8. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the drug recommendation method according to any one of claims 1 to 6 when executing the computer-readable instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the drug recommendation method according to any one of claims 1 to 6.

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