1503 results about "Patient data" patented technology

Systems and methods for improving reimbursement rates from payors for services or procedures provided by a medical care provider are disclosed herein. In some examples, reimbursement can be improved by improving the accuracy of medical coding of services and diagnosis codes through the use of machine learning techniques. In some examples, the system can suggest codes more likely to get reimbursed by a payor through analysis of patient data and / or insurance information. In some examples, the system can determine a likelihood of reimbursement through analysis of patient data and / or insurance information.

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.

Systems and methods for generating a response summarizing patient data are provided. One or more prompts, comprising 1) patient data retrieved from one or more patient databases and 2) instructions, are received. A response summarizing the patient data is generated based on the instruction using a large language model. The response is output.

A method for diagnosing diseases in human patients can include providing a set of global X-ray diffraction (XRD) data to a global data center (GDC) and processing the set of global XRD data and categorizing it into data clusters, where each data cluster corresponds to a diagnostic indicator for assessment of a physiological or pathological condition. The method can further include communicating local XRD data and local patient data from a local autonomous cell (LAC) to the GDC, where the local XRD data includes information about a tissue sample measured using a tissue diffractometer of the LAC, and wherein the tissue sample includes skin. The method can further include processing the local XRD data, comparing it with the data clusters to determine a local diagnostic indicator for the local patient, and communicating the local diagnostic indicator to the LAC.

Systems and methods for designing and implementing patient-specific surgical procedures and / or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated.

The invention relates to a multi-agent medical diagnosis and treatment system which comprises a plurality of agents. The plurality of agents comprise: a task planning agent analyzes input content and generates a corresponding task process, and the task planning agent sequentially issues task instructions to a data management agent, a tool use agent and a medical decision agent according to the task process; the task instructions comprise a data task instruction, a tool task instruction and a decision task instruction; the data management agent extracts patient data from the database and sends the patient data to the task planning agent according to a data task instruction issued by the task planning agent; the tool using agent performs feature extraction processing on the patient data according to the patient data and the tool task instruction issued by the task planning agent so as to obtain key features and send the key features to the task planning agent; and the medical decision-making agent carries out diagnosis and treatment reasoning according to the key features and the decision-making task instruction issued by the task planning agent, and a diagnosis report is obtained and displayed.

The invention relates to an intensive care cross-institution collaborative data processing method, which comprises the following steps of: defining a hierarchical data model based on the intensive care field, and modeling various types of information including vital signs, clinical diagnosis and laboratory detection results of a patient according to a time sequence; a self-adaptive standardized interface is designed, and data formats from different hospitals or devices are identified and converted; real-time dynamic tags are introduced for patient data through a tagging technology; various health data, including vital signs, medical history and treatment feedback, of a critical patient are converted into a multi-dimensional health track, and sequential analysis is used for conducting continuous modeling on the condition of the patient; a deep learning and reinforcement learning method is combined to establish a disease evolution deduction engine; the disease development is predicted according to historical data, and the influence of different intervention measures on the disease is simulated to intelligently recommend a treatment strategy; and according to the dynamic deduction result, generating a plurality of treatment schemes, and dynamically adjusting the intervention strategy according to the real-time response of the patient.

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

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.

A system and method for providing therapy to a patient can include a monitoring unit and a therapy unit. The monitoring unit can have a user interface and one or more sensors to measure patient data. The therapy unit can have a stimulator for generating electrical stimulation and a microcontroller for controlling the generation of the electrical stimulation based on the measured patient data.

The invention, which relates to the technical field of nursing management, discloses a patient information safety management system for liver, gall and pancreatic internal medicine nursing, comprising a nursing information management center. The nursing information management center is in communication connection with a patient information collection module, an information classification processing module, a health trend analysis module, a risk assessment analysis module, a risk early warning planning module and an information synchronous updating module. According to the patient information safety management system for nursing in the department of hepatobiliary and pancreatic, through implementation of the patient information safety management system, medical staff can access latest and most accurate patient data in real time, and a risk assessment analysis module comprehensively analyzes the medical history, the current illness state and the treatment progress of a patient and calculates a risk assessment coefficient; in addition, by updating the medical information of the patient in real time, making and executing a personalized nursing plan, the change of the illness state of the patient is quickly responded, and the nursing and treatment scheme is adjusted in time.

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.

A method is provided for optimizing a dental treatment plan. In some cases, the method can include receiving patient data comprising one or more progress indicators associated with an dental treatment plan, processing the patient data to determine a level of progression associated with the dental treatment plan based on the one or more progress indicators, modifying the dental treatment plan in response to the determined level of progression, and generating a notification of the modified dental treatment plan. In some cases, modifying the dental treatment plan in response to the determined level of progression includes advancing a patient associated with the patient data to a subsequent stage of the dental treatment plan or retaining the patient within a current stage of the dental treatment plan.

A system and method for providing therapy to a patient can include a monitoring unit and a therapy unit. The monitoring unit can have a user interface and one or more sensors to measure patient data. The therapy unit can have a stimulator for generating electrical stimulation and a microcontroller for controlling the generation of the electrical stimulation based on the measured patient data.

