860 results about "Drug utilisation" patented technology

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 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 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 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 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 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 discloses a multi-source data-based medical temperature-adjusting atomization drug delivery system for the pneumology department, relates to the technical field of medical temperature-adjusting atomization drug delivery, and aims to solve the problems that the atomization effect is poor and the atomization process is not intelligent. By adjusting atomization power in different respiratory cycle stages, monitoring airway abnormity and respiratory fluctuation in real time to correct parameters and adapting temperature control and atomization components as required, medicine waste and airway stimulation can be reduced, variables such as air source fluctuation and liquid medicine viscosity can be coped, atomization stability is guaranteed, administration efficiency and safety are improved, and the method is suitable for large-scale popularization and application. The requirements of patients with weak respiratory functions are met, the basic information unit is linked with the medical history of the patients and the medicine information to determine the safe temperature, the atomization rhythm is matched in combination with the respiratory parameters, temperature deviation caused by information dispersion can be avoided, manual errors are reduced, and the medication safety is improved.

The invention relates to the technical field of medical health management, and discloses an endocrine patient health management system based on big data. According to the system, a multi-source data acquisition module is adopted to acquire heterogeneous health data including physiological monitoring indexes, medication record data, medical image reports and the like, and living habit logs can also be included. The physiological feature noise reduction module is used for extracting steady-state physiological features, the multi-modal fusion module is used for generating a focus dynamic map, and the medication feedback analysis module is used for generating a drug response mode evolution sequence. The data are input into a multi-dimensional decision model, and a health management scheme is output by a personalized scheme generation module. And the living habit analysis module analyzes the living habit logs to generate influence factors, and participates in making a health management scheme. The system realizes multi-source data fusion analysis, can provide a personalized health management scheme for an endocrine patient, improves the disease diagnosis accuracy and treatment effect, and improves the life quality of the patient.

The invention relates to the technical field of medical information processing, in particular to a type 2 diabetes precise typing and dynamic treatment system which comprises a clinical feature extraction module, a metabolism dynamic modeling module, a precise typing module, a treatment parameter generation module and a dynamic treatment output module. Wherein the clinical feature extraction module is used for collecting a multi-dimensional clinical data set of a patient for continuous 14 days; the metabolism dynamic modeling module is used for extracting blood glucose fluctuation characteristics and pancreas islet function compensation characteristics; the precise typing module is used for dividing metabolism-driven subtypes to which the patients belong through a clustering algorithm; the treatment parameter generation module is used for mapping a subtype-treatment rule base and outputting a matched personalized treatment parameter combination; and the dynamic treatment output module outputs a dynamic treatment scheme. According to the invention, through a dynamic treatment mechanism based on typing, linkage control of individual difference identification and real-time adjustment of the medication scheme of the type 2 diabetes patients is realized, and the accuracy and adaptability of treatment are improved.

The invention relates to the technical field of information processing, in particular to a medicine conflict automatic detection and prescription optimization system for old multi-disease patients. According to the system, a data management module is used for collecting individual data of patients and group data of co-diseased reference groups and receiving a planned medication scheme; the parameter calculation module is used for identifying a target medicine combination with time overlapping in the patient medication record and the planned medication scheme; determining a basic risk index according to the group medication response data, the patient medication record and the work and rest text data; determining a lag risk coefficient according to the physiological indexes and the drug metabolism data; determining group medication associated risk parameters according to atypical reactions and group medication records in the medication reaction data; the risk fusion module is used for determining a drug use conflict risk value based on the basic risk index, the lagging risk coefficient and the group drug use associated risk parameter; and the prescription optimization module is used for generating an optimal medication scheme, so that the generated optimal scheme is more suitable for the actual life of the patient.

