979results about "Medical practises/guidelines" patented technology

The invention discloses a special disease queue data capturing method and system based on an intelligent medical knowledge graph, and relates to the technical field of medical information, and the method comprises the following steps: S1, constructing a special disease intelligent medical knowledge graph which comprises a bidirectional mapping relation between standard terms of a single disease category and clinical actual corpora, clinical text data is accumulated in a mode of combining manual annotation and machine learning, and a domain exclusive knowledge base containing symptoms, diagnosis and examination indexes is formed. According to the special disease queue data capturing method and system provided by the invention, by constructing the special disease intelligent medical knowledge graph, bidirectional mapping of single disease specification terms and clinical actual corpora is realized, and the problem of insufficient semantic understanding when non-standardized clinical corpora are processed by a traditional method is effectively solved; the entity information in the unstructured medical data can be accurately extracted by utilizing a natural language processing model and an inference engine.

An AI-driven, cloud-based system (100) for real-time biomedical and pharmaceutical compliance and risk management, including: (a) a compliance knowledge module configured to ingest, interpret and structure regulatory data using natural language processing (NLP) and generate machine-readable compliance rules; (b) a real-time monitoring and event recording module configured to collect and normalise operational data from distributed biomedical and pharmaceutical systems, including laboratory information management systems (LIMS), manufacturing execution systems (MES) and IoT-enabled devices; (c) an intelligent risk assessment and prediction module configured to correlate operational data with compliance rules, calculate dynamic risk scores and predict potential compliance violations using machine learning models; (d) an automated policy and workflow enforcement module configured to initiate remedial actions, assign tasks and log activities based on predefined standard operating procedures (SOPs); (e) an audit readiness and reporting module configured to generate compliance logs, audit trails and standardised regulatory reports in real time; and (f) an adaptive learning and feedback optimization module configured to refine rule sets and predictive models based on feedback, historical data and regulatory updates; g) the modules are integrated into a cloud infrastructure to enable real-time, scalable and predictive compliance and risk management across biomedical and pharmaceutical processes.

The invention provides a medical auxiliary diagnosis method and system based on time sequence and semantic weighting, and belongs to the technical field of medical information processing. Constructing the preprocessed current and historical medical record information of the patient into patient medical record data, and inputting the patient medical record data into a pre-trained medical language model to generate a preliminary diagnosis result; the comprehensive weight of the patient medical record data vector sequence is calculated through a time decay function and the content correlation weight, a medical record fusion vector is obtained through fusion, and the medical record fusion vector and the preliminary diagnosis result are spliced into a query vector; searching candidate fragments in a clinical guide knowledge base based on the query vector, calculating semantic evidence scores and coverage scores based on patient medical record data and tokens of the candidate fragments, obtaining sorting probabilities of the candidate fragments in combination with metadata prior scores of the candidate fragments, and screening out target guide fragments; and the preliminary diagnosis result and the target guide fragment are fused to generate final auxiliary diagnosis and treatment information, so that auxiliary diagnosis and treatment suggestions with traceability, verifiability and authoritative basis are provided.

The invention discloses an auxiliary film reading method and system based on artificial intelligence, and the method comprises the steps: 1, collecting a pathological WSI, an electronic medical record, detection data and equipment parameters, correcting the equipment difference through adaptive dyeing normalization, and constructing a structured data package associated with an ID-timestamp of a patient; 2, developing a dynamic branch CNN, migrating teacher model knowledge through knowledge distillation, and introducing federated learning; step 3, the edge generates a thermodynamic diagram to mark a suspicious area, and the cloud outputs a structured report; step 4, constructing a normal tissue feature space by the variational auto-encoder, detecting abnormal slices and triggering expert re-checking; a reverse automatic encoder generates a pseudo-health image to compare and position a pathological area, and dynamic weight adjustment balances the federal learning convergence speed; 5, integrating the thermodynamic diagram, the gene data and the clinical indexes by a three-dimensional platform, and supporting multi-dimensional superposition display; webGL realizes browser end rendering, and NLP automatically generates a report abstract marked with a key evidence chain and is in butt joint with an international diagnosis and treatment guide.

