560 results about "Drug utilisation" patented technology

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 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 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 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 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 relates to a medication recommendation method and system. The method comprises the following steps: preprocessing original medical data of a current patient into a standardized time sequence diagnosis and treatment sequence; inputting the time sequence diagnosis and treatment sequence and the medical professional knowledge base into a code-doctor-seeing double-level attention model for processing to obtain code-level aggregation features of each doctor seeing and corresponding doctor-seeing-level attention weight, and performing weighted aggregation on the code-level aggregation features based on the doctor-seeing-level attention weight to obtain a code-level aggregation feature; generating a global health representation of the current patient; based on at least one of the multiple medical feature information of the current patient, similar historical cases are retrieved from a case information base to serve as reference cases; and generating and outputting a medication recommendation list for the current patient at least based on the global health representation of the current patient and the medication scheme of the reference case. By adopting the method and the device, the accuracy and the safety of drug recommendation for the current patient can be improved, so that a doctor can be better assisted to make a drug decision.

The invention relates to the technical field of medical monitoring and intelligent decision making, and particularly discloses an intraoperative noninvasive real-time blood flow monitoring and medication intervention analysis system. Pulse wave oscillation harmonic component characteristics and diastolic attenuation characteristic characteristics are extracted by using variational mode decomposition and a polynomial fitting algorithm; the multi-dimensional features are fused and then input into a deep learning classification model, and accurate recognition of insufficient heart pump efficiency, abnormal peripheral vascular tension and circulation capacity imbalance states is achieved; time sequence prediction and grading early warning are carried out based on the hidden Markov model and the individualized baseline safety interval, and finally a targeted medication scheme is output through the intervention strategy knowledge base.

The application relates to the technical field of medical information, and discloses a remote medical inquiry system based on the Internet, which comprises the following modules: a data acquisition and quality guarantee module, which is used for collecting multi-source heterogeneous medical data, performing time sequence alignment, quality monitoring and intelligent repair interpolation; a feature extraction and fusion module, which is used for performing deep feature extraction, cross-modal semantic alignment and hierarchical attention fusion; a complication correlation reasoning module, which adopts a graph attention network and multi-hop reasoning to obtain a deep complication correlation reasoning result; a complication progression prediction module, which is used for constructing a complication progression prediction model, performing meta-learning enhancement and uncertainty quantification; an intelligent medication decision module, which is used for generating candidate schemes and screening a Pareto optimal scheme; a compliance management module, which is used for performing compliance causal inference and closed-loop optimization; and an effect evaluation module, which is used for performing effect evaluation and dynamic optimization; the application establishes a compliance improvement mechanism through causal inference and reinforcement learning, and realizes interpretable human-computer collaborative decision and closed-loop optimization management.

The invention relates to the technical field of chronic obstructive pulmonary disease rehabilitation medical equipment and services, discloses a chronic obstructive pulmonary disease multi-mode respiratory training system based on behavioral design science, and aims to solve the technical problems that an existing respiratory training product is poor in patient compliance, poor in user experience and lack of personalized behavior guidance. The system comprises a hardware equipment cluster and a software service platform, a multi-dimensional intervention architecture is constructed based on an FBM behavior model, and closed-loop services of respiratory training, drug management, health monitoring and rehabilitation support are realized through an motivation promotion module, a capability optimization module, a multi-sensory prompt module and a data interaction module. The system effectively stimulates the training enthusiasm of a patient, reduces the operation threshold, improves the training and medication compliance, improves the respiratory function and life quality of the patient, reduces the consumption of medical resources, and has remarkable clinical application value and social and economic benefits through deep fusion of behavioral design and a multi-modal rehabilitation technology.

