68 results about "Patient risk" patented technology

The invention provides an hemorrhagic brain injury intracranial pressure prediction and early warning system based on a CT image, and the system comprises a data collection module which is used for collecting the original data of a target data source of a target patient; the hybrid model comprises a 3DCNN model used for extracting hematoma spatial features of the head CT image sequence; the LSTM model is used for extracting hematoma time sequence characteristics of the physiological signals; the multi-modal fusion module is used for fusing the hematoma space features and the hematoma time sequence features and outputting hematoma fusion features; the prediction module is used for predicting an ICP sequence of the target patient in a future preset time period based on the hematoma fusion features; the self-adaptive early warning module is used for outputting a visual result based on the predicted ICP sequence in the future preset time period; and through a dynamic threshold adjustment mechanism, adjusting an early warning threshold of the target patient in combination with the case information, and triggering graded early warning in real time. Through personalized data processing and dynamic early warning, accurate, efficient and real-time patient risk management is realized.

The invention relates to the technical field of medical data processing and prediction, and discloses a chemotherapy adverse reaction prediction and intervention system based on big data. The system integrates an unstructured disease course text and structured inspection data of a patient, generates a time-series symptom event, and performs time window alignment and fusion on the time-series symptom event, a medication record and a physical sign monitoring stream to form a multi-dimensional time-series data block. The system is combined with an external medical knowledge base to construct a dynamic association network among symptoms, medicines and physiological indexes, and dynamically calculates the confidence coefficient of an adverse reaction mode according to real-time data. By using a predictive model of the timing attention mechanism, the system can output a continuous curve of patient risk over time. The system automatically matches and generates a personalized intervention instruction sequence containing specific measures and execution time windows according to key time points and modes when the risk curve exceeds a threshold value. According to the invention, dynamic and advanced early warning and accurate intervention of adverse reaction risks of chemotherapy are realized.

The invention relates to the technical field of medical care, and provides a patient risk dynamic assessment system in medical care teaching, which comprises a data acquisition module used for acquiring multi-modal image data of a postoperative gastric organ anastomosis area of a patient; the data processing module is used for preprocessing the multi-modal image data, establishing a three-dimensional model of organs around the anastomotic stoma and analyzing edge features of the anastomotic stoma; and the risk assessment module is used for constructing an anastomotic stoma state assessment model and outputting comprehensive scores of an anastomotic stoma blood perfusion condition, a tissue metabolism abnormal probability, an anastomotic stoma leakage risk, anastomotic stoma scar hyperplasia and healing trend prediction. A quantum dot fluorescence image, a Raman spectrum image, a CT image and an intraoperative high-definition white light image are enhanced through the system and evaluated, a nursing scheme is obtained in combination with opinions of medical staff, and compared with manual evaluation of the medical staff, the accuracy is higher, the feasibility of the nursing scheme is higher, and medical nursing use is facilitated.

The invention discloses an anorectal postoperative rehabilitation intelligent supervision method and system based on multiple modes, and belongs to the technical field of intelligent supervision. A temperature, humidity, air quality and other multi-mode sensor group is arranged in each anorectal postoperative rehabilitation management inpatient area, the body temperature, heart rate and wound surface images of a patient are synchronously collected, and the wound surface area and seepage color are extracted through image processing; the method comprises the following steps: by taking a time node as a unit, adding a time label to an environment and patient basic data set, constructing a multi-modal comprehensive data set in a unified format, calculating an environment comprehensive index and a physiological comprehensive index, fusing the environment comprehensive index and the physiological comprehensive index to calculate a postoperative rehabilitation risk value, setting a threshold interval to generate low, medium and high three-level risk labels, and the patient risk label set is pushed to the doctor end in real time, a dynamic supervision system is constructed, the anomaly recognition rate and the clinical response speed are improved, the complication occurrence rate is reduced, the rehabilitation period is shortened, and reliable data support is provided for nursing quality improvement and evidence-based medical research.