The present invention relates to a method for quantitatively evaluating the quality health of pharmaceutical manufacturing sites; specifically, the invention pertains to a method that measures audit readiness, data integrity compliance, and overall operational performance of the pharmaceutical manufacturing sites.

The invention relates to the technical field of medical information, in particular to a method and a system for processing medical record documents by utilizing artificial intelligence, and particularly relates to a method and a system which can receive and process multi-modal input information including images, videos and voices, can work cooperatively through a multi-agent system and can process the medical record documents. The invention discloses a system for automatically generating, controlling quality and safely inputting medical records in combination with a medical knowledge base and an implementation method thereof. The invention relates to an automatic medical record writing system based on multi-modal input and multi-agent driving. The automatic medical record writing system comprises a multi-modal input module, a multi-agent processing platform and a safety intranet input module. According to the method, establishment of the data channel between the medical intranet and the external AI system is proposed for the first time, the problem of medical intranet isolation is solved, industrial pain points are solved in a breakthrough mode, and a new medical AI landing path is developed.

The invention provides a medication guidance generation system, and relates to the technical field of medical informatization. Comprising a knowledge base construction module used for constructing a medication guidance knowledge base; the graph term construction module is used for constructing a knowledge graph and a standard term set according to the medication guidance knowledge base; the index construction module is used for splitting the medication guidance knowledge base and constructing a vector index and a full-text index; the personal information processing module is used for acquiring personal information of a patient and preprocessing the personal information to obtain preprocessed personal information; the mixed detection module is used for performing retrieval by utilizing a knowledge graph, a vector index and a full-text index according to the preprocessed personal information to obtain a retrieval result set and obtain an intersection result according to the retrieval result set; and the generation module is used for generating medication guidance corresponding to the target patient according to the intersection result. The problems that traditional medication guidance mainly depends on experience and professional knowledge of medical staff, and is low in efficiency, prone to errors and difficult to personalize are solved.

The invention belongs to the technical field of medical treatment and public health, and particularly relates to a hypertension group knowledge recommendation method and system based on a large language model multi-agent. Hypertension knowledge question-answer pair information, medical short video information and user personal health portraits are finely extracted by constructing a hypertension knowledge question-answer pair library; according to the method, video content quality evaluation and matching degree evaluation are performed, and user platform interaction data adjustment is combined, so that personalized and accurate knowledge recommendation services can be provided for hypertension users, the requirements of hypertension patients for health knowledge are met, the accuracy and effectiveness of knowledge recommendation are improved, and self-health management of the patients is facilitated.

The invention discloses a medical diagnosis report generation method based on a large model and interaction and related equipment, and belongs to the technical field of natural language processing and medical informatization. According to the invention, through multi-modal data acquisition and dynamic inquiry path adjustment, collection of comprehensive clinical information is ensured. And a symptom-examination result consistency verification and diagnosis basis sufficiency evaluation mechanism is introduced, so that the logic preciseness of medical judgment is ensured. By constructing the medical knowledge graph, related diagnosis and treatment guidelines are automatically provided, and omissions and wrong references caused by manual consultation are avoided. A first draft of differential diagnosis and treatment suggestions is automatically generated by means of a large language model, and the professionalism and safety of report content are improved in combination with examination and verification of a clinical rule engine. A doctor participates in modification through multiple rounds of interaction in the whole process, the main right of professional judgment is reserved, intelligent support provided by AI is fully utilized, and therefore the finally generated diagnosis report conforms to the actual clinical situation and meets medical specifications and specialized requirements.

The invention discloses a mobile rescue intelligent management method based on RFID automatic identification and AI artificial intelligence interaction, and belongs to the field of mobile rescue intelligent management. According to the invention, the problem of low efficiency of medicine and material inventory management of the rescue carriage in an inpatient area due to an existing manual inventory mode is solved, batch accurate identification can be carried out by adopting an RFID remote identification technology, batch inventory is realized to replace one-by-one checking, the inventory efficiency is effectively improved, and the inventory management cost is reduced. Manual electrocardiogram data checking is replaced by image visual recognition of electrocardiogram data, secondary recording is not needed, rescue data can be automatically recorded, rescue efficiency is improved, medical advice recognition is assisted by AI voice instead of manual recording of medical advice, the workload of nurses is reduced, the workload of secondary recording is reduced, and rescue efficiency is improved. And through a rescue scene full-process digital AI closed loop, digital tracing can be completed in the rescue process, and the problems that traditional handwritten records and data are not real-time and inaccurate are solved.