The invention provides a drug dosage adjusting system and method based on blood concentration prediction, and belongs to the field of medical artificial intelligence, and the method comprises the steps: obtaining a standard structured data code based on a preset historical clinical database; performing standard structured data coding based on the predicted feature factor set to obtain a predicted feature factor set and a time point combination; constructing a classification prediction model and a regression prediction model based on the prediction feature factor set; a to-be-predicted blood sample is collected in a prediction characteristic factor set time point combination mode, and a blood concentration prediction result is obtained based on the to-be-predicted blood sample of the prediction characteristic factor set, the prediction characteristic factor set classification prediction model and the regression prediction model; and adjusting the dosage according to the blood concentration prediction result. According to the invention, individual differences are analyzed individually, personalized medication dosage adjustment suggestions are generated, and the medication dosage adjustment reliability is improved.

The invention provides a dynamic medication dosage optimization method fused with reinforcement learning. The method comprises the following steps: acquiring a first physiological index parameter of a target patient; obtaining a first diagnosis report of the target patient, wherein the first diagnosis report comprises basic information of the target patient and first disease information of the target patient; the first disease information comprises a first disease type and first disease description information; determining a first reinforcement learning algorithm corresponding to the first disease type; determining a first control parameter of the first reinforcement learning algorithm according to the first physiological index parameter and the basic information; and performing operation on the first disease description information through the first reinforcement learning algorithm and the first control parameter to obtain a first medication dosage parameter. Based on the application, the drug effect can be consistent with the physical condition of a patient, and poor drug effect and excessive side effects caused by too strong drug effect are avoided.

The invention discloses a traditional Chinese medicine chronic disease dialectical treatment optimization method based on a graph neural network, and the method comprises the following steps: S1, collecting electronic medical record text data and traditional Chinese medicine knowledge data of a patient, and constructing an initial dialectical graph; s2, obtaining an initial feature vector of a node; s3, performing multi-order spiral perception processing by using a boa convolution unit, constructing a spiral adjacent path for each target node according to a structure depth and a relation direction, and introducing a structure position coding mode to perform sequential perception modeling on a node neighborhood relation to generate an adjacent feature matrix; s4, constructing a syndrome semantic tension matrix; s5, inputting the adjacency feature matrix and the syndrome semantic tension matrix into a GATv2 model to obtain a final node feature vector; and S6, outputting a syndrome type prediction result corresponding to the patient by using the final feature vector of the node, and generating prescription and drug path recommendation. According to the method, the GATv2 model and the boa convolution unit of structure perception are combined, so that traditional Chinese medicine chronic disease dialectical reasoning and drug recommendation are realized.

The invention discloses a data management system and method based on a large model, and relates to the technical field of big data analysis, and the method comprises the steps: setting a data monitoring management period, enabling the system to collect the stock change information of various types of medicines in a hospital, analyzing the historical warehouse-out data of a department in combination with a time sequence model, and precisely measuring and calculating a first warehouse-out predicted value; and comparing the daily average dose with a standard limited daily dose, deeply mining compatible drug data associated data in an auxiliary drug use scene, analyzing a compatible drug data ex-warehouse trend, and accurately mastering a collaborative consumption rule. By tracking ex-warehouse proportion changes of target drug data and compatible drug data in a historical period, predicting a proportion trend, predicting ex-warehouse quantity in combination with the compatible drug data, dynamically calculating an auxiliary drug use demand, and deeply deconstructing inventory change data, prospective pre-judgment of the inventory change data is realized, hospitals are assisted to optimize an inventory structure, and the workload of the hospitals is reduced. And the accuracy and response efficiency of inventory data management are improved.

