32 results about "Patient characteristics" patented technology

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 provides a highland thrombosis disease risk early warning system, comprising: a sensing acquisition module, which is used for acquiring patient characteristic data, the patient characteristic data including physical sign data and individual baseline data; an index analysis module, which is used for preprocessing the patient characteristic data and generating risk indexes; a risk assessment module, which is used for assessing a risk coefficient according to the risk indexes; and a risk early warning module, which is used for obtaining a risk degree corresponding to the risk coefficient according to a pre-constructed risk classification standard, and implementing corresponding risk early warning according to the risk degree. The application solves the problem in the prior art that a portable health monitoring device cannot accurately measure core hemodynamic parameters closely related to thrombosis, and also cannot accurately perform risk early warning on highland thrombosis diseases according to the acquired parameters.

This invention discloses a hybrid model-driven nutritional risk prediction method and system for precision nutritional intervention. The method constructs a large-scale patient typing model containing multiple typical patient types and their associated assessment parameter packages, integrating a standardized first screening model with a personalized second screening model. In application, the first screening model is first used to standardize and initially screen patient data. Then, the large-scale patient typing model extracts patient feature inputs based on the patient data and initial screening results for typing and obtains the corresponding assessment parameter packages. The second screening model is then personalized based on these parameter packages, supplemented with key data, and combined with personalized thresholds to calculate the final risk level and generate a comprehensive assessment report. This invention achieves dynamic individualized adaptation of assessment strategies, improves the accuracy and clinical interpretability of risk identification, and optimizes workflow and resource allocation.

The application relates to the technical field of cardiovascular disease diagnosis and treatment and artificial intelligence, and discloses a cardiovascular disease diagnosis and treatment scheme optimization system based on architecture, which comprises an original data preprocessing module, which is used for collecting and standardizing time series monitoring data and static data of medical history texts, adopts an improved and normalized algorithm, utilizes model structured text data, and outputs standardized patient feature data; and an improved logic analysis module, which is used for receiving the standardized patient feature data, optimizing the architecture by introducing a sparse attention mechanism, and performing time series correlation analysis, pathological feature mapping and individual difference modeling. The improved logic analysis module is used for introducing the sparse attention mechanism, deeply mining time series correlation and implicit pathological logic coupling relationships in complex time series monitoring data, generating a high-dimensional patient state feature vector, and solving the problem that traditional methods are shallow in analysis and cannot accurately identify individualized pathological states.

This invention discloses a method for medication risk warning and dynamic intervention using a smart pillbox for elderly patients with chronic diseases, relating to the fields of intelligent medical devices and chronic disease management technology. The method first constructs four types of chronic disease knowledge graphs: medication behavior, physiological indicators, drug interactions, and environmental conditions. Then, it processes the graphs using a GNN module with a GRU-like gating mechanism, weightedly fusing multi-time-step features using a time-step attention mechanism, and integrating similar patient features based on Jaccard similarity to generate comprehensive risk features. During the training phase, the risk level and type are output after feature optimization using a collaborative attention mechanism. In the inference phase, a similarity propagation mechanism is used to address the cold-start problem for newly enrolled patients, matching a three-level intervention rule base to generate personalized strategies, and iteratively optimizing the model based on intervention feedback. This invention achieves proactive and precise warning and dynamic intervention, improving risk assessment performance and patient medication adherence in different scenarios.

This invention relates to the field of neuroelectric stimulation technology, specifically a method and system for current regulation during neuroelectric stimulation. The method identifies the target nerve location and depth based on tissue impedance distribution data and patient characteristic parameters, using a deep neural network model to obtain a recommended initial stimulation current value and a personalized target amplitude. It acquires the deviation between the evoked potential amplitude and the personalized target amplitude, and analyzes this deviation with waveform characteristic parameters to obtain a first current regulation step value to adjust the coarse stimulation current until the evoked potential amplitude exceeds the personalized target amplitude. At this point, the current coarse stimulation current value is recorded as the optimized stimulation current value, and a comprehensive quality score is obtained. Based on the comprehensive quality score, a second current regulation step value is dynamically calculated to adjust the fine-tuned stimulation current value. When the comprehensive quality score exceeds a preset quality score threshold, the final stimulation current value is obtained. This effectively improves the accuracy of current regulation during neuroelectric stimulation for different patients.

