39 results about "Patient identifier" patented technology

The invention relates to the field of data query, and particularly provides a medical big data query method and system based on dynamic desensitization, a terminal and a medium, and the method comprises the steps: collecting multi-source heterogeneous medical data, carrying out semantic normalization, constructing a unique identifier of a patient, and generating standardized intermediate plaintext data; constructing an adaptive isomorphic index structure based on the data, constructing a semantic hash B + tree index for structured data, constructing a feature vector index for unstructured data, and dynamically optimizing index hierarchy and parameters according to access frequency; and after a query request is received, the permission context is analyzed, intermediate plaintext data is positioned and read by using the index, and a differential privacy protection strategy is dynamically applied according to the access permission. According to the method, the problem of balance between privacy protection and data effectiveness as well as between retrieval efficiency and storage cost in medical data processing is effectively solved, and data availability and access performance are improved while safety compliance is guaranteed.

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.

The invention provides a hemodialysis room whole-process information management system based on active learning, and relates to the technical field of data processing, and the system comprises a feature construction module which is used for carrying out the structural processing of original dialysis information data, and extracting a plurality of feature factors related to the current physical state of a patient; the risk identification module is used for constructing a training sample group, performing supervised training by introducing a label, and generating a risk judgment model; the early warning generation module is used for generating a risk probability result and generating early warning information associated with the current patient identifier when the risk probability result reaches a preset risk threshold value; the scheduling module is used for dynamically adjusting the treatment scheduling sequence of the current patient according to the early warning information and recording the actual intervention effect after the treatment scheduling sequence is adjusted; the feedback learning module is used for endowing each feature factor with a weight value according to an actual intervention feedback result and feeding back the weight value to the risk identification module; according to the invention, autonomy and accuracy of information management are improved.

The invention discloses a high-precision real-time urodynamic monitoring method and system, and belongs to the technical field of medical monitoring. A urine flow sensor is arranged at the downstream of a catheterization assembly to collect urine flow and generate an electric signal; the electric signals are transmitted to a micro-control unit for digital processing, and the instantaneous flow velocity of urine, the accumulated urine volume per hour and the total urine volume of 24 hours are calculated in real time through an integral flow velocity algorithm; a calculation result is output to a local display screen, and real-time data viewing and historical curve graph and statistical report display are supported; meanwhile, the urine volume data are packaged according to timestamps and patient identifications and uploaded to a hospital nursing information system through a standardized interface protocol; if it is detected that the urine volume is abnormally lower than a set threshold value or is completely in a urine-free state, the system automatically triggers an alarm and pushes the alarm to the nursing terminal; by means of the method, automation, informatization and intelligentization of urine volume monitoring can be achieved, and the method is suitable for continuous nursing requirements of ICU and other critical areas.

The embodiment of the invention discloses an auxiliary decision support information generation system and an auxiliary decision support information generation method. A specific embodiment of the system comprises a server, each heterogeneous data source and at least one terminal, the server is configured to execute the following processing: acquiring a heart and kidney metabolic syndrome patient information table which comprises each patient identifier; creating and updating a heart and kidney metabolic syndrome knowledge graph; for each patient identifier, generating a standardized diagnosis and treatment event record information set; generating an initial diagnosis and treatment event space-time diagram; determining each generated initial diagnosis and treatment event time-space diagram as an initial diagnosis and treatment event time-space diagram set, and storing the initial diagnosis and treatment event time-space diagram set; generating a diagnosis and treatment event space-time diagram corresponding to the treatment data; generating auxiliary decision support information; and sending the auxiliary decision support information to the terminal. According to the embodiment, scheduling resources of the medicine taking robot are reduced.

The application relates to the technical field of electrocardiosignal processing, and discloses a monitoring electrocardio data analysis method based on online monitoring, wherein the method comprises the following steps: performing multistage filtering pretreatment on an original electrocardio signal; detecting a QRS complex and extracting an RR interval sequence; extracting time domain statistical features, frequency domain features and wavelet coefficients to form an electrocardio feature vector; inputting the electrocardio feature vector and an electrocardio signal segment into an atrial fibrillation detection model containing a signal feature learning branch and a sequence feature learning branch, and outputting an atrial fibrillation detection result; and generating an alarm information based on the detection result and pushing the alarm information to a medical terminal. Through atrial fibrillation load statistics and stroke risk grade evaluation, the single abnormality detection result is expanded into a continuous risk quantization index, a coherent analysis link from abnormality identification to risk evaluation is provided for clinical doctors, complete data storage is associated with a patient identifier and a time stamp, all analysis processes can be traced back, and the needs of clinical audit and department review are met.