The invention belongs to the technical field of medical informatization, and particularly relates to a medical insurance medication rationality interception system and method based on deep reinforcement learning and an FP-Growth algorithm, and the method comprises the following steps: S1, receiving electronic prescription data transmitted by a hospital HIS system in real time; s2, according to original diagnosis information in the prescription, performing standardization processing to obtain a diagnosis set; according to the original medicine information in the prescription, cleaning and integrating to obtain a medicine set; s3, based on an FP-Growth association rule algorithm, performing diagnosis-drug association analysis and drug-drug association analysis on the diagnosis set and the drug set, and identifying a low-association prescription according to a preset condition and marking the low-association prescription as a suspicious prescription; s4, starting deep reinforcement learning analysis on the suspicious prescription through DRL decision optimization, and outputting a prescription reinforcement learning feature set; and S5, outputting a prescription qualitative analysis result and a corresponding confidence score according to the prescription reinforcement learning feature set.

The invention relates to the technical field of medical care informatics and precision medical treatment, and discloses a hemodialysis patient precision medication system based on multiple omics and big data, and the system comprises a multi-source data collection module which collects the multi-source heterogeneous data of a patient and carries out the standardization and integration processing; the drug metabolism analysis module is used for analyzing drug metabolism paths and individual differences through multi-omics data, constructing a drug metabolism polymorphic model and outputting gene metabolism capability grades and biomarker risk tags; the initial medication decision-making module is used for constructing an initial medication decision-making model based on a federal optimization framework of a momentum enhancement and robust aggregation mechanism, and outputting a personalized initial medication scheme for erythropoietin and a vein iron agent; and the drug dosage adjustment module is used for dynamically optimizing the time node and dosage adjustment opportunity of drug administration through an asynchronous federal reinforcement learning framework in combination with a prospective parameter correction strategy. According to the invention, the intelligent medication system with strong robustness and fine decision is constructed.

The invention relates to an intelligent decision-making system for nutrition metabolism collaborative management of elderly chronic disease patients, in particular to the field of nutrition metabolism collaborative management of elderly chronic disease, which is characterized in that drug molecule characteristics and nutrient metabolism paths are dynamically integrated through a multi-modal knowledge graph, and a cross-domain associated three-dimensional knowledge network is constructed; based on a reinforcement learning real-time optimization rule confidence threshold value, the early warning sensitivity is adaptively adjusted according to the degree that the metabolic index of the patient deviates from the safety interval; the streaming conflict detection engine accurately identifies the potential risk of asynchronously input medication and diet data, and triggers graded early warning through space-time alignment and sub-graph matching; the closed-loop evolution mechanism fuses patient compliance feedback and blood potassium change trend, drives the taboo rule base to continuously and autonomously evolve under the constraint of renal function layering, and realizes personalized risk prevention and control and metabolic state collaborative optimization.

The invention provides an intelligent medicine management and reminding system based on multi-dimensional environment perception, and relates to the technical field of intelligent wearable equipment and medical health management. According to the system, a medicine prescription is set in the smart watch, user behaviors and environment are monitored in real time, a three-point positioning technology is applied to manage medicine positions, and dynamic graded reminding is implemented according to medicine importance. The system adaptively adjusts the reminding mode, improves the medication compliance of the user, reduces the risk of missing taking, optimizes the energy management, and prolongs the cruising ability.

The invention provides an intelligent diagnosis and decision-making method and system for crop diseases in intelligent agriculture, and the method comprises the steps: extracting spatial features through employing a convolutional neural network based on standardized remote sensing image data in a unified multi-source data set, generating preliminary positioning data of a scab region, and carrying out the preliminary positioning of the scab region; the scab area preliminary positioning data comprises scab position coordinates and range boundaries; inputting the key time weighted sequence data and the scab area preliminary positioning data into a space-time attention mechanism, and fusing spatial position coordinates and time environment features through attention weight distribution to generate comprehensive disease feature vector data; and based on the preliminary prevention and control prescription data, adopting a drug resistance evaluation model to analyze the drug use frequency, the disease type and the crop variety in the historical drug use data, generating optimized prevention and control prescription data, and adjusting the drug variety or dosage to reduce the drug resistance risk.

