1141 results about "Patient data" patented technology

The invention belongs to the technical field of medical data processing, and discloses a medical data multi-source fusion verification and correction method and system, and the method comprises the steps: obtaining multi-source data; constructing a dynamic baseline model of the emergency department, the ICU and the common ward, responding to and verifying the abnormal condition of the patient data, and performing early warning; establishing a term library, carrying out multi-source fusion on the patient data obtained from multiple sources, and carrying out data correction; and generating a multi-source data quality evaluation table of different departments based on the whole-process data, and feeding back and updating the term library and fusing the correction weight based on the quality evaluation table. Through a real-time monitoring and intelligent early warning mechanism, quality problems in the data can be found and corrected in time, human errors are reduced, and the data management cost is greatly reduced. Through multi-source data fusion and consistency verification, the system can ensure seamless connection of data from different data sources, and cross-system and cross-platform data consistency and cooperative work are realized, so that the accuracy of medical decision and the reliability of data are improved.

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

The invention relates to the technical field of picture generation, in particular to a picture generation method based on multi-scale features. The method comprises the following steps: firstly, collecting medical images, patient data and lesion stage information under different equipment and acquisition parameters, after screening preprocessing, constructing and training a multi-scale VQ-VAE model, introducing an attention mechanism, and adopting adaptive codebook updating and multi-codebook fusion quantification; then, a hierarchical autoregression model based on a Transform decoder is constructed and trained, and lesion stage information is fused into the hierarchical autoregression model; and finally, inputting specific lesion stage information, generating a multi-scale discrete index sequence through a hierarchical autoregression model, converting the multi-scale discrete index sequence into a codeword vector through a multi-scale VQ-VAE model, and finally generating a simulated medical image. According to the scheme, the multi-scale VQ-VAE model is utilized to encode the input image into the multi-scale discrete feature representation, and meanwhile, the autoregression model is applied to the discrete hidden space of the VQ-VAE, so that the distribution of the discrete representation sequence can be effectively modeled, and the medical image with higher quality and more realistic sense can be generated.

The invention discloses a heart failure treatment aid decision generation system based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting the multi-modal data of a patient, and the multi-modal data comprises structured data, unstructured data and medical image data; the data processing module is used for carrying out standardization, quantization and vectorization processing on the multi-modal data; the knowledge graph construction module is used for constructing a knowledge graph of heart failure treatment, and the knowledge graph comprises a disease entity, a pathological feature, a treatment scheme and an association relationship thereof; the reasoning module is used for generating a personalized treatment decision based on the knowledge graph and the patient data; and the treatment scheme generation module is used for dynamically adjusting and outputting a personalized treatment scheme in combination with the real-time state data of the patient. The problems that multi-modal data are difficult to fuse and real-time disease change is difficult to dynamically adjust in heart failure diagnosis and treatment are solved, accurate diagnosis and personalized treatment are realized through knowledge graph reasoning and a dynamic correction mechanism, and the diagnosis and treatment efficiency and accuracy are remarkably improved.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.

A medical apparatus for a patient comprises an external system configured to transmit one or more transmission signals, each transmission signal comprising at least power or data; and an implantable system configured to receive the one or more transmission signals from the external system. The apparatus can be configured to treat a patient and / or record patient data. Methods of treating a patient and recording patient data are also provided.

The invention relates to the technical field of medical data analysis, and discloses an intelligent interpretation management platform for a medical examination report. The platform comprises a data acquisition module used for acquiring multi-source heterogeneous medical examination report data and generating a medical index data set; the feature extraction module is used for constructing a deep feature fusion network based on a multi-head attention mechanism and extracting cross-modal medical features to generate a joint feature vector; the correlation analysis module is used for carrying out dynamic anomaly scoring by adopting a space-time diagram convolutional network and constructing an anomaly propagation map; the dynamic knowledge graph construction module is used for integrating the medical ontology library and the diagnosis and treatment guide based on an incremental knowledge graph embedded learning algorithm; and the personalized interpretation generation module is used for generating a multi-level interpretation report through the conditional variation auto-encoder. In addition, the platform anonymizes patient data based on a differential privacy mechanism. The platform can efficiently process multi-source data, accurately mine index relations, provide personalized interpretation and guarantee data security.