The invention discloses a time axis key node automatic acquisition system for chest pain diagnosis and treatment, and relates to the technical field of information and communication of medical data, and the time axis key node automatic acquisition system comprises the following steps: pre-storing an interval template containing a starting or ending anchor point rule; acquiring an event stream which comes from one or more medical information systems and carries a chest pain patient identifier and a source system timestamp in real time; creating and maintaining an active interval instance in a memory for the chest pain patient; inferring a candidate end event; and identifying the time reference drift of the source system, and adding a credibility mark to the time-consuming data. Automatic and real-time monitoring of time consumption in the diagnosis and treatment process is achieved, the system supports starting and ending anchor point matching, automatically recognizes time reference drift and adds credibility marks, and the reliability of time consumption data in a real and complex environment is guaranteed on the premise of not depending on strict time synchronization of a hospital-wide system.

The embodiment of the invention provides an operation scheduling method and a medical data processing system, which are used for improving the efficiency and convenience of arranging an operation for each operating room. The method provided by the embodiment of the invention comprises the following steps: in response to a selection operation on any patient operation information in a patient operation information display component and an interaction operation between the selected patient operation information and an operating room information display component, generating a first interaction instruction, the patient operation information display assembly and the operating room information display assembly are displayed on a preset interactive interface, and the patient operation information display assembly is used for displaying a surgeon, a patient department and at least one of a patient identifier and an operation identifier. The operating room information display assembly is used for displaying the number of operations in an operating room, the total duration of the operations and visual operation progress information; according to the first interaction instruction, the selected patient operation information is switched from the patient operation information display assembly to the operating room information display assembly.

This disclosure presents an auxiliary decision support information generation system and method. One specific implementation of the system includes: a server, various heterogeneous data sources, and at least one terminal; the server is configured to perform the following processes: acquiring a patient information table for cardiorenal metabolic syndrome, wherein the patient information table includes individual patient identifiers; creating and updating a knowledge graph for cardiorenal metabolic syndrome; generating a standardized set of medical event records for each patient identifier; generating an initial spatiotemporal graph of medical events; determining the generated initial spatiotemporal graphs of medical events as an initial spatiotemporal graph set, and storing the initial spatiotemporal graph set; generating a spatiotemporal graph of medical events corresponding to the patient's medical data; generating auxiliary decision support information; and sending the auxiliary decision support information to the terminal. This implementation reduces the scheduling resources of the medication dispensing robot.

This disclosure relates to a method, apparatus, storage medium, and electronic device for processing medical events, belonging to the field of medical big data processing technology. The method includes: acquiring raw medical data corresponding to the current patient based on the current patient's patient identifier; performing structured extraction on the raw medical data to obtain current event entities included in the raw medical data; determining the current event axis of the current event entity based on its event category; mapping the current event entity onto the current event axis based on its current time node to obtain a target event axis; and displaying the target event axis so that medical personnel can analyze the current patient's treatment process based on the target event axis. This disclosure enables the visualization of medical events included in raw medical information.

The invention discloses an ophthalmology intelligent auxiliary diagnosis method and system based on heterogeneous data collaboration, and the method comprises the steps: synchronously obtaining an ophthalmology multi-mode heterogeneous data set of a patient, including an OCT image, a fundus photograph and a visual field inspection report; the OCT image is subjected to retina layered structure segmentation, and an RNFL thickness distribution diagram is extracted; performing optic disc region positioning and blood vessel tree segmentation on the fundus photography to generate a CDR and a blood vessel curvature quantized value; mode deviation probability graph coding is carried out on the visual field inspection report, and a quadrant sensitivity missing matrix is output; an ophthalmic multimodal heterogeneous data set is associated through a patient unique identifier, and a RNFL thickness distribution map of OCT is mapped to an optic disc coordinate system of fundus photography by using feature-driven non-rigid registration. According to the invention, an ophthalmology multi-modal data cooperation barrier is broken through, an intelligent decision-making system with structure-function mutual verification is constructed, and the diagnosis reliability and clinical interpretability are significantly improved.