The invention discloses a wound healing state evaluation method and system based on artificial intelligence, and belongs to the field of wound healing evaluation. The method comprises the following steps: integrating medical record data and wound physiological parameters, and extracting relevance between a medication history and an infection type to form a multi-dimensional pathological factor; hemoglobin oxygenation, blood flow and metabolic heat are subjected to cross-period correlation analysis to construct dynamic physiological time-space information, pathological factors and the physiological time-space information are fused into a heterogeneous graph, nodes comprise drug intensity quantized values, infection risk levels and microcirculation fluctuation values, and edge weights are determined by the time sequence matching degree of drug doses and blood flow changes; based on the node weight and the edge association relationship, the treatment time and the physiological parameter acquisition period are aligned to generate a spatio-temporal evolution matrix, the spatial distribution of the infection risk level and the metabolic heat value characteristics are combined, the dynamic association of the drug action intensity and the microcirculation fluctuation is analyzed, and wound healing evaluation information is generated. The accuracy of wound healing state evaluation can be improved.

The invention relates to the technical field of medical products, in particular to a medical product market prediction and supply chain collaborative management system, which comprises a health risk identification module, a risk trend analysis module, a demand sorting and grading module, a supply chain matching module and a distribution path optimization module. According to the method, through synchronous analysis of spatial data, the risk pattern of population mobility and disease distribution in each geographic area can be dynamically displayed, multi-dimensional accurate identification for high-risk areas of the mobility population is realized, fluctuation of market demands and potential public health pressure are actively identified based on a trend linkage triggering mechanism and in combination with a periodic change index, and the risk of population mobility and disease distribution in each geographic area is accurately identified. The early warning response speed is improved, multiple parameters such as drug use demand urgency levels, drug delivery timeliness and spatial distances are comprehensively considered, a demand sorting and resource allocation global system is constructed, supply and demand connection is promoted to be more scientific and reasonable, resource flows between medical institutions and storage nodes are matched, and multi-point inventory and priority demands are combined, so that the efficiency of early warning is improved. And the overall resource utilization rate is improved.

The invention discloses a Chinese herbal medicine knowledge graph construction method and system, and the method comprises the steps: collecting structured data and unstructured data, and generating a drug property and pharmacology data table; extracting property and flavor channel tropism attributes of the medicinal materials, generating a medicinal property triple and a pharmacological triple, and establishing preliminary cross-domain association; defining logical constraints and compatibility rules of nature and flavor channel tropism, and constructing a causal reasoning chain; performing spatial semantic mapping on the channel tropism attributes and the pharmacological entities to generate a dynamic cross-layer mapping relationship; using an attribute graph database to store node types and edge types, and constructing a hybrid index; new data streams are collected in real time, the atlas is updated, and an intelligent decision-making interface and a visual interaction platform are provided. Through multi-source fusion, dynamic association, efficient storage and closed-loop optimization, the problems of semantic segmentation, update lag and low retrieval efficiency in traditional Chinese medicine digitization are solved, and scientific and real-time technical support is provided for traditional Chinese medicine modernization research, accurate medication and new medicine research and development.

The invention relates to the technical field of medical information processing, and discloses an intelligent prescription auxiliary issuing system and method based on generative artificial intelligence and a storage medium, and the system comprises a multi-modal input module, a model generation module, a knowledge integration module, a prescription generation module, an auditing optimization module and a prescription output module. The method corresponds to the system, and the storage medium corresponds to the method. According to the application, the limitation of function fragmentation is avoided, diagnosis and treatment instructions, personalized habits of doctors, dynamic medical knowledge and risk control requirements are cooperatively and comprehensively planned through the modules, a medication safety base line is kept through risk grading verification, optimization suggestions are generated according to diagnosis and treatment habits of the doctors, meanwhile, the accuracy and efficiency of prescription making are improved, and the application prospect is wide. The comprehensive requirements for safety, personality and high efficiency of prescription assistance in clinical scenes are fully met, powerful support is provided for diagnosis and treatment work of doctors, and medication safety and treatment effects of patients are indirectly guaranteed.