The invention belongs to the technical field of medical follow-up visit, and particularly discloses a multi-task collaborative prediction medical follow-up visit method and system, a terminal and a medium, and the method comprises the steps: collecting and preprocessing multi-source medical data, and generating a target data set in a unified format; respectively inputting the target data set into a classification model, a regression model and a time sequence model to obtain a patient risk level, a key monitoring index short-term prediction result and an illness state long-term trend prediction result; according to the prediction result, a personalized follow-up visit plan is generated in combination with the disease feature library and the core monitoring index weight, and the follow-up visit interval and follow-up visit items are dynamically adjusted according to the change of the prediction result in the follow-up visit execution process; when the follow-up plan is executed, a follow-up task is pushed, feedback information is received, and the feedback information is analyzed and stored in the medical information system for subsequent dynamic adjustment and model updating. Multi-source data fusion of medical follow-up visit, multi-task collaborative prediction and dynamic optimization of follow-up visit plans are realized, and personalization and timeliness of follow-up visit are improved.

The invention relates to the technical field of stomach tube interaction, and discloses an intelligent multi-mode man-machine interaction stomach tube system, which comprises a stomach state threshold evaluation module, a stomach state monitoring module, a stomach interaction module and a stomach tube verification module, and is characterized in that the stomach state threshold evaluation module comprises a patient correlation evaluation unit and a patient risk threshold evaluation unit; the stomach state monitoring module comprises a stomach comprehensive risk analysis unit and a first risk assessment unit, and the stomach interaction module comprises a fluctuation risk assessment unit and a second risk assessment unit. According to the system, historical patient data and current patient information are integrated, high-risk samples are screened through correlation coefficient calculation, a personalized risk threshold value is constructed, evaluation objectivity is improved, multi-dimensional stomach parameters are collected, the risk coefficient is synthesized to comprehensively reflect the stomach state, and the risk coefficient is used for evaluating the state of the stomach. The multi-modal data is converted into a visual stomach risk fluctuation coefficient and is dynamically displayed to assist medical personnel in quickly making decisions, so that the stomach condition of the patient is accurately monitored.

The invention discloses a critical patient risk prediction method based on big data, and the method comprises the following steps: S1, carrying out the butt joint of a hospital multi-department electronic medical record system, organizing medical information personnel and department experts to formulate a unified data interface standard, and regularly capturing data through an interface; s2, noise data are removed through data mining recognition, a disease association map is constructed and trained through a graph neural network framework, and recessive common disease association is mined; s3, aligning a recessive common disease association time sequence by using a time sequence causal inference model, inputting common disease feature training, identifying and eliminating false association, and retaining a true causal relationship; s4, when the difference between the true causal and the actual causal exceeds a set threshold value, triggering reverse updating of the model, and adjusting a corresponding common disease association weight value in the graph neural network according to a result; therefore, early warning and personalized intervention suggestions can be pushed in real time, medical workers are assisted in timely intervention, and the severe illness treatment efficiency is improved.

The invention relates to the technical field of tumor nursing, in particular to a tumor nursing service management system based on artificial intelligence, and the system comprises a medication detection module, a combined medication module, a physical sign recognition module, an action pushing module and a resource scheduling module. And comparing the actual dose with the standard dose. According to the invention, by automatically analyzing a continuous medication mode of a patient, physical sign changes and real-time laboratory monitoring data and fusing multi-source health parameters, dynamic collection and linkage judgment are completed, risk factor high-frequency identification and sensitive physical sign fluctuation monitoring are realized, intervention measure pushing information is generated in time, and a nursing task response process is optimized; by means of multi-dimensional data fusion and intelligent task sorting, accurate matching of patient risk management and nursing resource scheduling is achieved, so that a data closed loop and an intervention closed loop in the whole tumor nursing process are promoted, and the initiative and adaptability of nursing decisions are enhanced.

