631results about "Medical practises/guidelines" patented technology

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.

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.

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.

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.

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.

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 present disclosure provides apparatus and methods to both infer stent expansion (vessel expansion or compliance) and probability of procedural success (vessel patency) from pretreatment diagnostic imaging at the point-of-care and also train an inference model to generate such an inference, thus allowing a physician to choose with greater certainty an optimal treatment tool and treatment protocol for treating a vessel of a patient.

The invention discloses a first-aid medical knowledge graph construction method and device, equipment, a medium and a product, and relates to the technical field of computers. The first-aid medical knowledge graph construction method comprises the following steps: acquiring a first-aid medical knowledge data set; knowledge extraction is conducted on the first-aid medical knowledge data set through a pre-trained final condition time sequence attention mechanism model, structured relation data is obtained, and the final condition time sequence attention mechanism model is obtained through training of an improved meta-learning framework; and performing knowledge graph construction on the structured relation data to obtain a first-aid medical knowledge graph. According to the method, training is carried out by improving the meta-learning framework, the small sample problem is solved, the final condition time sequence attention mechanism model more adaptive to the first-aid medicine direction is obtained, the accuracy of extracting the relation in the first-aid medicine knowledge data set is improved, and the reliability of the knowledge graph is improved.

A method of displaying an operational parameter of a surgical system is disclosed. The method includes receiving, by a cloud computing system of the surgical system, first usage data, from a first subset of surgical hubs of the surgical system; receiving, by the cloud computing system, second usage data, from a second subset of surgical hubs of the surgical system; analyzing, by the cloud computing system, the first and the second usage data to correlate the first and the second usage data with surgical outcome data; determining, by the cloud computing system, based on the correlation, a recommended medical resource usage configuration; and displaying, on respective displays on the first and the second subset of surgical hubs, indications of the recommended medical resource usage configuration.

The present invention provides a method and system for automatically and seamlessly processing clinical trial source data into electronic data capture (EDC) systems. In one embodiment, a file structure is defined for an electronic binder system (eBinder); source data is uploaded to the eBinder; the source data is encrypted, Patient Identifiable Information in the source data is masked; the source data is converted into machine readable plain text in the JavaScript Object Notation (JSON) format using Natural Language Processing (NPL) technologies; the JSON data is converted into tabulated machine readable data in the HyperText Markup Language (HTML) format using NPL technologies; the HTML data is converted into machine understandable data using NPL technologies; the machine understandable data is populated into EDC datasets using NPL technologies; the source data and converted data are displayed side-by-side for source data verification; and a platform is provided for regulatory data verification or auditing.

Techniques for evaluating adherence to clinical guidelines using large language models (LLMs) are described. A computer-implemented method comprises extracting information from clinical guideline data describing a clinical guideline for providing medical care to patients using a LLM generating a prompt decision tree (PDT) representative of the clinical guideline using the information, and employing the PDT to perform a question and answering session for a patient, comprising sequentially selecting prompt questions from the PDT based on respective answers to immediately preceding prompt questions and sequentially applying each of the prompt questions as input to the LLM or another LLM to extract the respective answers for each of the prompt questions from patient data for the patient. The method further comprises determining adherence information regarding whether and to what degree the patient received the medical care in accordance with the clinical guideline based on the prompt questions and the respective answers.

The present invention relates to an AI-based system and method for generating enhanced radiology reports. The system comprises a database for storing multimodal patient data, a natural language processing (NLP) module for extracting clinical information, and a machine learning module for correlating the clinical information with radiology images to identify diagnostic insights. An AI-based report generation module analyzes the images and clinical information to generate a preliminary report, which is refined based on radiologist input. The generated report is then integrated into the patient's electronic health record. The system employs techniques such as multimodal deep learning, active learning, explainable AI, and federated learning to enhance diagnostic accuracy, capture expert feedback, provide transparency, and enable multi-institutional collaboration. The invention aims to improve the accuracy, efficiency, and value of radiology reporting in patient care.

A system and method for improving patient outcomes by dynamically selecting the medical products used for a population to optimize patient outcomes is disclosed.