A surgical system comprising a surgical instrument and a surgical hub is disclosed. The surgical instrument is configured to perform a surgical procedure and comprises a housing, a shaft assembly, an end effector, and a sensor configured to sense a parameter associated with a function of the surgical instrument. The surgical hub comprises a processor and a memory. The memory stores instructions executable by the processor to receive data from the sensor, wherein the data comprises the parameter associated with the function of the surgical instrument; infer a step of the surgical procedure being performed by the surgical instrument, based on the data from the sensor; receive data from a database, wherein the data from the database is associated with the step of the surgical procedure; compare a value of the parameter to the data received from the database; and control the surgical instrument based on the comparison.

An interactive and dynamic surgical simulation system may be used in the context of a computer-implemented interactive surgical system. The surgical simulation system may provide coordinated surgical imagining. A processor may be configured to execute a simulation of a surgical procedure. The surgical procedure may be simulated in a simulated surgical environment. The processor may generate a first visual representation and a second visual representation. The first visual representation may be of a first portion of the simulated surgical environment. The second visual representation may also be of the first portion of the simulated surgical environment.

A hospital quality abstraction is automatically generated from health records. In some examples, a large language model (LLM) is queried with prompts selected to elicit data to generate a hospital quality abstraction report. The LLM outputs are combined with patient data from health records to improve the accuracy of the responses to the LLM queries. These methods and systems may improve the operation of the LLM, which is deeply needed particularly for healthcare.

Systems and methods for providing an intelligent network to onboard patients to specialty medications are described herein.

The invention relates to a personalized traditional Chinese medicine health-care intelligent recommendation system combined with an acupuncture theory. The system comprises a multi-modal data acquisition module, an AI diagnosis module and an acupuncture scheme generation module. According to the invention, the health information of the user is acquired through the multi-modal data acquisition module, the deep learning model fused with the acupuncture meridian theory is utilized to realize physique identification and disease diagnosis, and the personalized acupuncture health care scheme is generated based on the established acupuncture point, physique and disease three-dimensional association database. A reinforcement learning algorithm is combined with a user real-time feedback dynamic optimization scheme, the limitation that traditional acupuncture depends on physician experience is broken through, intelligentization, individuation and dynamics of acupuncture health care are achieved, the accuracy and operability of the health care scheme are improved, and the method is suitable for various scenes such as family health care and community medical treatment and has a wide application prospect. The method has remarkable innovativeness and practicability, the physique and the disease of the user are accurately identified based on multi-modal data, the acupuncture scheme adaptive to personal characteristics is generated, and the problem that a traditional universal scheme is insufficient in adaptability is solved.

The invention relates to the technical field of reinforcement learning, and discloses a chronic disease intervention scheme generation method and system based on reinforcement learning and multi-modal data, and the method comprises the steps: obtaining the multi-modal data of a patient, the multi-modal data at least comprising electronic medical record data, voice data, image data, text data and physiological time sequence data; performing feature extraction on the multi-modal data to obtain a multi-modal feature vector; on the basis of the multi-modal feature vectors, health state vectors are constructed, and the health state vectors at least comprise a physiological risk score, a treatment compliance score and a lifestyle health degree score; designing a reward function according to the dynamic change of the health state vector; optimizing the strategy network in a predefined intervention action space according to the reward function by utilizing a reinforcement learning algorithm so as to output an optimal intervention action; and converting the optimal intervention action into personalized natural language interaction content through a generative AI model. According to the invention, the efficiency, precision and patient compliance of chronic disease management can be significantly improved.

A device to process data associated with a surgical event of a surgery may include a processor. The processor may be configured to receive multiple data streams during the surgical event. The processor may be configured to select a primary data stream based on a surgical data interface via which the primary data stream is received. The processor may be configured to select a secondary data stream based on a surgical data interface via which the second data stream is received. The processor may be configured to identify the surgical data interfaces. The processor may be configured to generate situational data associated with the primary data stream based on the secondary data stream. The situational data may indicate a medical decision-making factor of the surgical event. The primary data stream and the situational data may be sent during the surgical event.