The application discloses a kind of multi-factor risk assessment methods for ovarian hyperstimulation, it is related to intelligent medical technical field, including, based on multi-source baseline data package, the trend of follicle volume change with time is calculated by ovarian response simulation, obtain follicle response curve, the probability of vascular injury is calculated by puncture path hemorrhage simulation, obtain bleeding probability, and the follicle response curve and bleeding probability are fused, form patient characteristic risk vector;According to the intervention suggestion in the structured strategy list is associated with ovarian hyperstimulation risk score and bleeding risk score to generate risk change description, and form risk assessment report according to risk change description;The application constructs the patient characteristic risk vector by fusing follicle dynamic response and puncture bleeding risk, effectively enhance the safety and individualization level of assisted reproductive treatment.

The invention relates to a system, a computer program and a computer program product for automatically planning and / or preparing a clinical intervention for implanting a heart valve in a patient. The system (100) comprises an input interface (101) for inputting data and a computing unit (102). The computing unit (102) is adapted to receive input data via the input interface (101). The input data comprises patient data (201) relating to patient characteristics, heart valve data (202) relating to an orientation of a prosthetic heart valve (202) relative to a delivery tool (401) to be used for implantation and / or relative to a loading tool (402) for loading the prosthetic heart valve (202) into the delivery tool (401), preferably placement data (203) relating to a placement of the delivery tool (401) relative to the patient, in particular before inserting the delivery tool (401) into a blood vessel of the patient. The computing unit (102) is adapted to determine orientation data (300) based on the input data, the orientation data (300) relating to a desired orientation of the prosthetic heart valve (2) in a final position of the prosthetic heart valve (1) relative to the native heart valve (1), in particular relative to a coronary artery (RCA, LCA) and / or relative to an annulus (4).

The application discloses an obesity whole-cycle management system and method based on an artificial intelligence large model, relates to the technical field of medical artificial intelligence, and comprises a multi-modal data fusion and preprocessing module, an obesity special disease large model core module and a whole-cycle management application module.The multi-modal data fusion and preprocessing module integrates and processes obesity related data of a target patient to generate structured multi-modal patient data.The obesity special disease large model core module processes the data through a large language model trained in a professional manner to output a pre-diagnosis brief containing demand conflict points and an initial individualized intervention scheme containing dietary suggestions and exercise plans.The whole-cycle management application module generates a customized management scheme according to the initial scheme and a patient characteristic portrait.The application provides reliable decision support through multi-modal data processing and professional model analysis, realizes standardized individualized diagnosis and treatment, and improves the whole-cycle management effect.

This invention belongs to the field of infectious disease prevention technology, specifically relating to a method for visualizing infectious disease virus fields based on spatiotemporal trajectory data. The method includes: collecting multi-dimensional data related to infectious diseases, including spatiotemporal trajectory correlation data, virus detection data, and patient-related data (based on data from children with diarrhea at Tianjin Children's Hospital from July 2020 to August 2023); cleaning, standardizing, coding, and statistically analyzing the data; constructing a spatiotemporal distribution field, a genotype distribution field, and a risk correlation field based on a spatiotemporal axis; and visually presenting the data using a combination of various charts such as bar charts, line charts, statistical tables, and phylogenetic trees, combined with an interactive interface. This method integrates multi-source data to accurately display the spatiotemporal epidemic characteristics of infectious diseases, the evolutionary patterns of viral genotype distribution, and the correlation between genotypes and patient characteristics. All results are based on measured data, providing scientific support for infectious disease prevention, control, and treatment, and improving the efficiency and accuracy of data application.

This invention discloses an intelligent triage and examination path planning method and system based on a multimodal medical guidance robot, belonging to the field of smart healthcare technology. The method includes: acquiring patient information through the multimodal medical guidance robot; analyzing the patient information to generate a preliminary examination item list; performing predictive resource pre-planning based on the preliminary examination item list and oriented towards the treatment path; acquiring the real-time status of hospital examination resources and determining a planning strategy based on patient characteristics; generating a preliminary spatiotemporal path sequence based on the planning strategy, the real-time status, and the preliminary examination item list; distributing and guiding the execution of the preliminary spatiotemporal path sequence; and optimizing the model of the planning strategy based on feedback information during execution. This invention solves the efficiency bottleneck caused by rigid paths and fixed strategies in existing technologies, achieving a systematic reduction in the total hospital stay and a personalized improvement in the patient's medical experience.