The invention discloses an infusion information management system, which relates to the technical field of medical informatization equipment and comprises a task information access module, a label data generation module, a dual-channel acquisition module, a context association processing module, a synchronous locking control module, a transaction state judgment module and an execution record collection module. According to the method, a cross time locking domain and a transaction serialization mechanism are introduced into the system, so that unified constraint of multi-terminal data in a time domain and a logic domain is realized; meanwhile, scanning, position and equipment identity information are subjected to multi-dimensional binding through a context association structure, atomic-scale correspondence of an infusion label and a patient identifier at the infusion execution moment is achieved, and false matching caused by time sequence drift or concurrent operation is avoided; according to the invention, the safety of infusion behaviors and the consistency of system-level information can be obviously improved.

The invention discloses a pathological image multi-task analysis method and system integrating medical priori knowledge, and the method comprises the steps: obtaining pathological image blocks of a plurality of patients, the pathological image blocks being associated with corresponding space coordinates, pathological labels and patient identifiers; respectively extracting visual features of each pathological image block; based on the space coordinates, the pathological labels and the visual features, constructing a graph structure fused with medical priori knowledge; taking the visual features as initial embedding of graph nodes of the graph structure, and inputting the visual features into a graph neural network for message passing so as to obtain updated graph node feature representation; and based on the updated graph node feature representation, executing multi-task learning so as to realize image block-level pathological classification and patient-level survival risk prediction. By constructing a medical priori knowledge graph structure fusing space coordinates, pathological labels and visual features and executing end-to-end multi-task learning, collaborative optimization of image block-level pathological classification and patient-level survival risk prediction is realized.

The invention discloses a general medical department doctor-patient information interaction method and system, and particularly relates to the technical field of information interaction.The method comprises the steps that multi-mode health information such as texts, voice and images is collected through a patient end, and an original data packet is formed through packaging according to a timestamp and a patient identifier; according to the method, semantic analysis and entity extraction are carried out on texts and voices, symptoms, parts and duration are mapped to structured health information vectors, association is carried out in combination with image features, and multi-modal unified coding representation is formed; calculating a symptom fluctuation index and a behavior consistency index, inputting the indexes into a predefined model to generate a comprehensive health judgment coefficient for intelligently judging key concern contents, generating a doctor end abstract report and a diagnosis and treatment reference prompt based on the information, and synchronously updating feedback to a patient end to realize dynamic optimization of doctor-patient information interaction; the problems that traditional doctor-patient information is scattered, and key health information is difficult to obtain quickly are effectively solved.

Described is a system comprising an input module receiving a first data set indicative of at least one patient condition for each of a plurality of patients obtained during a predetermined time period, a comparison module comparing each of the at least one patient condition to at least one filter criteria, a filter module selecting a patient to include in a second data set if the at least one patient condition of the patient satisfies the at least one filter criteria, a report module generating a report based on the second data set, wherein the report includes at least one patient identifier for each patient in the second data set and at least one descriptor of the at least one patient condition for each patient in the second data set, and a classification module storing at least one classification value for the at least one patient condition.

A system for outputting a visual representation of a brain of a patient is configured to receive sensor data representing a behavior of a region of the brain of the patient. The system retrieves mapping data that maps a prediction value to the region. The prediction value is indicative of an effect on a behavior of the patient responsive to a treatment of the region, the mapping data being indexed to a patient identifier. The system receives, responsive to an application of a stimulation to the region, sensor data representing behavior of the region. The system executes a model that updates, based on the sensor data, the prediction value for the region. The system updates, responsive to executing the model, the mapping data by including the updated prediction value in the mapping data. The system outputs a visual representation of the updated mapping data comprising the updated prediction value.

A medical device data back-association system, apparatuses, and methods are disclosed. In an example embodiment, an electronic medical record memory receives, while an infusion pump is administering a medication to a patient, an identifier message including at least two of a device identifier of the infusion pump, a patient identifier of the patient, and a medication order identifier of a medication being administer by the infusion pump. The memory creates an association between an electronic medical record (“EMR”) of the patient and the infusion pump using the identifier message. The memory next receives, after the association between the EMR of the patient and the infusion pump, infusion pump data from the infusion pump. The infusion pump data includes the device identifier. The memory then stores the infusion pump data to the EMR of the patient based on the created association between the EMR of the patient and the infusion pump.