The invention discloses a medical drug knowledge RAG optimization method based on a Trie tree, and relates to the field of artificial intelligence and medical drug information processing. The method comprises the following steps: constructing a dual Trie tree knowledge base in the field of medical drugs (a term Trie tree: storing drug / disease / medication attribute terms and ATC / ICD-11 codes; the content Trie tree is used for storing medicine specification hierarchical content and efficacy weight), carrying out preprocessing segmentation on a medical medicine ultra-long text and associating Trie node attributes, and adopting multi-stage retrieval of Trie prefix matching and BioBERT semantic retrieval to recall knowledge blocks; the context (father node-chapter overview, child node-refining rule and brother node-security association information) is expanded based on the hierarchical relationship of the nodes of the Trie tree, finally a response with accurate reference (drug name + ATC code + specification chapter) is generated, and drug conflict is avoided through consistency check. The medical drug knowledge retrieval accuracy and response safety are improved, and the method is suitable for scenes such as intelligent medication consultation and clinical medication aid decision making.

The invention relates to the technical field of digital medical treatment and drug evaluation, in particular to an individualized drug curative effect and safety evaluation method and system based on digital twinborn and causal reinforcement learning. Individualized anti-fact prediction is realized through a patient digital twinning model, and potential curative effects and adverse event risks of different medication schemes can be evaluated on a single patient level. Meanwhile, an off-strategy causal reinforcement learning and dual-robust estimation method is adopted, the individualized value of a long-term sequential medication strategy is evaluated from observed real world data, and the preciseness of causal inference is improved; in addition, a curative effect-safety joint value function is constructed, curative effect benefits, safety risks and compliance cost are uniformly incorporated into the same decision framework, and multi-objective joint optimization is achieved; finally, an interpretability analysis report containing key features, value comparison and risk analysis is provided, the understanding and trust of clinicians on recommendation results are enhanced, and application and popularization in clinical practice are facilitated.

The invention relates to the technical field of elderly chronic disease medication management, and discloses an elderly chronic disease medication compliance intelligent reminding and management system. The system comprises an individualized medication modeling unit for establishing an individual pharmacodynamic model containing medication taboo and dosage rules based on an electronic health record of a patient and pharmacokinetic parameters; the multi-modal behavior sensing unit is used for collecting physiological data and movement tracks through wearable equipment, and generating a medication behavior sequence in combination with the record of the intelligent medicine box; the compliance deviation analysis unit is used for comparing an expected behavior with an actual behavior to calculate indexes such as a time matching degree and dose deviation; the risk decision engine unit is used for outputting an intervention level and a reminding strategy in combination with the deviation index, the physiological state and drug interaction; and the cross-platform collaborative execution unit triggers multi-channel reminding, medicine box locking and emergency contact notification. According to the system, the whole medication management process is intelligent, and the medication compliance and safety of the elderly patients are improved.

The invention discloses an anesthesia medical material management system based on Internet of Things and cloud data collaboration, and the system comprises intelligent medicine vehicle terminals which are disposed in operation rooms and are used for storing, issuing and recovering anesthesia essence medicines; the cloud data collaborative management and control platform is in communication connection with the intelligent medicine vehicle terminal (100) and is used for receiving and processing the medicine operation data from the intelligent medicine vehicle terminal and generating a whole-process management instruction; and the hospital management platform is in butt joint with the cloud data collaborative management and control platform and is used for providing operation schedule information of the patient, receiving medication charging information and generating an electronic prescription. According to the invention, automatic, visual and traceable closed-loop management of the whole process of applying, storing, taking, recycling and charging traceability of the hemp essence medicine can be realized, and integrated, centralized and standardized management of consumables and airway management tools is realized, so that the management precision, the medication safety and the operating room operation efficiency are remarkably improved.

The invention relates to the technical field of medical information, in particular to a medication safety management method, device and equipment and a readable medium. The method comprises the following steps: acquiring electronic prescription data containing medicine names, specifications, dosage, usage, medication time and basic information of patients; performing multi-dimensional risk analysis on the electronic prescription data based on the medication safety knowledge base, including incompatibility analysis, dose safety analysis, medication time conflict analysis and special usage suitability analysis, and generating medication risk prompt information and a personalized medication guidance scheme; the medication guidance scheme comprises a text abstract and instruction data used for generating a two-dimensional code; driving label printing equipment to print the intelligent medication two-dimensional code; a detailed text, a voice interpretation file or a medicine use demonstration video file in the medicine use guidance scheme is associated with the two-dimensional code and stored in a server; a user can access the multimedia guidance file by scanning the two-dimensional code through terminal equipment, so that the medication compliance and safety of a patient are improved.