This invention discloses a temperature control method and system for water-cooled radiofrequency ablation, belonging to the field of temperature control technology. The method includes: determining a temperature control threshold range based on individual patient characteristics; real-time temperature sampling during ablation and comparison with a model; calculating the optimal radiofrequency power using a temperature prediction and control algorithm when the temperature deviates from the threshold; adjusting the electrode output power; and determining the temperature difference after a pre-hysteresis time, otherwise employing a self-learning correction algorithm. This invention constructs a temperature prediction and control algorithm based on the target ablation temperature, accurately calculating the optimal radiofrequency output power and adjusting the output power of the ablation electrodes in a timely manner. This effectively avoids the risk of tissue damage caused by excessively high or low temperatures in the ablation area, providing patients with a safer radiofrequency ablation treatment environment.

Embodiments herein relate to patient characteristics indicative of CAR-T cell manufacturing quality. In certain aspects, parameters are collected and input by a trained algorithm to determine various attributes of a CAR T drug product. Such determinations can be used to optimize the manufacturing process of a CAR T drug product by adjusting various manufacturing parameters.

This application provides a knowledge graph-based rehabilitation diagnosis and treatment system and method for cerebrovascular diseases. It maps multimodal rehabilitation data onto a pre-defined knowledge graph, generating a feature subgraph. Starting with the core functional impairment node in the patient's feature subgraph, a multi-hop relationship expansion search is performed in the knowledge graph to obtain a set of candidate rehabilitation intervention nodes. Based on the empirical validity data of each candidate rehabilitation intervention node in a historical patient group with similar characteristics to the current patient, the topological association strength of the knowledge graph, and the feasibility constraints of the target patient's current medical scenario, the personalized fit of each candidate rehabilitation intervention node is determined. The results of ranking each candidate rehabilitation intervention node according to all personalized fits form a draft recommendation of a structured rehabilitation plan for the target patient. Using the scheme of this application, multimodal rehabilitation data and dynamic medical knowledge of patients can be integrated to conduct intelligent rehabilitation plan recommendations.

Systems and methods for analysis and review of surgical videos are disclosed. The systems and methods may include analyzing video frames of prior surgical procedures performed by a particular surgeon to ascertain a skill level of the particular surgeon; accessing a data structure containing patient characteristics associated with the prior surgical procedures; accessing a surgical schedule including prospective surgical procedures overlapping in time; obtaining patient characteristics associated with the prospective surgical procedures; analyzing the plurality of video frames to ascertain an expected amount of time for the particular surgeon to perform a particular prospective surgical procedure; determining requirements for particular prospective surgical procedure; determining whether the particular surgeon meets the requirements; and outputting an indicator that the particular surgeon meets the requirements.

A method for managing glucose levels of a patient under the supervision of a healthcare professional (HCP). The method includes obtaining patient information for the patient, the patient information comprising patient medication data, patient monitoring data, and patient characteristic data. The method also includes creating a patient risk profile based on the patient information. The method also includes determining a likelihood that the patient will experience hyperglycemic effects based on the patient risk profile. The method also includes determining an optimum treatment plan for the patient based on the patient risk profile and the likelihood that the patient will experience hyperglycemic effects. The method also includes executing the optimum treatment plan.

The present invention relates to a system, method, computer program, and computer program product for automated planning of clinical interventions for implanting heart valves in patients. The system (100) comprises an input interface (101) for inputting data and a computing unit (102). The computing unit (102) receives input data relating to patient characteristics via the input interface (101) and automatically selects surgical instruments (VT, DT) and surgical method characteristics (AS, VP, PS) based on the patient characteristics, and the surgical instruments (VT, DT) and surgical method characteristics (AS, VP, PS) are suitable for implanting heart valves in patients and are adapted to provide output data relating to at least the selected surgical instruments (VT, DT) and selected surgical method characteristics (AS, VP, PS).