This disclosure relates to a method for securely storing and retrieving medical data. The method includes: - obtaining medical data in a secure environment, the medical data including patient attribute data and patient identifier data, wherein the patient identifier data indicates at least one patient corresponding to the patient attribute data; - generating de-identified medical data in the secure environment by replacing the patient identifier data in the medical data with non-patient identifier coded identifiers; - generating a re-identification database in the secure environment indicating the correspondence between the non-patient identifier coded identifiers and the de-identified medical data; - generating an encrypted re-identification database by applying at least one symmetric and / or asymmetric encryption method to the re-identification database in the secure environment; - storing the encrypted re-identification database and the de-identified medical data on cloud storage outside the secure environment.

An apparatus for the anonymization of user data is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory contains instructions configuring the processor to receive a plurality of user data. The memory contains instructions configuring the processor to identify a plurality of patient identifiers within the plurality of user data. The memory contains instructions configuring the processor to generate contextual data associated with each patient identifier of the plurality of patient identifier. The memory contains instructions configuring the processor to identify one or more false positive patient identifiers within the plurality of patient identifiers as a function of the contextual data.

This invention discloses a method for tamper-proof verification of personal radiation dose logs from CT scans, comprising: real-time acquisition of radiation dose data and related operation information during CT scans to generate a structured raw dose log; generating an encrypted log string through asymmetric encryption signature and SHA-256 / SM3 hash operation, combining patient identifier, timestamp, and device number to generate a unique verification identifier and adding a blockchain calibration timestamp; synchronizing relevant data to a consortium blockchain node cluster and a third-party trusted server for distributed redundant storage; supporting verification triggered by multiple entities including patients, hospitals, and regulatory agencies, outputting results through encrypted string comparison, field-level consistency verification, and multi-dimensional verification of format / field / logic; accurately tracing the source of tampering based on stored data, and automatically sending warning information to relevant parties when warning conditions are met. This invention achieves transparent verification, efficient verification, and reliable traceability of dose logs, improving data credibility, reducing medical disputes, and ensuring the effectiveness of medical quality supervision.

The invention discloses an image output management method for a medical image, which relates to the technical field of medical information management, and comprises the following steps: recording an output context parameter based on an image output request, generating an output event record including an operator identifier, a department identifier, a patient identifier, an examination number, a DICOM image identifier, an output type, an output target address, a workstation identifier and an output timestamp, and writing a global unique number generated based on the timestamp into the output event record; comparing a local cache index according to the global unique number to screen unregistered records, generating deduplicated output event records, and sequentially writing the deduplicated output event records into a cache log file; generating a serial number word string according to the global unique serial number recorded by the output event in the cache log file, and executing SHA-256 operation based on the serial number word string to generate batch Hash abstract data; according to the invention, synchronous guarantee of medical image output behaviors in three aspects of context binding, content consistency and visual auditing traceability is realized.

Techniques monitoring and preventing a patient conditions include a sensing module, a storage module, an analysis module and a visualization module. The sensing module receives, from a sensor, samples that indicate measurements suitable for a condition of a patient and storing sample data in a local buffer. The storage module is configured to receive a sample data packet indicating sample type, patient identifier, device identifier, timestamp, and sample value, and to store them into a sample data structure. The analysis module is configured to determine a metric based on the sample value, and to store into that into a metric data structure metric type, patient identifier, device identifier, timestamp, and metric value. The visualization module is configured to present the sample or the metric on a display device configured to be viewed by a caregiver for the patient.

The application relates to a computer-based hospital bed dynamic allocation method, which receives a bed event and a patient event; maintains a bed record containing a bed identifier, a bed state, an associated patient identifier and a state version number; in response to a bed allocation request, determines a target bed from candidate beds in an allocable state, updates the state of the target bed to reserved and starts a reservation timer; in the reserved state, when a receiving department confirmation receiving event and a patient admission receiving completion event are received, the bed state is updated to confirmed; in the confirmed state, when a patient transfer start event is received, the bed state is updated to locked and re-allocation is prohibited; if a cancellation event is received or the reservation timer expires, the bed state is restored to allocable and the associated patient identifier is cleared; if a cancellation event is received before the patient transfer start event is received, the bed state is restored to allocable and the associated patient identifier is cleared; and finally, each state change is recorded.