A computer-based adaptive decision support system for radiotherapy, including: • a patient data acquisition module configured to acquire patient-specific clinical data, including an anatomical image, a physiological signal, and genomic data; • a preprocessing and feature extraction modality compatible with normalizing, preprocessing and extracting statistical features from the acquired data; • a status estimator used to provide a dynamic representation of the patient's treatment evolution status based on radiation, biological, dosimetric characteristics; • customized action rescaler to define a series of clinically meaningful treatment adjustments depending on the patient's current condition; • a reward function engine used to calculate therapeutic outcome scores based on the probability of tumor control, the probability of complications in normal tissue, and other predetermined factors; • a reinforcement learning agent that can learn and update treatment adaptation policies based on deep reinforcement learning techniques; • a clinical decision dashboard that provides recommended treatment adjustments and personalized interaction with the physician; and • a clinical integration interface adapted for exporting the customized treatment plan to an external treatment planning or delivery system.

The invention relates to the technical field of medical rehabilitation monitoring, in particular to a rehabilitation cloud platform system for cardiopulmonary function monitoring. According to the system, an intelligent monitoring terminal collects physiological data; the cloud computing platform adopts wavelet threshold denoising and an index moving average method to remove noise, combines dynamic time warping to achieve multi-physiological signal time sequence alignment, constructs a double-flow model, generates a health state index and a comprehensive health risk index, introduces a multi-scale branch and differential attention mechanism, and achieves the multi-scale health state index and the comprehensive health risk index. High-precision anomaly detection is realized through prediction deviation analysis, multi-target reinforcement learning is adopted to balance health improvement and risk control, and a personalized training scheme is generated; the remote medical support module supports a doctor to monitor patient data in real time and intervene in an adjustment scheme; the user interaction end provides a real-time monitoring data visualization and individuation rehabilitation plan. Through multi-modal data fusion, time sequence synchronous optimization and adaptive reinforcement learning, accurate monitoring, dynamic rehabilitation optimization and remote medical collaboration are realized.

The embodiment of the invention discloses a spinal metastatic tumor treatment scheme generation system. According to one specific embodiment, the system comprises a data processing server, an information fusion server and a scheme generation server which are in communication connection with one another, and the data processing server is used for preprocessing multi-source patient data to obtain standard multi-source patient data; the information fusion server is used for executing the following steps: performing feature code fusion on standard multi-source patient data to obtain a multi-source patient feature vector; performing feature enhancement on the multi-source patient feature vector to obtain a joint patient characterization vector; generating an initial therapeutic schedule result based on the joint patient characterization vector; and the scheme generation server is used for performing feature decision processing on the initial treatment scheme result to obtain a final treatment report. According to the embodiment, waste of computing resources can be reduced, and system response time can be shortened.

The invention discloses a coronary intervention postoperative risk prediction model construction method and system, and belongs to the technical field of medical care information and health monitoring. A coronary intervention postoperative risk prediction model construction method comprises the steps of constructing an attending doctor experience scoring system and a nursing personnel ability scoring system, and performing weighted fusion on results of a doctor experience model and a nursing personnel model to form a final additional model for output. According to the coronary intervention postoperative risk prediction model construction method and system provided by the invention, the main model is combined with the additional model, the main model is comprehensively modeled through the regression model and the time sequence model based on the static and dynamic characteristic data of the patient, and the individual health state change of the patient is captured; the additional model evaluates the influence of medical teams and relatives on the postoperative risk by quantifying the experience of doctors and the ability of caregivers, and overcomes the defect that a traditional model only depends on patient data.

An example digital therapy platform is disclosed that provides personalized recommendations. Patient data is collected from a plurality of data sources associated with a patient profile of a patient in the digital therapy platform. At least a subset of the patient data is processed to detect a patient event. In response to detecting the patient event, a personalized recommendation is generated. The personalized recommendation is associated with the patient profile and generated using at least one machine learning model. An action is invoked in the digital therapy platform based on the personalized recommendation, and the patient profile is adjusted to reflect the action. The digital therapy platform causes presentation of at least one of a first indication of the action at a first device associated with a therapist assigned to the patient profile or a second indication of the action at a second device associated with the patient.

A wireless adapter for a standalone medical device can include a socket housing at a first end of the wireless adapter along a longitudinal axis of the wireless adapter, the socket housing configured to receive and electrically connect with a wireless dongle. The adapter can further include a medical device connector at a second end of the wireless adapter opposite the first end. The medical device connector can electrically connect with a standalone medical device; receive patient data from the standalone medical device; and pass the patient data to the wireless dongle to enable the wireless dongle to wirelessly transmit the patient data to a medical network interface coupled to a patient monitoring hub.