A large language model-based medical examination conclusion generation method includes: obtaining a target manifestation text corresponding to a target medical examination; extracting medical examination inference knowledge that matches the target manifestation text from a medical examination inference knowledge base, where the medical examination inference knowledge includes a manifestation text and a conclusion text corresponding to a medical examination; constructing a sample based on the extracted medical examination inference knowledge, and constructing a prompt text based on the sample and the target manifestation text; and inputting the prompt text into a large language model, and outputting, by using the large language model, a target conclusion text that corresponds to the target medical examination and that is obtained by performing inference based on the target manifestation text and under guidance of the sample.

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.

A clinical decision support system and method for patients with pulmonary arterial hypertension is disclosed herein. The system may comprise a processor to process instructions to execute one or more pulmonary arterial hypertension risk algorithms configured to generate a risk score value associated with a patient surviving within a given time period. The system may comprise a means for input and output, wherein input variable data may be received and a set of risk score values may be displayed. A method for operating the clinical decision support system is also disclosed.

The invention discloses an intelligent generation and maintenance system and method for a dynamic cerebral apoplexy knowledge graph, and relates to the field of intelligent medical treatment, and the system comprises a data access and preprocessing module which is used for accessing and retrieving cerebral apoplexy related information from a multi-source data source; the knowledge extraction module based on LLM is used for acquiring candidate knowledge fragment streams; the knowledge graph structuring and filling module is used for integrating the candidate knowledge fragment flow into a formal knowledge graph structure stored in a graph database; the LLM-driven verification and refinement module is used for detecting the candidate knowledge fragment stream and the formal knowledge graph structure to obtain a detection result; and the dynamic updating and maintaining module is used for realizing dynamic fusion and version control of the formal knowledge graph structure. Through modular architecture design, a high-performance knowledge extraction and verification mechanism of a large language model and a dynamic processing capability oriented to continuous updating are fused, and high automation, standardization and intelligentization of a medical knowledge graph construction process are realized.

The invention relates to the technical field of medical treatment, and discloses a knee joint knee protection treatment personalized planning system based on multi-modal data fusion. A data acquisition and preprocessing module of the system acquires a multi-modal data stream of the knee joint of a patient in real time and generates a standardized data packet; the treatment event identification module is used for automatically identifying and verifying key treatment events from the standardized data packet and generating a treatment event sequence; the historical treatment path query module queries a distributed database and extracts treatment modes of similar patients; the graph matching and path generation module is used for calculating the similarity between the current treatment event sequence and a historical treatment mode, generating a fusion treatment path when the similarity exceeds a threshold value and storing the fusion treatment path to the block chain network; the abnormity monitoring and analysis module monitors abnormal event points in the fusion treatment path in real time, triggers a path decomposition mechanism and performs multi-dimensional performance analysis; and the personalized scheme generation module is used for dynamically adjusting treatment parameters and generating a personalized treatment planning scheme.

The invention discloses an intensive care treatment scheme intelligent decision-making system based on data driving, and relates to the technical field of medical information, and the system comprises a data collection module which is used for obtaining multi-source clinical data of an intensive care unit in real time; and the decision generation module is connected with the data acquisition module and is used for generating a treatment scheme according to the clinical data. According to the intensive care treatment scheme intelligent decision-making system based on data driving, through four-dimensional snapshot construction of a dynamic decision-making chain and medical interpretable evolution relation labeling, continuous logic of an intensive care decision-making is guaranteed, data abnormal driving change and medical logic jump risks are effectively identified, medical errors caused by decision-making faults are reduced, and the decision-making efficiency is improved. The abstract decision-making process is converted into a concrete evidence chain capable of penetrating auditing, and the clinical responsibility traceability and the doctor-patient credibility are improved.

The invention relates to the technical field of auxiliary diagnosis and treatment systems, in particular to an intelligent auxiliary diagnosis and treatment system for doctors. Comprising a system initialization and role service registration module, a user input analysis and intention recognition module, an intelligent routing and professional tool set selection module, an SSE streaming response initialization and workflow starting module, a knowledge graph retrieval and reasoning module, a diagnosis and treatment logic reasoning and scheme generation module and a streaming result output and node state updating module. A dynamic knowledge update triggering and verifying module; according to the invention, on the system integration level, efficient integration of multi-professional medical resources is realized based on role-as-a-service architecture, and complete coverage of multi-professional knowledge modules of internal medicine, surgery, oncology and the like is ensured through a role service registration validity evaluation mechanism.

