1458 results about "Drug utilisation" patented technology

The invention relates to the field of antitumor drugs, in particular to a pharmaceutical composition of a KRAS G12C inhibitor and Ly2090314 and application of the pharmaceutical composition. According to the invention, the Ly2090314 and the KRAS G12C inhibitor are combined to play a synergistic effect, a DNA damage repair pathway is inhibited, a better inhibition effect is generated on tumor cells containing KRAS G12C mutation, the dosage required for achieving the same anti-tumor effect is greatly reduced, and the medication safety is improved.

The invention discloses an AI-based traditional Chinese medicine dialectical auxiliary diagnosis method and system and a medium thereof, and relates to the technical field of intelligent auxiliary diagnosis, and the method comprises the steps: collecting physiological monitoring data, tongue condition data and pulse condition signals of a patient, obtaining patient symptom information, mapping the patient symptom information to a preset traditional Chinese medicine pathogenesis classification model, and generating an initial symptom feature vector; calling a knowledge base containing traditional Chinese medicine prescription rules and compatibility medication taboo, performing data space-time alignment processing and dynamic combination calculation on the initial symptom feature vector, and generating a personalized candidate prescription set conforming to the traditional Chinese medicine compatibility taboo; according to dynamic combination calculation, a solution algorithm based on a constraint satisfaction problem is adopted, and multi-dimensional association rule matching is carried out in combination with the mapping relation between syndromes and modern medical symptoms; and outputting a prescription medication recommendation scheme containing traditional Chinese medicine compatibility, dosage and decoction methods based on the candidate prescription set. The method supports the formulation of an accurate personalized diagnosis and treatment scheme, and meets the requirements of high-quality traditional Chinese medicine personalized auxiliary diagnosis and treatment.

The invention discloses a patient medication risk early warning method, and relates to the technical field of medication risk evaluation. The method comprises the following steps: acquiring multi-dimensional data such as age, past medical history, allergy history, clinical examination data and prescription data of a patient, extracting chronic disease diagnosis information from the multi-dimensional data, and carrying out standardized coding to generate a common disease combination tag; calling a preset disease weight system, and calculating a common disease severity score in combination with an age factor; extracting metabolism-related indexes to construct a drug metabolism capability feature vector of the patient; mapping the drug combination, the co-disease tag and the metabolic feature vector to a clinical drug knowledge base to generate a comprehensive conflict detection result; and generating corresponding early warning information based on the conflict detection result and a preset early warning rule base, wherein the rule base comprises a mapping relationship between the conflict type and the early warning level. According to the method, drug use risk early warning can be realized according to the multi-disease coexistence state of the patient, and the drug use safety is improved.

The invention relates to the technical field of tumor treatment, in particular to an intelligent decision-making system for tumor precise targeted medication based on individual mutation information, which comprises a data processing layer, a variation annotation and function prediction layer, a knowledge base integration layer and a scheme decision-making engine and report visualization module. According to the intelligent decision-making system for tumor precise targeted medication based on individual mutation information, a rule engine and a prediction model are combined, dynamic priority ranking is output, multi-model fusion decision making is achieved, and clinical scene deep adaptation is achieved by predicting primary and secondary drug resistance, calculating liver and kidney function adjusting dosage and generating a combined medication time sequence scheme; through an individualized drug delivery scheme, combination drug use optimization is achieved, a visual clinical report is generated, clinical executable operation is further strengthened, and through algorithm quantification, a dynamic knowledge graph, AI auxiliary decision making and a clinical operation closed loop, the next-generation technical research direction of a tumor precise drug use system can be represented.

The invention relates to the technical field of medical information, in particular to a hospital pharmacy dispensing information intelligent processing method and system, and the method comprises the following steps: S1, obtaining the basic data of a medicine in real time; s2, acquiring medical record information, current medication conditions and physiological parameters of the patient from a hospital information system; s3, analyzing an existing drug combination, and detecting potential drug incompatibility; s4, a medication scheme of the patient is automatically generated; s5, pushing the medication scheme and the taboo information generated in the step S4 to work terminals of doctors and pharmacists, and providing medication suggestions at the same time; and S6, continuously monitoring the reaction of the patient, and when abnormity is monitored, performing drug adjustment through an adjustment algorithm. According to the system and the method, the medicine data is updated in real time, the patient information is integrated, and incompatibility identification and medication adjustment are carried out by utilizing an intelligent algorithm, so that the safety and the efficiency of hospital pharmaceutical dispensing are remarkably improved, and meanwhile, the individuation and the scientificity of a treatment scheme are optimized.