The invention relates to the technical field of medical rehabilitation, and discloses a virtual fusion personalized upper limb rehabilitation training cloud platform and method. The platform is composed of a multi-source biological signal acquisition module, a motion function feature modeling module, a heterogeneous data fusion module, a rehabilitation knowledge graph construction module, a dynamic training planning module and the like. The method comprises the steps of collecting multi-source biological signals, carrying out hierarchical feature modeling, carrying out data fusion, constructing an atlas database, generating a personalized rehabilitation scheme and the like. In this way, individuation and precision of upper limb rehabilitation training are achieved. Patient data can be comprehensively collected, fusion rehabilitation state representation is generated, a double-layer atlas database is constructed to assist training planning, training safety is guaranteed, the rehabilitation effect is predicted, pertinence, scientificity and efficiency of rehabilitation training are effectively improved, and high-quality rehabilitation service is provided for upper limb dysfunction patients.

A system and method for generating patient care instructions based on real-time sensor and medical data. The method includes receiving time-stamped sensor data from a sensor network comprising motion, occupancy, and environmental sensors, and receiving medical data associated with a patient, including medical conditions, treatment history, medication data, and biometric data. The sensor data is enriched with room-specific information, and activity pattern data is generated in real time using a pattern recognition model. The activity pattern data includes mobility, sleep patterns, medication adherence, statistical measures, temporal patterns, and correlations with medical data. Anomalies indicating potential health risks are detected by comparing current activity patterns with baseline data. A prediction model, trained on historical patient data, assesses the patient's health and generates care instructions accordingly. The care instructions are securely delivered to patient devices, caregiver applications, or automated medication dispensing systems, enabling timely interventions and continuous patient monitoring.

The invention provides a virtual reality rehabilitation training self-adaptive regulation and control method and system based on a heart flow double-mapping model, and the method comprises the steps: constructing the heart flow double-mapping model in advance, which comprises a patient data and heart flow state input mapping model and a task parameter and heart flow state output mapping model; collecting multi-dimensional data of the patient in virtual reality rehabilitation training in real time; the current cardiac flow state is calculated through the patient data and cardiac flow state input mapping model, if the current cardiac flow state does not reach the target cardiac flow state, the optimal task parameter combination is solved through the task parameter and cardiac flow state output mapping model under the medical rule constraint condition, virtual reality training task parameters are dynamically adjusted, and the target cardiac flow state is obtained. The heart flow state of the patient reaches the preset target. According to the method, through multi-dimensional data fusion and cardiac flow double-mapping model collaborative optimization, a high cardiac flow state of a patient can be accurately induced and maintained, and the participation degree and the curative effect of rehabilitation training are remarkably improved.

The invention provides a medical data sharing method and system based on multi-party security computing, and the method comprises the steps: carrying out the credible access control of participants through zero-knowledge proof and biological feature authentication, and enabling the participants to comprise a medical institution, a medicine enterprise / research institution, a patient and a supervision institution; after credible access control is passed, when a medical institution extracts patient data, k-anonymity, differential privacy and homomorphic encryption technologies are utilized to encrypt and desensitize the extracted patient data and store the data to a block chain; receiving a calculation task request and calculation parameters published on the block chain by a medicine enterprise / research institution, realizing mixed circuit and secret sharing mixed multi-party security calculation by a calculation node based on an ABY3 framework, and verifying the compliance of a calculation result; and receiving a result verification and auditing request of the supervision mechanism, and if the calculation result is verified to be within an authorization range through the smart contract, allocating rewards to the medical mechanism, the calculation node and the block chain platform according to a preset proportion, thereby effectively improving the security and timeliness of medical data sharing.

The invention provides a health scheme recommendation method, device and equipment based on a large model and a knowledge graph. The method comprises the following steps: acquiring multi-modal data of a cardiovascular disease patient; the multi-modal data is preprocessed, and fusion of the multi-modal data is achieved through time alignment and feature alignment; features of the preprocessed multi-modal data are extracted, a multi-dimensional feature matrix is constructed, and the multi-dimensional feature matrix is identified and labeled; inputting the multi-dimensional feature matrix into a pre-constructed fusion behavior recognition model, enhancing the model performance through time sequence modeling and multi-modal fusion, realizing the recognition of psychological disorders, and obtaining a recognition result; and integrating the patient data, the recognition result and a pre-constructed psychological disorder knowledge graph, and adopting collaborative filtering, content recommendation and reinforcement learning methods to generate a personalized psychological health management scheme. Precise recognition and personalized management of the psychological disorder of the patient with the cardiovascular disease are achieved, and the psychological health level and life quality of the patient can be improved.