The invention discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

An example of a context sensitive guidance (CSG) system for guiding caregivers providing medical care for a victim, for example a trauma victim, includes a CSG engine and contextual data sources communicatively coupled to the CSG engine. The contextual data sources include a medical device configured to collect physiologic data and provide the physiologic data to the CSG engine and an emergency environment data source configured to receive emergency environment data and provide the data to the CSG engine. The CSG engine includes hardware logic and / or software logic configured to receive the contextual data, evaluate protocols based on the contextual data, select at least one action item based on the protocols, generate at least one instruction based on the action item(s), the instruction(s) including a caregiver instruction and / or a medical device instruction, and provide the instruction to a caregiver interface device and / or the medical device.

To provide systems and methods allowing for coach-counselor assistance to the individual-user or for automated content delivery.SOLUTION: An interface of an electronic device 56 to demonstrate an example of a user's direct interaction with the smoking cessation behavior modification program system / method includes any number of data subsets previously including, but not limited to, subsets of data, and shows submitted biological data 76, applications 78, and prompts 40. Biological data 76 shows a measured exhaled CO reading value (shown as "3") submitted by the user by using an exhaled breath sensor. Application data shows an application entry of the number of cigarettes smoked over a period of time (shown as "8"). A display includes any associated text as needed, as well as a control panel 46 for interacting with the system and / or coaches.SELECTED DRAWING: Figure 7A

The invention provides a multi-center special disease first-aid cooperation and data closed-loop method and system based on AI, and relates to the technical field of medical information, and the method comprises the steps: extracting diagnosis and treatment information from multi-center special disease first-aid historical cases, constructing a knowledge pedigree, and formulating diagnosis and treatment standard specifications; calculating an optimal hospital by using federated learning based on the first-aid data and the decentralized node network; feature extraction and correlation analysis are carried out on special disease first-aid data, influence factors are positioned through a traceable chain, and dynamic updating of special disease knowledge pedigree is realized. According to the invention, the cooperative efficiency of special disease first aid is improved, optimal allocation of first aid resources is realized, and a data-driven special disease diagnosis and treatment continuous optimization mechanism is formed.

The invention provides a medical image visualization analysis system based on deep learning, and relates to the field of artificial intelligence. The objective of the invention is to overcome the defects of an existing system in the aspects of deep learning model interpretability, clinical interactivity and multi-modal data fusion. The system comprises an image data acquisition and standardization module, a deep feature extraction and representation learning module, a multi-task intelligent analysis module, an interpretability analysis module, a multi-dimensional visualization and interaction module, a clinical knowledge fusion and feedback learning module and a system management and integration module. Through the system, the diagnosis efficiency and accuracy can be improved, the trust of doctors is enhanced, the obstacle of a traditional black box model is overcome, and a new man-machine cooperation intelligent diagnosis normal form is constructed.

The invention discloses a knowledge graph-based cerebral hemorrhage clinical pathway intelligent recommendation method and system, and the method comprises the steps: constructing a pathway knowledge graph and an illness state grading system through analyzing a diagnosis and treatment guide, recognizing a key node of pathway execution and an illness state grade switching boundary, and configuring a differentiation decision rule for a single illness state region and a mixed illness state region; the method comprises the following steps of: performing dimensionalization acquisition on multi-source illness state data of a patient, extracting illness state characteristics, monitoring a dynamic index change trend, generating illness state description through weighted characteristic extraction and dynamic early warning analysis, and calculating a matching degree between a current illness state and a candidate path; when path execution deviation is detected, a deviation source node is positioned through cross-stage transitivity analysis and reverse tracing, and a targeted path correction instruction is generated; normativity of an adjustment scheme is ensured through node integrity verification and forced constraint correction, a clinical pathway recommendation result matched with the actual illness state of the patient is output, and adaptability and accuracy of clinical pathway recommendation are improved.