The invention provides a multi-source medical outpatient service data comprehensive management method and platform based on RPA, and the method comprises the steps: S1, designing a script based on robot process automation, and carrying out the data collection and storage of multiple systems; s2, constructing a knowledge graph # imgabs0 # according to the structured data set; s3, introducing a causal inference technology into the knowledge graph # imgabs1 #; s4, according to the abnormal mark knowledge graph # imgabs2, generating personalized medicine use optimization suggestions through joint learning of a graph structure and node attributes; and S5, implementing closed-loop management by executing optimization suggestions and dynamically updating system rules. According to the method, the defects of a traditional method in the aspects of data integration, drug use anomaly detection and optimization suggestion generation can be overcome.

The invention discloses a drug traceability code acquisition method, system and device based on image recognition, and a medium. The method comprises the steps of obtaining a drug package image; preprocessing the medicine packaging image, and performing edge detection, region segmentation, image correction and barcode analysis on the preprocessed medicine packaging image to obtain medicine traceability code information; obtaining prescription data, and checking the drug traceability code information according to the prescription data; and when the drug traceability code information is checked to be correct, carrying out associative storage on the drug traceability code information and the prescription data. According to the invention, drug traceability code information can be rapidly and accurately identified, and environments such as complex backgrounds, illumination changes, traceability code deformation and the like can be adapted; meanwhile, multiple drug traceability codes can be collected, the purpose of batch collection is achieved, supervision of a drug dispensing end is achieved, medication safety of patients is ensured, drug management can be perfected, and subsequent drug traceability is facilitated.

The invention discloses a multi-modal fusion-based traditional Chinese medicine recommendation method, system and device and a medium, and the method comprises the steps: constructing a heterogeneous graph according to a core representative object and preprocessed traditional Chinese medicine multi-modal data; inputting the heterogeneous graph into a graph neural network for data fusion to obtain traditional Chinese medicine multi-modal fusion data; constructing a knowledge graph based on the traditional Chinese medicine multi-modal fusion data to obtain a traditional Chinese medicine knowledge graph; segmenting the traditional Chinese medicine knowledge graph into sub-graphs processed by multiple agents according to the drug effect and the drug use safety, and obtaining a medicinal material sub-graph associated with a drug effect agent and a safety sub-graph associated with a safety agent; based on the medicinal material sub-graph and the safety sub-graph, collaborative iteration optimization is carried out through a medicinal material combination strategy and a medicinal material safety checking strategy to select medicinal materials, and an optimal compound recommended by the traditional Chinese medicine is obtained; and inputting the optimal compound into the fine-tuned large language model, and determining a target compound recommended by the traditional Chinese medicine. The accuracy of traditional Chinese medicine recommendation can be improved.

The invention provides a chronic disease co-patient accurate medication real-time management system based on an AI large model, and the system comprises accurate medication guided by the AI large model, through the mathematical modeling and simulation technology, the related information of patients, medicines and diseases is integrated, and accurate medication is guided. According to the method, the administration scheme can be formulated more accurately, the curative effect is improved, side effects are reduced, a patient medication report is provided for physicians and pharmacists, the physicians and pharmacists can dynamically master patient medication information in real time, reference is provided for the physicians to formulate the next medication scheme, and the treatment effect on chronic disease patients is improved.

The invention discloses a tumor patient medication side effect management method and system based on big data analysis, and the method comprises the following steps: collecting and preprocessing patient data and drug data, and extracting patient features and drug features, the patient data comprising genome data provided by a patient; constructing a prediction model which comprises a time sequence analysis sub-model, a gene-drug interaction analysis sub-model and a risk score generation sub-model and is used for predicting risk scores of different types of side effects of the patient by taking the drug characteristics and the patient characteristics as input of the prediction model; inputting the basic information, the medication information and the genome data of the current patient into the trained prediction model to obtain risk assessment of various side effects of the current patient; according to the risk assessment result, personalized adjustment suggestions suitable for the patient are selected from a preset intervention scheme library and provided for the current patient. The scientificity and accuracy of administration side effect management in the treatment stage can be effectively improved.