This invention discloses a method and apparatus for predicting the clinical intervention effect based on a diffusion-cross counterfactual regression network, comprising: a patient feature dataset construction module, a clinical intervention effect prediction model construction module, a model optimization and training module, and a clinical intervention effect prediction model application module. By constructing a patient feature dataset and building and training a drug clinical efficacy prediction model based on a diffusion-cross counterfactual regression network, this invention effectively solves the problems of potential confounding factors, selection bias, and sample imbalance in clinical observation data. This approach significantly improves the accuracy and reliability of predicting individual patient intervention effects and can accurately distinguish between patient subgroups with and without effective interventions, thereby providing decision support for personalized medicine.

The application provides a full-process intelligent auditing method and system based on intravenous medication orders, which comprises the following steps: acquiring intravenous medication orders and multidimensional medical record data, generating order feature vectors and patient feature vectors; a rule engine performs static compliance determination to screen the first order; an unsupervised model is used to detect hidden risks to determine physiological parameters that need to be dynamically monitored; physiological parameters are collected in real time, and dynamic detection is performed through the rule engine; corresponding medication control operations are performed according to the dynamic detection results, the unsupervised model is incrementally corrected, and the prediction ability of the unsupervised model is iteratively optimized. Through the triple auditing mechanism of static auditing, dynamic auditing and hidden risk investigation, the application realizes full-process, high-precision and individualized intelligent auditing of intravenous medication orders, effectively solving the core drawbacks of the existing static auditing of intravenous medication orders, such as limited auditing range, low precision, inability to identify hidden risks, lack of dynamic adaptation and iterative optimization ability.

The present application relates to medical informatics and clinical auxiliary diagnosis technical field, especially in kind of based on similarity's chronic obstructive pulmonary disease auxiliary analysis method and system, method includes obtaining the clinical diagnosis and treatment data of COPD patient to be analyzed, clinical diagnosis and treatment data at least includes lung function index, clinical symptom score and medical history information;Clinical diagnosis and treatment data are processed to the characteristics, and the disease characteristic vector is constructed;The disease characteristic vector is calculated with the historical COPD patient characteristic library, and the similar COPD patient set is filtered;Based on the clinical outcome information of similar COPD patient set, COPD auxiliary diagnosis analysis result is generated, and is used to assist doctors to carry out disease assessment and risk prompt.The present application can comprehensively reflect the individual difference of COPD, improve the individualization level and clinical interpretability of disease assessment, and be applicable to the outpatient, hospitalization and follow-up management scene of COPD.

This invention proposes a method and system for prognostic analysis of hematological stem cell transplantation based on OCR and NLP technologies. The method includes receiving and recognizing medical reports uploaded by users, recognizing them as raw text through an OCR service, and then converting them into medical text through post-processing. Subsequently, an NLP service is invoked to extract structured patient features, which are then optimized and input into a prognostic prediction model to obtain prediction results for various prognostic indicators. Finally, the outputs of multiple models are merged to generate a visualized prediction report. This invention achieves seamless data linkage and automated flow between modules without human intervention by constructing a fully automated collaborative architecture that integrates medical data uploading, OCR recognition, structured data extraction, AI prognostic prediction, and result output. This eliminates data breakpoints and human errors, solving the technical problems of isolated functional modules and data flow breakpoints in existing technologies. Furthermore, it improves adaptability to medical scenarios.

The application discloses a kind of acquisition and analysis system of malignant tumor occurrence probability atlas, belong to the technical field of malignant tumor, including: data acquisition integration module, for collecting patient multi-source data;Data processing atlas acquisition module, for processing patient multi-source data and determining patient characteristic data, according to malignant tumor occurrence probability prediction model, patient characteristic data is identified, determine malignant tumor occurrence probability prediction result, and generate malignant tumor occurrence probability atlas;Atlas analysis visualization module, for analyzing malignant tumor occurrence probability atlas.The present application solves the problem that the existing cannot effectively obtain malignant tumor occurrence probability atlas and analyze, and cannot promote the comprehensive development of malignant tumor prevention and treatment.The present application can effectively obtain malignant tumor occurrence probability atlas and analyze, improve the early diagnosis rate of malignant tumor, and can promote the comprehensive development of malignant tumor prevention and treatment.