The invention provides an X-ray Flash radiotherapy system and method with intensity modulated by a compensator, and belongs to the technical field of radiotherapy equipment control. A patient data management module is used for acquiring basic information, patient image data and patient radiotherapy plan data of a patient; the region-of-interest determination module is used for determining a radiotherapy region-of-interest; and the Flash plan design module with the intensity modulated by the compensator can be used for calculating the Flash effective dose of a patient and obtaining an optimal X-ray Flash plan with the intensity modulated by the compensator according to clinical requirements. According to the method, the effective doses of different Flash effect models are calculated, and the X-ray Flash plan with the intensity modulated by the compensator is optimized in a step-by-step or synchronous mode; technical conditions are provided for realizing X-ray Flash intensity modulated radiation therapy and clinical application thereof; the blank of the X-ray Flash radiotherapy technology is filled, and the method has important significance for converting and applying the Flash radiotherapy technology.

The invention discloses an intelligent infusion monitoring management system, and relates to the technical field of medical equipment, and the system comprises the steps: collecting patient data, synchronizing the patient data to a central monitoring system through a block chain technology, and generating a personalized infusion scheme; based on a personalized infusion scheme, a medical worker uses a mobile device to carry out identity verification on a patient and obtain medicine information, and a voice assistant function is integrated; the monitoring module is responsible for monitoring vital signs of a patient and the temperature of a ward by using wearable equipment and a temperature sensor, and adjusting a personalized infusion scheme and liquid temperature according to the vital signs and the temperature of the ward; the risk value prediction module is responsible for predicting risk values of blockage and leakage according to the adjusted personalized infusion scheme and the liquid temperature and setting a graded alarm mechanism; patients and family members receive the infusion progress through the intelligent infusion monitor. According to the invention, the block chain technology and the advanced artificial intelligence algorithm are integrated, so that the health data and medicine information of the patient can be efficiently managed and analyzed.

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.

The present disclosure provides systems and methods for providing neurostimulation therapy according to patient features. The patient features may be analyzed to develop a patient model between physiological and / or patient reported features and optimal settings for a neurostimulation therapy using machine learning operations. The model is trained using data corresponding to time intervals in which electrical pulses are applied to the patient, including video kinematic data, patient reported pain levels, implanted or worn sensor patient data, and stimulation parameters. The model is used to control ongoing neurostimulation therapy for the patient.

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.

A patient data charting device configured to automatically capture electronic patient care record (ePCR) data from a caregiver is provided. The device includes a memory storing an ePCR including a plurality of data fields, an output device, a microphone configured to acquire speech, and a processor. The processor is configured to convert the speech to text, identify a first value of a data field of the plurality of data fields based on the text, populate the first data field with the first value, generate a prompt that requests a second value of a second data field of the plurality of data fields based on the first data field, and present the prompt to the caregiver via the output device.

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 myocardial infarction patient risk assessment model construction method and system, and particularly relates to the field of cardiovascular disease prediction, and the method comprises the following steps: I, collecting modal data of a hospital information system, an image filing system and wearable equipment, and integrating the modal data; according to the method, the accuracy of myocardial infarction risk prediction can be optimized, risk assessment not only depends on statistical characteristics, but also fuses pathophysiological mechanisms, the clinical interpretability of prediction is improved, the limitation that a traditional data-driven method is only based on correlation modeling is avoided, the optimal treatment scheme can be dynamically adjusted, data-driven personalized medical treatment is achieved, and the risk assessment efficiency is improved. The scientificity of intervention decision making is improved; the method can quickly adapt to new patient data, improve the individualized prediction ability, improve the data utilization rate, enhance the generalization ability of the model for different patient groups, avoid training the whole model from zero, greatly reduce the consumption of computing resources, make up for the problem of insufficient data of a single patient, and improve the prediction ability of rare cases.

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.

The invention provides an electronic medical record management method and system based on AI calculation. The method comprises the following steps: designing a general template of an electronic medical record; preprocessing the patient data; dividing the preprocessed patient data into patient numerical data and patient text data; marking key content items in all the universal content items in the universal template; performing abnormal data detection on the patient numerical data and the patient text data based on the target universal template; extracting a patient data entity and a content item entity from the target electronic medical record by using a named entity recognition model; constructing a medical record content graph network of the target electronic medical record in combination with the patient data entity and the content item entity; and performing abnormal content detection on the target electronic medical record based on the medical record content graph network, and if the target electronic medical record passes the abnormal content detection, storing the target electronic medical record. The method has the effect of intelligently generating the high-quality electronic medical record.

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.

The invention relates to the technical field of medical data processing, in particular to an internal medicine emergency treatment data processing method and system based on cloud computing, and the method comprises the following steps: monitoring the data of an emergency treatment patient, carrying out the dynamic interval correction of the systolic pressure, diastolic pressure and blood oxygen critical values of the patient through the analysis of the age, gender and weight, and generating an index threshold interval. According to the method, the abnormal physiological parameter detection threshold value is adjusted, the actual health condition of the patient is accurately reflected, the correlation and fluctuation trend of multiple physiological data are analyzed, the emergency degree of the emergency patient is effectively evaluated, the data transmission priority is optimized, key information is ensured to be transmitted in time, and encryption in the data transmission process is combined; according to the method, the integrity and the reliability of data are guaranteed, the storage position and the access priority are dynamically adjusted according to the access frequency and the activeness of the data, the data retrieval efficiency is improved, the utilization of storage resources is optimized, the response speed and the processing capacity are improved, and reliable real-time support is provided for medical staff.