The present invention relates to the field of medical artificial intelligence technology, and discloses a pediatric procedural sedation medication regimen recommendation system based on multi-objective optimization, comprising: a data acquisition and preprocessing module, which is used to acquire multi-source heterogeneous clinical data and perform data cleaning and feature extraction to generate a multi-source heterogeneous clinical feature set; a multi-objective optimization decision module, which is used to establish a number of objective functions, and adopt an improved multi-objective optimization method to optimize and solve each objective function to obtain an optimal medication regimen; a medication regimen module, which is used to automatically generate a specific medication regimen based on a scheme generation method with time constraints according to the optimal medication regimen; a safety inspection module, which is used to perform a multi-level safety review on a specific medication regimen, and obtain a multi-level safety review result by calculating a feasibility judgment function through risk assessment; the present invention improves the scientificity and reliability of the medication regimen, so that each patient child can obtain a sedation regimen that best suits his or her individual characteristics.

To generate appropriate candidates for a medication history to perform appropriate medication counseling.SOLUTION: A medication history generation support system comprises: a medication data storage unit 32 that stores prescription data and medication history data in association with each other as medication data for every patient and every clinical department; an extraction unit 34 that, on the basis of newly obtained patient's prescription data, extracts, from the medication data, prescription data in the same clinical department as the newly obtained patient's prescription data and medication history data in the same clinical department as the newly obtained patient; a conversation text generation unit 13 that generates text data of a conversation with the patient on the basis of voice data of the conversation; and a medication history data generation unit 16 that causes a generative AI to generate medication history data corresponding to the newly obtained patient's prescription data, on the basis of the extracted prescription data and medication history data, the generated text data, and prompt text data for generating the medication history data.SELECTED DRAWING: Figure 1

The invention belongs to the technical field of medical informatization, and provides an anesthesia medication management system with biological recognition, comprising: a biological recognition module verifies the identities of medical staff and patients through a vein recognition technology; the multi-source data acquisition module acquires vital sign data and equipment parameters of a patient in real time to obtain multi-source data; the intelligent drug management module authorizes to store and take drugs according to the biological recognition result and generates an electronic anesthesia prescription; the processing decision module receives the multi-source data and generates a medicine adjusting instruction through a preset rule and an algorithm in combination with an electronic anesthesia prescription; the execution control module carries out secondary confirmation on the medicine adjusting instruction through biological recognition and then sends the medicine adjusting instruction to anesthetic infusion equipment to be executed; according to the method, through the electroencephalogram double-frequency index, the entropy index and the hemodynamic parameters, in combination with the anesthesia depth evaluation result and the personalized drug metabolism and effect model, the medication scheme is dynamically adjusted, the optimal infusion rate is generated, the anesthesia depth control is more accurate, and the fluctuation range is narrowed.

The invention discloses a chronic disease intervention safety detection system and method based on large model multi-agent cooperation, and relates to the technical field of natural language processing and multi-agent cooperation in the medical health field. A standardized interface and an adaptive cleaning algorithm are adopted to realize data desensitization, missing value filling and format unification, and a dynamically updated patient health portrait is constructed; complex tasks are decomposed based on a chain reasoning technology, special tools such as a drug interaction detection tool and a nutrition gap calculation engine are developed, and a clinical knowledge base is integrated for parallel analysis; suggestion conflicts are eliminated through a multi-agent debate mechanism, the priority is calculated in combination with a weight rule, manual auditing is triggered to process low-confidence disputes, and finally a structured health report containing medication adjustment, diet optimization and behavior intervention is generated. The limitation of a traditional method in data integration, cross-domain reasoning and conflict resolution is solved.

The invention relates to the technical field of medical information, and discloses an internet-based remote medical inquiry system, which comprises a data acquisition and quality assurance module for acquiring multi-source heterogeneous medical data and performing time sequence alignment, quality monitoring and intelligent repair interpolation; the feature extraction and fusion module is used for performing deep feature extraction, cross-modal semantic alignment and hierarchical attention fusion; the complication association reasoning module is used for obtaining a deep complication association reasoning result by adopting a graph attention network and multi-hop reasoning; the complication progress prediction module is used for constructing a complication progress prediction model and carrying out meta-learning enhancement and uncertainty quantification; the intelligent medication decision module is used for generating candidate schemes and screening a Pareto optimal scheme; the compliance management module is used for carrying out compliance causal inference and closed-loop optimization; the effect evaluation module is used for carrying out effect evaluation and dynamic optimization; according to the method, a compliance improvement mechanism is established through causal inference and reinforcement learning, and interpretable man-machine collaborative decision and closed-loop optimization management are realized.