The invention discloses an interpretability analysis and decision sharing verification method and system for a rectal cancer prognosis model, and relates to the field of medical artificial intelligence interpretability. The method comprises the following steps: carrying out gradient weighting class activation mapping analysis on a prognosis model to generate an image thermodynamic diagram; calculating the contribution degree of the multi-modal features by using an SHAP interpreter; an integrated visual interface is constructed, and patient data, model prediction and the explanation result are presented to a doctor together; the doctor performs independent risk assessment based on the interface information; finally, decisions of doctors and the model are compared, and model auxiliary efficiency is evaluated. Through a doctor-model decision sharing verification mechanism which is explained and innovated in a multi-level mode, the transparency and clinical credibility of the complex AI prognosis model are remarkably improved, the value of time sequence data in dynamic risk assessment can be verified, and clinical landing application of the AI model is powerfully promoted.

The invention discloses a CBCT (Cone Beam Computed Tomography) high-quality CT (Computed Tomography) image synthesis method based on structure prior guidance, which comprises the following construction steps of patient data collection and arrangement, space guidance deformable attention convolution, structure prior guidance type conditional diffusion image synthesis and reasoning process and quality evaluation. The method specifically comprises the steps of technical design of a space guiding module, technical design of a deformable sampling kernel generator, technical design of an attention weight modulator, contour extraction operation, diffusion modeling operation, conditional denoising network design and target training. The method provided by the invention has the advantages of structural perception, high spatial selectivity, high modulability and the like; and the reduction precision of the model on the key anatomical region is enhanced by taking a contour map as a structure priori condition. The whole diffusion generation network realizes unification of local detail reservation and global structure stability while maintaining noise robustness, and gradually generates a high-quality pseudo CT image which is close to a real CT modal and has excellent structure consistency.

There is provided a method of performing a medical scan of a subject using a medical imaging system, the method comprising: a) initiating a medical scan session; b) performing, using the medical imaging system, a first image acquisition sequence during the medical scan session to obtain first image scan data; c) performing, using a computer system, image analysis on the first image scan data acquired during said first image acquisition sequence to identify one or more quantitative indicators of pathology and / or image quality; d) based on said identification of one or more quantitative indicators at step c), determining, using the computer system, whether any additional image acquisition sequences are required during said medical scan session; and, if so required: e) determining, using the computer system, a second image acquisition sequence based on the one or more quantitative indicators; and f) performing, using the medical imaging system, a second image acquisition sequence during the medical scan session to obtain second image scan data.

The invention relates to the technical field of medical care, in particular to an intensive care data management system based on a block chain, and the system comprises an access behavior analysis module, a data encryption module, a sensitivity monitoring module, a health abnormality monitoring module and a node monitoring module. According to the invention, intensive care data is finely managed by using the block chain technology, the medical process is optimized, the access behavior and response time difference is collected and analyzed, the operation efficiency of medical personnel and medical equipment is effectively monitored, the operation delay and the process bottleneck are identified in real time, the response speed is optimized, and the quality of medical services is improved. Through encryption processing and data classification, the confidentiality of information is guaranteed, the data security is also improved, the data leakage risk is reduced, and due to accurate monitoring and real-time abnormal monitoring of sensitivity, key medical data is particularly protected, and the service quality of patient data management is enhanced.

A method of processing medical implant device data for preoperative patients, the method including storing, by a computer memory, a first list of medical implant devices. The method assigning, by a processor in communication with the computer memory, one or more device-variables to each of the plurality of medical implant devices, and storing, by the computer memory, one or more queries assigned to the one or more device-variables. Further, connecting a client computer with the processor and transmitting to the client computer the one or more queries. Receiving, by the processor, one or more data inputs from the client computer in response to the one or more queries. Generating, by the processor, a second list of medical implant devices as a function of the one or more data inputs received, and transmitting the second list of medical implant devices to the client computer.