The invention belongs to the technical field of curative effect evaluation of teenager depression drugs, and particularly relates to a curative effect evaluation method of a drug for treating teenager depression. According to the method, through dynamic fusion analysis of multi-dimensional physiological and behavior data, the dynamic effect of drug intervention can be reflected more comprehensively and accurately, meanwhile, the data dimension is effectively reduced through dimension reduction processing and key feature vector extraction, capture of key information is ensured, and the accuracy of drug intervention is improved by constructing a drug use window and conducting quantitative scoring on the drug curative effect in the window. According to the method, evaluation of the drug curative effect and introduction of an interpolation compensation technology are realized, the problem of data missing is further solved, a continuous dynamic curative effect evaluation sequence is formed, and a time sequence evolution rule of the drug curative effect can be intuitively reflected according to a curative effect change trend identified by the dynamic curative effect evaluation sequence; the comprehensive curative effect stability index is calculated by combining the duration and the alternating frequency of the curative effect change trend in the medication cycle, and a scientific basis is provided for determining the drug curative effect grade.

The invention provides a multi-module collaborative clinical blood coagulation function intelligent evaluation and drug recommendation system, and relates to the technical field of medical information processing. Comprising a data acquisition and fusion module, a state recognition module, a drug intervention effect evaluation module, a decision recommendation module and a system visualization module. According to the system, continuous integration of clinical scattered data is realized by constructing a unified time sequence structure of multi-source detection indexes and medication records; generating a blood coagulation state vector containing the bleeding risk and the thrombus risk through a state recognition mechanism combining rule judgment and data calculation, outputting a confidence score and an index contribution degree, and forming a state track evolving along with time; performing causal evaluation on the difference of the state change before and after intervention to obtain a drug average treatment effect; and individualized drug suggestion or early warning information is generated based on the state track and the intervention effect. And dynamic evaluation and drug intervention measurement and calculation of the blood coagulation risk are realized.

The invention discloses an intelligent generation method and system for a medication guidance sheet based on multi-source heterogeneous medicine data, and belongs to the field of intelligent generation of medication guidance sheets, and the method comprises the following steps: S1, carrying out the standardization and real-time fusion of the multi-source heterogeneous medicine data; s2, integrating the standardized multi-source heterogeneous data and multi-source clinical knowledge, and constructing a knowledge graph; s3, constructing a medication guidance sheet intelligent generation model, and training the medication guidance sheet intelligent generation model; s4, inputting the real-time data of the patient into the trained medication instruction sheet intelligent generation model and the knowledge graph, and generating a medication instruction sheet; and S5, iteratively optimizing the medication guidance sheet intelligent generation model and the knowledge graph. By the adoption of the intelligent generation method and system for the medication guidance list based on the multi-source heterogeneous medicine data, precise, personalized and real-time generation of the medication guidance list is achieved, and the defects of multi-source data integration, dynamic updating, personalization and content quality control in the prior art are overcome.

The invention relates to the technical field of medical informatization, and discloses an intelligent medicine selling system and method, and the system comprises a dynamic efficacy anchor point module which is used for constructing a dynamic hash fingerprint of a doctor duty state, a patient privacy encryption voucher and a medicine flow space-time stamp, and associating the states of three nodes in real time through a logic rule engine to generate a prescription efficacy map, and an off-line bidirectional verification protocol is designed to realize prescription-drug matching in a network-free environment. A multi-point qualification checking mechanism is innovatively integrated, when a doctor is qualified to suspend in a hospital A but is effective in a hospital B, the system automatically associates the state of an issuing mechanism based on a prescription type, and the risk of cross-hospital prescription violation is accurately intercepted; aiming at the prescription continuing requirement of a chronic disease patient, when the licensing state of a doctor is changed, the system automatically compares a medication rule with health data, dynamically prolongs the validity period of a prescription, triggers referral early warning and constructs a medical safety transition period.