Systems and methods for providing an intelligent network to onboard patients to specialty medications are described herein. A server system for managing communication is configured to receive from a user at a healthcare practice, an electronic request to obtain a medical prior authorization (medPA) for a specialty drug to administer for a patient of the healthcare practice; select, using a rules service, a communication pathway to submit the medPA to a healthcare payer from a plurality of communication pathways; and initiate a connection with the healthcare payer using the selected communication pathway.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating answers to medical questions using neural networks and other components. In one aspect, a method includes: obtaining question data representing a medical question; obtaining a plurality of document snippets from a medical database that stores medical documents; for each document snippet in the plurality of document snippets, determining a relevance score for the document snippet by using a ranking neural network based on the document snippet and the medical question; selecting, based at least in part on the relevance scores, a subset of the plurality of document snippets; generating a prompt that includes (i) the medical question and (ii) the subset of the plurality of document snippets; and generating an answer to the medical question based on processing the prompt using a generative neural network.

The invention provides a clinical decision knowledge graph construction method and system, and the method comprises the steps: extracting standardized entities corresponding to diseases, symptoms and diagnosis and treatment elements from medical knowledge data; constructing a clinical concept knowledge graph for representing a medical concept logic relationship and a causal relationship according to the semantic association relationship and the causal dependency relationship among the standardized entities; a diagnosis event, an examination event and a treatment event related to the patient are extracted, link evidences among the events are determined based on the event chain relation among the events, and a clinical event knowledge graph used for representing the disease course evolution process of the patient is constructed according to all the link evidences; and performing knowledge element fusion based on an entity association relationship between the clinical concept knowledge graph and the clinical event knowledge graph, and generating a target knowledge graph for clinical decision analysis. By adopting the scheme of the invention, the cross-map fusion of the static medical concept knowledge and the dynamic disease course event chain relationship can be realized, and the clinical decision knowledge structure with the reasoning ability can be constructed.

A system for confirming an advisory interaction with an artificial intelligence platform. The system includes a constitutional generator module configured to receive a first advisory input, retrieve an expert input, select a machine-learning process as a function of the expert input, and generate a therapeutic corrector. The system includes a constitutional advisory module configured to display a therapeutic corrector on a graphical user interface and receive a second advisory input. The system includes a best practices module the best practices module designed and configured to retrieve from an expert database a best practices training set, calculate an optimal vector output, generate an optimal vector output containing an expected therapeutic corrector implementation response, authenticate a second advisory input, and update the best practices module.

The invention discloses an intelligent recommendation system for health management, which belongs to the technical field of intelligent medical treatment and comprises an acquisition module, a knowledge analysis module, a calculation processing module and an interaction module. The system collects original health data, manages a health knowledge graph and a safety recommendation rule base, constructs an initial health portrait by using the original health data, generates a first recommendation list and a second recommendation list based on the initial health portrait and a logic rule base, generates a recommendation card through the first recommendation list and the second recommendation list, and sends the recommendation card to a user. And collecting user behavior data for feedback. According to the method, the problems of high cold start difficulty, insufficient safety guarantee and limited personalized effect in the existing health recommendation service are solved, the initial availability and recommendation scientificity of the system are improved, the health behavior of the user can be effectively guided, and the method has positive significance in improving the health level of people.

The invention relates to the technical field of intelligent medicine recommendation, and provides a medicine recommendation method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the time sequence coding processing of the current treatment information of a patient, and generating a semantic vector of the current treatment information; performing multi-message propagation processing on the heterogeneous medical graph based on a graph convolutional neural network to determine knowledge memory vectors of various medical entities, and determining knowledge vectors of the current doctor seeing information based on the knowledge memory vectors; determining fusion information according to the semantic vector and the knowledge vector; and inputting the fusion information into a drug recommendation model, carrying out probability calculation processing that new drugs are used and probability calculation processing that historical drugs are reserved and used on the fusion information, and based on the determined newly-added use probability of each drug, the reserved use probability of each historical drug and an adjacent matrix of drug interaction, carrying out drug recommendation. And determining the drug combination recommended to the patient. The safety of medicine combination is improved, and the accuracy of medicine recommendation is effectively improved.

Apparatuses, systems, and methods are disclosed to manage and process surgical data, and enable collaboration between two or more surgeons or other clinicians. A repository may be created that includes surgical data (including video data and / or 2024 / 030683 or radiological image data) that may be selectively shared between medical professionals. One or more trained neural networks may process the annotated surgical reports in order to populate the repository. Additional trained neural networks may generate surgical recommendations in response to user requests, based on contents of the repository. Other trained neural networks may generate surgical templates to guide a surgeon during an operation.