The application provides a distributed medical auxiliary diagnosis method with privacy protection and smooth correction, comprising the following steps: S1: constructing a connected network composed of multiple hospitals or medical institutions (hereinafter referred to as nodes), the network contains n nodes, only the basic connected condition needs to be met, that is, any node only needs to be connected with part of the nodes, and can communicate with all nodes in the network, and the node does not need to be directly connected with all nodes; S2: collecting m internal characteristic data to form a vector set; S3: establishing a logistic regression problem for the medical diagnosis problem; S4: calling the Exact diffusion distributed optimization algorithm with smooth correction; S5: calculating the value of the logistic function by using the obtained and new patient characteristic data; S6: making a medical diagnosis. Different from other distributed optimization algorithms, the nodes in the Exact diffusion algorithm do not transfer gradient information and have the smooth correction effect with deviation, which brings more strict protection to the privacy data and higher prediction accuracy.

A construction method for an individualized virtual laparoscopic intraoperative risk feedback model and a feedback method for an individualized virtual laparoscopic intraoperative risk feedback model, belonging to the field of medical robotics. The construction method for an intraoperative risk feedback model comprises: constructing a multi-dimensional risk assessment model based on individual patient characteristics that comprehensively considers organ morphology, a physical distance of a mechanical arm, clinical risk levels of organs, and historical risk accumulation, so as to quantify risk scores for different organs during virtual laparoscopic surgery; improving a conventional attention mechanism on the basis of the risk scores, and designing a risk attention module; embedding the risk attention module into an encoder and a decoder of a conventional U-Net segmentation model to obtain an RU-Net segmentation model; and using the RU-Net segmentation model to achieve organ segmentation, highlight risk areas, and dynamically generate visual feedback such as adaptive transparency adjustment and risk warnings. The present application improves the comprehensiveness and accuracy of intraoperative risk analysis, and enables dynamic real-time visual feedback, significantly improving the reliability of virtual laparoscopic surgery.

The application relates to the technical field of medical diagnosis and artificial intelligence, and discloses a man-machine knowledge interaction type disease subtype diagnosis system, which comprises the following modules: a data acquisition and preprocessing module, which is used for acquiring multi-modal data of a to-be-diagnosed object and performing preprocessing, and outputs multi-modal fusion features; a clinical knowledge base module, which is used for storing a structured clinical knowledge graph; a man-machine knowledge interaction module, which is used for providing an interactive interface to receive input of a clinical expert, and quantifying the input into mathematical constraints that can be analyzed by a model; and a double-drive diagnosis model module, which injects the structured knowledge in the clinical knowledge base module into a neural network, performs fusion of the multi-modal fusion features under the guidance of the knowledge, constructs a patient-feature heterogeneous graph, mines potential knowledge from the multi-modal data, and generates an interpretable diagnosis result of a disease subtype. The application realizes high-precision and low-training-cost diagnosis of a disease subtype, and is suitable for accurate diagnosis of subtypes of non-infectious chronic diseases.

This application relates to the field of medical simulation teaching technology, and more specifically, to an intelligent airway training model and its operational evaluation method and system. The method includes: acquiring raw sensor data of intubation operations based on sensor components in the intelligent airway training model; preprocessing the raw sensor data to obtain operational data; acquiring video data corresponding to the intubation operation using a video acquisition component; evaluating the operational data and video data based on evaluation rules to obtain an operational score; acquiring patient characteristics; acquiring supplementary characteristics based on the operational data, and fusing the supplementary characteristics with the patient characteristics to obtain fused characteristics; acquiring matched cases that match the fused characteristics based on a matching model, and obtaining optimization suggestions for the intubation operation based on the matched cases and supplementary characteristics; and generating an evaluation report based on the operational score and optimization suggestions. This method is used to improve the accuracy of evaluation and the effectiveness of medical teaching for simulated intubation operations based on an airway training model.

A medical data integration and analysis system includes a plurality of medical devices, a medical record database, a data flow processing device, a database managing device, an artificial intelligence predicting device and a vision displaying device. The medical devices respectively provide a plurality of detection data flows. The medical record database provides a record data flow for a plurality of patients. The data flow processing device is configured to assign the detection data flows and the record data flow. The database managing device for storing the detection data flows and the record data flow. The artificial intelligence predicting device provides a predicting data flow including the predicting results to the data flow processing device. The vision displaying device is for displaying the detected features, the patient features and the predicting results.