The invention provides a drug recommendation method fusing medical record features and risk control, and belongs to the technical field of medical information. Existing drug recommendation depends on external medical knowledge to a great extent, so that a recommendation system is poor in expandability and insufficient in clinical practicability. On the basis, the invention provides a drug recommendation method which does not depend on external medical knowledge and is only based on electronic medical record data. According to the method, an individualized illness state recognition and evolution modeling module is designed, records most representative for the current illness state can be automatically recognized from historical medical records of a patient, the disease course development process is described, and individualized medication preferences and treatment modes are mined. According to the method, semantic features of data are fully utilized, so that joint optimization of recommendation risks and effects is realized under the condition of no external knowledge intervention. Experimental results show that the method is obviously superior to existing mainstream methods in multiple evaluation indexes, and has good accuracy, robustness and clinical application prospects.

The invention discloses a medical prescription auditing and medication safety management and control method, device and equipment and a storage medium, and relates to the technical field of medical informatization and health big data, the method comprises the following steps: obtaining drug information, patient allergy history and multi-dimensional health data in a target prescription, and constructing a prescription data set; in combination with a medicine knowledge base and a taboo database, intelligent verification is carried out on the medication rationality of the prescription by utilizing a Duros rule engine and a graph neural network model, and a verification result and verification detail data are output; if the verification result is unreasonable, marking the prescription as a risk prescription, generating a medication risk assessment report and a prescription modification suggestion based on verification detail data, and pushing the report and the suggestion to a corresponding doctor terminal; and if the verification result is reasonable, generating personalized medication guidance through prescription-patient feature matching, and sending the personalized medication guidance to the doctor terminal, the patient terminal and the pharmacy terminal. According to the application, intelligent prescription checking and medication safety guarantee can be realized through risk assessment and personalized adaptation.

The invention provides a combined medicine recommendation method based on multi-modal alignment. A core module of the recommendation method comprises a cloth perception multi-modal medicine alignment module, a time sequence multi-view patient aggregation module and a combined medicine recommendation module. According to the recommendation method, electronic health records EHRs serve as a data source, multi-modal information matched with physical signs of a patient and medicine application characteristics is obtained, effective alignment and fusion are conducted on the multi-modal information through the core module, and suggestion data matched with the multi-modal information and composed of multiple medicines are generated. A DDI loss function and a dynamic weighting strategy are introduced to process conflicts among different drugs in the generated suggestion data so as to output safe suggestion data, and it is avoided that conflicting drugs are recommended in the suggestion data; according to the method, personalized, high-safety, scientific and reasonable drug combination suggestions can be provided.

The invention relates to the field of data processing, in particular to a drug safety signal mining method and system based on multi-source data. The method comprises the following steps: firstly, constructing a longitudinal event sequence containing medication and diagnosis events for each patient; calculating a time sequence correlation factor of medication and subsequent adverse events, wherein the time sequence correlation factor is in negative correlation with the time interval of the two events and the number of hybrid diagnosis events in the interval; meanwhile, based on the matching degree of the event time interval and the known incubation period probability distribution, obtaining an incubation period rationality weight; combining the drug dosage, the time sequence correlation factor and the reasonable weight of the incubation period, accumulating the related drug event influence before the occurrence of the adverse event, and calculating the accumulated correlation energy of the specific drug and the adverse event; and finally, carrying out non-proportional analysis on the accumulated associated energy of the patient group, realizing drug safety signal mining, and improving the accuracy and reliability of signal identification.