An apparatus for the anonymization of user data is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory contains instructions configuring the processor to receive a plurality of user data. The memory contains instructions configuring the processor to identify a plurality of patient identifiers within the plurality of user data. The memory contains instructions configuring the processor to generate contextual data associated with each patient identifier of the plurality of patient identifier. The memory contains instructions configuring the processor to identify one or more false positive patient identifiers within the plurality of patient identifiers as a function of the contextual data.

Methods and systems are provided that are related to electronic health records (EHRs). A first EHR system can receive a request to report information to a second EHR system. Responsive to the request, the first EHR system can generate an EHR report that includes one or more EHR identifiers. The one or more EHR identifiers can include an EHR-user identifier, an EHR-patient identifier, and a behavior identifier. The first EHR system can send the EHR report to the second EHR system. The second EHR system can: access information based on the EHR-user identifier and / or the EHR-patient identifier, generate an output based on the accessed information, and provide the output.

The invention discloses a high-risk anesthesia warning system and method based on a distributed network, and belongs to the field of medical information security. The system comprises a central database server which stores a high-risk patient list including a malignant hyperpyrexia history, a difficult airway history, a serious adverse drug reaction history and an abuse drug history based on a non-relational database; the security authentication gateway is used for realizing security access based on an OAuth 2.0 and a TLS protocol; the data access interface is used for providing RESTful API (Application Program Interface) to receive the patient identifier subjected to hash processing; the data processing and comparing module is used for executing low-latency query; and the warning information generating and pushing module is used for pushing the warning information in the JSON format to the target terminal through the message queue. Through a series of technical means, information islands among medical institutions are effectively broken, safe, real-time and efficient data interaction and warning are achieved, serious anesthesia complications can be remarkably avoided from the technical level, and medical safety in the perioperative period is improved.

The invention provides a CBCT registration data processing system and method in radiotherapy, and relates to the field of medical image processing. The system comprises an image acquisition module which is used for completing image registration processing in response to a CBCT image and a positioning CT image, intercepting a registration result interface image containing registration parameters, and storing the registration result interface image to a specified storage path; the text extraction module is used for extracting text information in the registration result interface image by utilizing an OCR (Optical Character Recognition) algorithm so as to obtain registration result data comprising a registration mode identifier, an error parameter and a patient identifier; the data storage module is used for associatively storing the registration result data and the corresponding registration mode identifier to a preset database; and the data analysis module is used for carrying out statistical analysis on the registration result data in the preset database according to a preset demand so as to obtain a corresponding positioning error statistical report. According to the scheme, CBCT registration data in radiotherapy can be rapidly and effectively collected and analyzed, and reference data are provided for quality control of clinical tumor radiotherapy.

A system for outputting a visual representation of a brain of a patient is configured to receive sensor data representing a behavior of a region of the brain of the patient. The system retrieves mapping data that maps a prediction value to the region. The prediction value is indicative of an effect on a behavior of the patient responsive to a treatment of the region, the mapping data being indexed to a patient identifier. The system receives, responsive to an application of a stimulation to the region, sensor data representing behavior of the region. The system executes a model that updates, based on the sensor data, the prediction value for the region. The system updates, responsive to executing the model, the mapping data by including the updated prediction value in the mapping data. The system outputs a visual representation of the updated mapping data comprising the updated prediction value.

The invention discloses a medical technical examination queue dynamic scheduling method and system oriented to a creative environment, and relates to the technical field of medical management, and the method comprises the steps: responding to the registration operation of a patient, and creating an examination instance of the patient; the examination instance comprises a patient identifier, a waiting state, waiting duration and a to-be-examined item; the waiting state comprises scheduling participation and scheduling elimination; in response to the fact that the state of the first medical technology resource is updated to be allocatable, screening a candidate instance set, matched with the first medical technology resource, of the to-be-detected item from the check instances in which scheduling participates, counting the number of allocatable resources, matched with the first medical technology resource, of the to-be-detected item, and sorting the check instances of the candidate instance set according to a preset sorting rule, and according to a sorting result, taking the examination examples corresponding to the allocatable resource number, allocating the examination examples to the corresponding medical technology resources, updating the waiting state of the examination examples allocated with the medical technology resources into scheduling exclusion, and dynamically adjusting the scheduling task in time in response to the change of the patient state and the medical technology resource state.