The invention relates to a nitric oxide data analysis method and system for bronchial asthma assessment. According to the method, exhaled air nitric oxide and pulmonary alveolar nitric oxide of a patient, omics data and clinical information are acquired in a standardized manner, and multi-dimensional features are constructed after data cleaning and fusion; comprise an inflammation level index, an inflammation region entropy, an inflammation synergy index, a glucocorticoid response factor, an infection-smoking synergy influence factor and a heredity-symptom distribution index. And outputting initial risk assessment based on an explainable elevator EBM model, and finally generating an asthma risk probability through a weighted integration formula to realize three-level layering: low / medium / high risk. The system dynamically associates treatment decisions, for example, the drug dosage and the monitoring frequency are improved when the risk is upgraded, and the medication scheme is optimized when the risk is degraded. According to the scheme, the limitation of traditional single marker static analysis is broken through, multiple mechanisms of dissection, immunity and pharmacology are fused, the evaluation precision and clinical applicability are remarkably improved, the acute attack rate is reduced, and accurate typing treatment is guided.

The invention relates to the technical field of medical drug dose analysis, in particular to a deep learning-based advanced nasopharyngeal carcinoma multi-target dose prediction system, which comprises a data acquisition module, an automatic sketching module, a dose prediction model construction module, a diversified dose generation module, a model training and optimization module, a result output and visualization module and the like. The automatic sketching module is used for realizing accurate and automatic sketching by using a multi-modal fusion and knowledge distillation framework; the dose prediction module accurately extracts features based on a UNetr network model and generates a multi-channel dose prediction result; the diversified dose generation module generates diversified complementary dose distribution through a specific method and a constraint mechanism; the model training and optimizing module effectively improves the model performance; the result output and visualization module presents results in various visual modes for doctors to make decisions; the nasopharynx cancer treatment dose prediction accuracy and individuation degree can be improved, the automatic sketching precision and efficiency can be improved, the clinical decision process can be optimized, and development of related technologies can be promoted.

The invention provides a ward rotation evaluation and model construction method based on multi-modal information, and belongs to the technical field of deep learning. The method comprises the following steps: firstly, constructing a multi-modal clinical data set, integrating five types of heterogeneous data including operation records, vital sign time sequence data, medical images, medication records and nursing logs, labeling postoperative state labels of patients, and simulating a clinical real scene to generate a training set and a test set containing random missing modals; secondly, constructing a ward rotation evaluation model, wherein the model is used for improving and designing a traditional teacher-student network architecture into a double-path heterogeneous distillation model; meanwhile, designing a multi-task loss joint optimization strategy for aligning the characteristics of the teacher-student network and constraining the evaluation result of the teacher network, and finally deploying a trained model to an edge computing device, receiving a multi-modal data stream in real time and outputting a transfer suggestion and a risk score; the invention provides a brand new intelligent solution under a ward rotation scene, and improves the evaluation robustness of a multi-modal missing scene.

The invention relates to the technical field of health management of old people, and discloses a method and a system for predicting multiple diseases of old people. The method comprises the following steps: acquiring multi-modal health monitoring data of a target old person in a preset time period, wherein the multi-modal health monitoring data comprises physiological index time sequence data, a medication record sequence and a daily activity ability evaluation result; and performing cross-modal correlation analysis on the multi-modal health monitoring data to generate a disease interaction characteristic matrix containing metabolic disease correlation degree, circulatory system disease coordination index and neurodegenerative disease progress rate. And performing hierarchical clustering processing on the feature matrix by adopting a dynamic weight distribution algorithm, and outputting potential common disease combinations of the target old people and a priority score of each common disease combination. And generating a personalized intervention strategy set including a drug interaction avoidance scheme, a rehabilitation training intensity adjustment scheme and a nutrition intake ratio scheme according to the priority score.