The invention discloses a multi-modal drug recommendation method and system based on large language model driving, and relates to the technical field of medical informatics and artificial intelligence. The invention aims to solve the limitation of an existing drug recommendation model in the aspects of patient characterization construction, drug multi-modal modeling and large language model application. Comprising the following steps: constructing patient characterization, and aligning a patient state and a potential medication space in a characterization learning stage through a cooperative prompt project driven by a large language model; multi-modal drug characterization is constructed, and clinical logic and chemical characteristics of drugs are deeply depicted through semantic expert and molecular structure expert dual-channel design; a'static anchoring dynamic 'time sequence reasoning mechanism is provided, context modulation is performed on a dynamic evolutionary clinical event sequence by utilizing a static treatment baseline formed by global medication history, and finally, an accurate and safe medication combination is generated through a label sensing prediction module. According to the method, the accuracy, safety and clinical logic self-consistency of drug recommendation are remarkably improved.

The invention discloses an adverse drug reaction monitoring method based on big data, relates to the technical field of drug safety big data monitoring, is applied to clinical medication safety management, solves the problems of traditional monitoring data dispersion, early warning lag and inaccurate evaluation, and realizes real-time incremental updating and storage. The data preprocessing step is used for cleaning abnormal data, filling missing values and standardizing the data into a uniform format; in the risk prediction step, an improved random forest model is combined with patient characteristics to output an adverse reaction risk level; an early warning and intervention step of pushing early warning and intervention suggestions according to risk grades; the feedback optimization step tracks the intervention effect, and the model is optimized by using new data regularly; in the secure storage step, sensitive data are encrypted, access authority is controlled, and secure compliance storage of the data is realized. According to the method, multi-source data are integrated to improve evaluation comprehensiveness, the iterative optimization model guarantees long-term precision, data are safe and compliant, and clinical medication safety is improved.

The invention relates to the field of medical health and artificial intelligence, and discloses a tumor-chronic disease co-disease medication dynamic risk early warning method fusing AI and a rule engine, and the method comprises the steps: S1, collecting multi-source data of a co-disease patient, and generating unified feature data; s2, constructing a risk prediction model comprising a rule engine and a few-sample learning model; the rule engine performs preliminary judgment on the drug use risk; carrying out refined risk probability prediction on the few-sample learning model by utilizing a global category representation and feature matching mechanism constructed by the few-sample learning model; carrying out weighted fusion on prediction results of the two; s3, dynamically optimizing parameters of the few-sample learning model by adopting a meta-heuristic optimization algorithm; and S4, based on the optimized risk prediction model, carrying out real-time risk early warning on the medication data of the co-patient. According to the method, the technical problems of difficulty in prediction of a few-sample scene, low parameter optimization efficiency, unreliable early warning result and the like are effectively solved, and real-time monitoring, accurate evaluation and individualized early warning of the medication risk of the co-patient are realized.

This invention discloses a method, apparatus, device, and storage medium for medication risk assessment during pregnancy, relating to the interdisciplinary fields of medical information technology and clinical pharmacy. The method includes: receiving patient information and proposed medication regimen information, whereby the patient information includes gestational age and information on multiple comorbidities; calling the corresponding gestational age adaptive risk weight matrix based on gestational age, weighting the patient features to generate a weighted feature vector, and inputting this vector into a machine learning model to obtain an initial medication risk score; based on a constructed heterogeneous knowledge graph, using entities corresponding to multiple comorbidities and medication regimen information as query starting points, performing multi-hop reasoning to determine the existence and severity of conflicting paths; if a conflicting path is detected, revising the initial medication risk score according to the severity of the conflict to generate a final risk score. This application enables individualized, interpretable, and evidence-based intelligent risk assessment of proposed medication regimens for pregnant women with multiple comorbidities.