The invention belongs to the technical field of application of an artificial intelligence technology to medical rehabilitation instruments, and relates to a method for designing an orthosis based on an AI artificial intelligence technology, and the method comprises the steps: S1, data collection and preprocessing: converting patient data into structured data; s2, feature extraction: extracting geometric and biomechanical features related to orthosis design, and constructing a feature set; s3, processing the features by using a deep learning model, and predicting personalized parameters; s4, designing and optimizing an orthopedic module, and optimizing pressure distribution; s5, a three-dimensional orthopedic CAD system is developed and deployed, and patient information and orthopedic device design are managed; and S6, evaluation and optimization: fusing clinical data, and optimizing the system according to an evaluation result. According to the method, the AI technology is utilized to improve the design efficiency and the adaptation accuracy, the personalized requirements are met, the rehabilitation effect is optimized, digital management of data and iteration of an AI model are achieved, a treatment basis is provided for rehabilitation medical employees such as a brace teacher and a rehabilitation therapist, and the clinical effectiveness of a rehabilitation treatment scheme is improved.

The invention relates to the technical field of patient body data analysis, in particular to a real-time monitoring method and system for a patient after an interventional operation. According to the method, multi-dimensional physiological data of multiple patients at each time point are continuously collected, and monitoring time periods are divided according to fixed duration windows. And analyzing the physiological anomaly degree of each dimension of the reference patient in the latest time period after the operation and the physiological disorder degree between adjacent sampling moments, and generating a physiological anomaly value of each dimension in combination with the physiological data change trend characteristics. And the to-be-analyzed dimension is screened accordingly. And then calculating the physiological data difference of the to-be-analyzed dimensions of the reference patient and other patients before the operation to obtain the physiological similarity. And constructing a personalized complication risk assessment model by combining the dimension abnormal value of the reference patient, the cross-patient similarity and other patient data distribution characteristics, and performing early warning. According to the method, the accurate complication risk assessment model is established, so that related personnel can accurately give an early warning for the condition of a patient.

The invention discloses a machine learning-based intra-operative bleeding risk prediction method and device for spinal surgery. The machine learning-based intraoperative bleeding risk prediction method for spinal surgery comprises the following steps: acquiring information of a patient to be predicted; obtaining a trained hemorrhage risk prediction model; and inputting the information of the patient to be predicted into the trained massive hemorrhage risk prediction model so as to obtain a prediction result. According to the method, the high-precision prediction model is trained through large-scale patient data (including basic information, operation parameters, blood indexes and the like), so that the accuracy and the stability of intraoperative SBL risk prediction are improved. And a Cell Saver use suggestion based on a risk threshold is provided, and blood resource allocation is optimized. Clinical decision-making efficiency is improved through an automatic tool, blood transfusion related complications (such as infection and immune response) are reduced, and patient prognosis is improved.

The invention discloses a medical record quality control method based on medical record segmentation and scenarized quality control rule matching, electronic equipment and a storage medium, the method comprises the following steps: S1, collecting patient data associated with a medical record to be subjected to quality control, and carrying out standardized preprocessing to obtain a medical record document which can be processed by a large model; s2, segmenting the medical record document according to key semantics to obtain a plurality of segmented key semantic units; s3, performing quality control rule matching on each key semantic unit to form second structured data including key semantics, unit medical record documents and quality control rules; and S4, inputting the second structured data into the large model, instructing to perform quality control, and outputting a quality control result. For medical record quality control, the accuracy of quality control rule matching is ensured through key semantic segmentation and scene quality control rule matching, quality control is effectively emphasized in combination with a large model technology, a quality control result and a processing suggestion are obtained, and powerful support is provided for subsequent medical record modification.

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.

The present disclosure provides a method for performing a treatment plan, wherein the method comprises: receiving first patient data, wherein the first patient data includes at least a first patient identifier associated with the first patient and a first treatment plan; receiving second patient data, wherein the second patient data includes a second patient identifier associated with the second patient and a second treatment plan; receiving first measurement data associated with a first performance level of the first treatment plan by the first patient; receiving second measurement data associated with a second performance level of the second treatment plan by the second patient; determining differential data, wherein the determining is based on a contrast of the first or the second measurement data or first or second patient data; and generating, based on the differential data, an instruction to modify an operating state of the treatment apparatus.

Systems and methods for de-identifying patient data are disclosed herein. In some embodiments, a method for de-identifying patient data includes receiving a patient record including one or more identifiers. The method can include generating a first de-identified record from the patient record using a first de-identification process. The first de-identification process can be configured to produce a first re-identification risk score. The method can further include receiving a request from a data recipient to access the first de-identified record. The method can also include generating a second de-identified record from the first de-identified record by using a second de-identification process. The second de-identification process can be configured to produce a second re-identification risk score lower than the first re-identification risk score.