The invention relates to the technical field of bioinformatics, and discloses an adverse drug reaction prediction method based on comment text information enhancement, and the method comprises the following steps: constructing an adverse drug reaction dictionary; extracting adverse drug reaction information based on the comment text; integration of adverse drug reaction information; integrating the drug characteristic information of the public database; calculating the similarity; constructing a heterogeneous graph; building a graph convolutional network model; model training and evaluation; predicting adverse reaction cases of drugs; and analyzing the occurrence mechanism of adverse drug reaction. According to the adverse drug reaction prediction method based on comment text information enhancement, diversified data such as drug comments, clinical test data, actual medication experience and feedback are comprehensively utilized, patient features are added to enrich the relationship between drugs and adverse reactions, more comprehensive drug safety assessment is provided, and the prediction accuracy is improved. In addition, compared with other models, the GCN model has the advantages that the training and prediction are quicker, and the calculation difficulty can be reduced.

According to the artificial intelligence-based antibacterial drug administration purpose judgment method and the automatic judgment system disclosed by the invention, after the acquired medical data of the patient, the clinical application guide and literature of the antibacterial drug and the clinical application guide principle of the antibacterial drug are preprocessed, the historical medical data set of the patient is constructed; a historical medical data set of a patient is randomly divided into a training set, a verification set and a test set according to a set proportion for training, and a trained antibacterial drug use purpose judgment model is converted into an antibacterial drug use purpose judgment rule. After an antibacterial drug order making request is received, an antibacterial drug medication purpose judgment rule is called, a matched antibacterial drug medication purpose is generated according to collected antibacterial drug order making information and current medical data of a corresponding patient, and automatic judgment of the antibacterial drug medication purpose is achieved. And selecting a corresponding antibacterial drug management and control rule and path according to the matched medication purpose.

The invention relates to the technical field of medication reminding, in particular to an intelligent reminding method for nursing medication. The method comprises the steps of obtaining a medication compliance coefficient according to the difference between a medication time interval of medication of a patient in a current time period and a preset medication time interval and the disorder condition of medication time of the patient; according to the correlation condition between the medication time interval of medication of the patient and the health index and the fluctuation condition of the medication time interval, the medication behavior sensitivity coefficient is obtained, and then the influence coefficient of the medication compliance behavior is determined. And determining a medication behavior deviation coefficient according to the difference between the proportioning amount of the medicines taken by the patient in the current time period and a preset proportioning amount, the fluctuation condition of the proportioning amount of each medicine, the medication compliance coefficient and the influence coefficient of the medication compliance behavior, and further adjusting the medication automatic reminding frequency. According to the invention, the self-adaptive adjustment of the medication reminding frequency of the patient is realized.

Systems and methods for providing recommended doses and instructions for pharmaceutical treatments, and more particularly systems and methods for measuring physiological and / or electrophysiological signals from a subject and providing recommended doses and instructions of pharmaceuticals based at least in part on the measured signals are described. Further, the recommended doses and instructions are based at least in part on a subject's disease(s), disorder(s), or injuries and / or symptom(s) of such, and / or side effects of medications or treatments the subject is receiving. The recommended doses and instructions are personally tailored to the subject to precisely and personally address the subject's needs, and may be based in part on correlation with database(s) comprising historical data of the subject and / or other subjects to treat the subject's movement disorders, injuries to the body or brain, cognitive diseases and disorders, sleep diseases and disorders, chronic pain, or other conditions.

The invention relates to the technical field of anesthesia, and discloses a personalized anesthesia scheme generation method, which comprises the following steps: S1, extracting and coding patient features; s2, modeling a pharmacokinetic model; s3, drug concentration prediction; s4, applying a PD model; s5, performing uncertainty quantification; s6, predicting and outputting; s7, updating and feeding back parameters; s8, verifying the model; in the personalized anesthesia scheme generation process, firstly, effective feature extraction and coding are carried out on multi-modal data of a patient; according to the personalized anesthesia scheme generation method, efficient feature extraction is carried out on multi-modal data (such as electronic health records, genome information and real-time monitored vital signs) through VAE and other deep learning methods, and the differences of patients in the aspects of physique, drug metabolism capability, gene background and the like can be fully described; therefore, the optimal anesthetic medication strategy is customized for each patient.

The invention relates to the technical field of intelligent medical treatment, and discloses a medical large model driven cross-department collaborative prescription generation method and system. According to the method, electronic medical records of patients and prescription data of multiple departments are acquired, key information is structurally extracted to generate directional vectors, and transfer learning and fine adjustment are performed by using historical conflict cases and a drug knowledge graph based on a medical basic large model and an AI chip, so that a collaborative prescription model is constructed. The model can identify drug incompatibility and dosage risks among departments and generate a prescription suggestion set. A doctor can dynamically correct a prescription based on feedback, a collaborative report containing a medication time sequence, a monitoring index and an emergency scheme is generated after multiple rounds of collaborative optimization, and the safety and effectiveness of multi-department combined medication of complex diseases are remarkably improved.