The application discloses a kind of Zhang Xizheng pure Chinese medicine theory prescription medicine digital inheritance and intelligent analysis system.The system includes disease and syndrome differentiation rule engine subsystem, prescription three-way search subsystem, drug knowledge enhancement subsystem, medical record intelligent search subsystem, Zhang Xizheng school review subsystem and large language model interaction subsystem.The method performs the following steps: LLM clinical information extraction and disease prediction (extract symptoms, tongue, pulse, and 8 big disease classification prediction), disease and syndrome differentiation KB rule matching (8 big disease classification x multiple syndrome type weighted scoring matching), multi-source search (prescription three-way search+drug knowledge double-way vector recall and prescription-drug dynamic association+medical record multidimensional mixed scoring search), LLM fusion generation (inject all search results Zhang Xizheng school special prompt word generation theory prescription medicine analysis report) and Zhang Xizheng school four-dimensional review (western medicine perspective+air theory+drug use principles+theory prescription medicine consistency).The present application first realizes the computer formalization expression of Zhang Xizheng "disease differentiation before syndrome differentiation" mode and "air monism" theory, based on "Medical Western Medicine Record" 172 prescriptions+213 drugs / medical theory / medical conversation+137 cases to build a complete knowledge search and intelligent analysis system, with the ability of western medicine perspective+air theory+drug use principles+theory prescription medicine consistency.

Described herein are techniques for automatically substituting information into orders generated by a data record system prior to persisting the orders in storage of the data record system. The techniques store substitution groups of data entities that store data associated with respective substances (e.g., medications). When order templates (e.g., medication regiments) are generated by the data record system, the techniques automatically modify the order templates (e.g., by substituting data entities in the orders) based on data entity substitution configurations of the substitution groups. Information from the modified order templates is input into orders before the orders are persisted in storage of the data record system.

The invention relates to the field of medical information processing, in particular to a medicine accurate query system based on artificial intelligence, and the system comprises a document analysis module which is used for analyzing the past and current diagnosis and treatment medical advice and diagnosis and treatment data of a patient to obtain a structured document analysis result; the query module is used for performing rule matching and reasoning calculation based on the analysis result to generate medication consistency test data; the scheme generation module is used for converting the inspection data into an executable drug administration plan; and the scheme correction module is used for adjusting the drug administration plan in real time based on the execution feedback data. Through medical advice clause logic analysis and medication time axis construction, time logic and conditional logic extraction is realized, and the reasonability of medical advices is ensured through conflict detection; furthermore, rule mapping is carried out in combination with changes of diagnosis and treatment monitoring indexes, a recommended medication plan is generated, dynamic correction is carried out under execution feedback, and accurate query based on reasoning can be realized under complex medical record information.

This invention belongs to the field of medical information technology and discloses an intelligent detection system and method for drug compatibility risks. The method includes: establishing and maintaining a multi-dimensional drug compatibility knowledge base based on a graph database; automatically acquiring drug information to be executed and retrieving the target patient's hospital-wide medication history through a standardized medical information interaction interface; calculating the estimated blood drug concentration of each historical drug at the current moment based on a pharmacokinetic compartment model to construct an effective drug list; performing combination analysis on the drugs to be executed and the effective drug list, and executing a multi-level compatibility risk detection algorithm, wherein the detection algorithm includes multi-drug combination cascade reaction risk prediction based on a graph neural network model; and generating and outputting graded early warning information containing risk levels and specific descriptions based on the detection results. This invention achieves dynamic, accurate, and real-time detection of complex multi-drug combination risks, significantly improving medication safety.

The application discloses a medical expense insurance simulation decision method and system based on data fusion, and relates to the technical field of medical expense insurance simulation decision. When a target patient has obtained a current stage treatment scheme, but the system cannot determine part of the medication sites to be determined as a unique medication direction according to existing diagnosis information and completed examination results, the candidate medication direction is reserved first, then the supplementary candidate is formed according to the medication category, stage continuation state and subsequent processing path, and the supplementary result of the previous stage corresponding to the same to-be-supplemented site is combined to perform an overlay determination, and only the unoverlaid part generates the supplementary result of this round; when subsequent new data arrives, only the affected unoverlaid part that satisfies the boundary condition and no longer establishes the original support relationship is executed for local reopening, so that the current stage input that can be called by insurance simulation decision is formed.