The invention relates to the technical field of chronic disease health management, and discloses a chronic disease health management method based on data analysis, and the method comprises the following steps: S1, obtaining medical records, wearable device data, laboratory results, self-reporting information and environment data of a patient through a multi-source collection gateway; and S2, performing desensitization processing and feature extraction on the data under a federated learning framework, and generating a multi-modal patient portrait containing time sequence, spatial features and biomarkers. According to the chronic disease health management method and system based on data analysis, through desensitization processing under a federated learning framework, on the premise of protecting patient data privacy, medical records, wearable device data, environment data and other multi-source information are effectively integrated, a comprehensive multi-modal patient portrait is formed, and the patient experience is improved. The problem that data are mutually separated in a traditional management mode is solved, and comprehensive data support is provided for subsequent risk prediction and intervention decision making.

The present invention relates to systems and methods for optimizing plasma collection volume. A plasma withdrawal system and a method for operating a plasma withdrawal system are provided by which the volume / weight of the anticoagulated plasma collected is optimized. In one example, a nomogram is provided that utilizes the donor hematocrit to calculate the volume / weight of raw plasma within a plasma product having a maximum volume allowed by the FDA nomogram. In a plasma withdrawal process having multiple collection phases followed by a reinfusion cycle in which concentrated red blood cells are returned to the donor, the volume of plasma product to be collected is calculated prior to the start of each collection cycle to account for the increasing hematocrit of the donor, resulting in a greater total volume of plasma product to be collected during the plasma withdrawal process.

A system and method for medical imaging protocol name standardization includes generating a synthetic training dataset from medical standards in public documentation utilizing knowledge elicitation, wherein the synthetic training dataset includes, for a given language and a given imaging modality, a plurality of combinations of possible medical imaging protocol names for respective standard protocol codes of a plurality of standard protocol codes for each standard medical imaging protocol. The system and method also includes generating a lightweight text classification model from the synthetic training dataset utilizing machine learning. The system and method further includes utilizing the lightweight text classification model to receive a medical imaging protocol name and to output a list of most probable protocol codes based on the medical imaging protocol name.

To provide a browsing support system for accurately describing medical-related information.SOLUTION: An information processing terminal 10 provided in a browsing support system 1 includes an input part 12 for creating a question to be registered in an electronic medical chart displayed on a display part 11. A medical chart management system 3 has a medical chart management part 31 for outputting creation information for creating an electronic medical chart input by an information processing terminal 10 to a RAG system 4, receiving an answer from the RAG system 4, and supporting the creation of the electronic medical chart. The RAG system 4 has a vector DB52 for storing knowledges vectorized and embedded in a vector space, and a knowledge-retrieving part 44 for retrieving the knowledges from the vector DB52 on the basis of the vectorized creation information, and outputting an answer obtained by forming the retrieved knowledges to the medical chart management part 31.SELECTED DRAWING: Figure 1

The invention provides an electronic health record enhanced reasoning method and system based on a thinking map and reinforcement learning, and the method comprises the steps: carrying out the definition and classification of an electronic health record EHR analysis task, and dividing the EHR analysis task into a clinical decision task and a risk prediction task; constructing an EHR reasoning data automatic generation process, including entity extraction and co-occurrence analysis, construction of a thinking graph based on a knowledge graph, reasoning path synthesis and data generation; and performing reasoning enhanced EHR analysis large language model training, including continuous pre-training, EHR reasoning data instruction fine tuning and EHR analysis task reinforcement learning. According to the method, the external medical knowledge graph is utilized to construct the thinking graph, two-stage post-training and reinforcement learning strategies are combined, and the reasoning ability, diagnosis accuracy and generalization of LLM in various EHR analysis tasks are improved.

The invention provides a pneumonia medical data analysis and processing method, system and device and a storage medium, and belongs to the field of medical image processing. Multi-scale features of the lung CT image are extracted layer by layer through convolution, global pooling operation down-sampling is carried out based on the multi-scale features, and saliency features of lung lobe and lesion areas are captured from local details to global semantics; performing convolution and multi-scale pooling on the saliency features to generate a feature map fusing global and local information; and recovering the feature map through layer-by-layer up-sampling to obtain a segmentation result of the lung lobe and the focus. Quantitative indicators are calculated based on regions and pixels of the segmentation results of the lung lobes and lesions. And performing model processing based on the quantitative index, the segmentation result of the lung lobe and the focus and clinical data of the lung lobe and the patient to obtain pneumonia focus characteristic data. And a doctor can conveniently make an objective and reasonable treatment scheme according to a result of lesion segmentation and feature data calculation based on the pneumonia lesion feature data.