The invention discloses a medication assisting system and device based on symptom-medicine two-way verification, and belongs to the technical field of intelligent medical treatment. The system comprises a data preprocessing module, an entity extraction and alignment module, an atlas retrieval module, a case retrieval module and a bidirectional verification and auxiliary decision module. The system extracts a medical entity through an AC automaton rule engine and a knowledge enhanced BERT model, and realizes mapping with a knowledge graph entity by adopting a two-stage term alignment strategy; performing knowledge graph query by adopting an operation chain generation mechanism driven by cue words to obtain a candidate drug set; meanwhile, similar cases are retrieved based on vector similarity, and an associated drug set is generated; and finally, fusing external medicine knowledge base data, executing a bidirectional verification and completion strategy, and outputting medicine recommendation with clinical safety and coverage range. The invention further provides a device structure for realizing the functions of the system, and the accuracy and interpretability of clinical medication aid decision making are effectively improved.

Systems and methods for intelligent medication monitoring are disclosed. The intelligent medication monitoring system includes a question engine, a supply management module, a monitoring module, a personalized adherence scoring module, an intervention module, and an outcome reporting module to provide intelligent medication monitoring, intervention and outcome reporting. The intelligent medication monitoring system reduces waste of unused medication and processing, provides targeted and personalized outreach to patients, and it is able to capture and report outcomes of interventions.

The invention relates to the technical field of treatment of chronic diseases of old people, and discloses a metabolic characteristic-based medicine dosage optimization method for chronic diseases of old people. The method comprises the following steps: acquiring drug metabolism parameters (including a plasma concentration peak value, a half-life period curve and the like) and organ function data (including a hepatocyte metabolism rate, a glomerular filtration rate and the like) of a plurality of monitoring nodes, arranging the drug metabolism parameters and the organ function data into a time sequence input vector, and extracting a dynamic feature vector by using a time convolution network and an adaptive filter network; performing feature crossing, pharmacokinetic constraint correction and feature enhancement processing, performing fusion to generate a joint feature vector, inputting the joint feature vector into a dose decision model to obtain an adjustment coefficient, and generating a drug dose interval with a safety threshold in combination with a historical drug use record. According to the method, multi-dimensional data integration and dynamic modeling are realized, the accuracy and safety of chronic disease medication of old people are improved, and the method is suitable for individualized treatment.

The invention relates to the field of intelligent medical services, and discloses an intelligent medicine shopping guide and recommendation system for a drugstore, which comprises a user information collection module, a medicine database module, a symptom matching module, a recommendation optimization module and a result output module, the user information collection module is used for receiving health data and personalized requirements input by a user and converting the health data and the personalized requirements into structured user feature data; the drug database module stores components, indications, prices and drug interaction information of drugs, and constructs a drug interaction knowledge graph for representing an interaction relationship between drugs and a risk thereof. By introducing a drug interaction knowledge graph, the interaction risk between drugs is dynamically evaluated, and the risk information is fused into a recommendation decision. Compared with a recommendation method which only depends on static rule matching in the prior art, the problem that potential drug conflicts in recommendation results are difficult to find is effectively solved, and the drug use safety is improved.

The invention relates to the technical field of medical report generation, in particular to an intelligent anesthesia record sheet automatic generation system. According to the system, patient information is automatically recognized through the OCR unit, and real-time collection and structured storage of physical sign data, medication records and surgical operation are achieved in combination with a medical equipment interface and a voice recognition technology; time sequence data analysis and multi-source data fusion modeling are adopted to construct a medical event causal relationship graph so as to improve the accuracy of anomaly recognition and patient sign evaluation; based on a time axis construction unit and a structured template engine, an anesthesia record list is automatically analyzed and filled, and the manual input workload is greatly reduced; and finally, in combination with block chain evidence storage and a multi-level encryption technology, the security and non-tampering property of the anesthesia record list are ensured, the reliability of medical data management is improved, the recording, monitoring and storage efficiency of the anesthesia process is comprehensively optimized, and the medical quality and the patient safety are improved.