The invention provides a full-period follow-up visit patient risk dynamic management method based on time series data analysis. The method comprises the steps of performing standardization processing on multi-source follow-up visit data of a patient; data acquisition is dynamically optimized through an adaptive strategy based on reinforcement learning, and multi-scale time sequence features are extracted; performing dynamic risk assessment by using the attention mechanism enhanced double-layer LSTM model, and outputting a risk probability; and calculating a comprehensive risk index by integrating the risk probability, the change trend and the volatility, and realizing dynamic risk layering and automatic intervention based on a rule engine. According to the method, a traditional fixed acquisition mode is converted into a dynamic optimization process capable of responding to patient risks, equipment states and system loads in real time, on the premise that high-risk patient monitoring is guaranteed, system resource consumption is remarkably reduced, and long-term optimal balance of data quality, system performance and resource overhead is achieved.

The invention relates to an intelligent platform management system carrying infusion port patients, and relates to the technical field of data management, and the system comprises a monitoring unit which is used for collecting impedance data, temperature data and medicine use time sequence data; the intelligent analysis unit is used for constructing a dynamic health baseline of the patient, calling a preset machine learning model based on the dynamic health baseline and outputting a complication risk level of the patient; the closed-loop execution unit is used for triggering a three-dimensional life behavior guidance scheme matched with the patient state based on the complication risk level, calculating a corresponding infusion port maintenance period based on the real-time smoothness score, and generating a medicine supply instruction in combination with medicine use time sequence data; and the hierarchical interaction platform is used for generating an early warning view containing the risk level of the patient and the key attention information based on the complication risk level, and pushing maintenance appointment suggestions and planning contents at the patient side based on the maintenance period of the infusion port. The system can improve the management efficiency and safety of the patients in the infusion port.

The invention provides a pancreatic cancer multi-modal hierarchical fusion prognosis prediction system and method.The system comprises a data input module, a preprocessing module, a multi-modal feature fusion module and a risk prediction module, and the data input module is used for receiving and managing medical image data, clinical table data and text report data of a patient; the preprocessing module is used for performing standardization processing and feature extraction on input medical image data, clinical table data and text report data; the multi-modal feature fusion module is used for fusing the image features and carrying out hierarchical fusion on the multi-modal features; and the risk prediction module is used for calculating a prognosis prediction result based on the multi-modal characteristics and providing support for clinical decision-making of pancreatic cancer. According to the method, the risk stratification of the patient can be accurately carried out, and a powerful basis is provided for clinically formulating a personalized treatment scheme.

The invention discloses an ECMO intelligent monitoring system and method based on multi-modal data fusion, and relates to the technical field of severe recovery monitoring. Acquiring patient basic information of the critical patient, wherein the patient basic information comprises personal information, medical record information and physiological parameter information of a plurality of recent continuous time periods; taking the basic information of the patient as input of a severe recovery condition analysis model for analysis to obtain risk assessment information; the severe recovery condition analysis model comprises a static feature processing branch, a dynamic feature processing branch and a fusion prediction branch, and the static feature processing branch is used for performing feature extraction on personal information and medical record information to obtain a static feature vector; the dynamic feature processing branch is used for performing feature extraction on the physiological parameter information to obtain a dynamic feature vector; and the fusion prediction branch is used for fusing the static feature vector and the dynamic feature vector to obtain risk assessment information. According to the invention, the risk condition of the critical patient can be accurately analyzed in real time while the consumption of medical resources is reduced.

The invention relates to the technical field of risk assessment of diabetic patients, in particular to a risk assessment method and system for diabetic patients. The eye fundus image containing local defects is directly processed through the trained lesion risk assessment model, lesion risk assessment is carried out on the decoupled lesion feature vector, and the preprocessing step of carrying out manual or algorithm restoration on the defect image is omitted, so that distortion errors possibly introduced in the restoration process are avoided, the assessment efficiency is improved, and the method is suitable for popularization and application. By constructing a feature decoupling network and constructing a total loss function including comparison loss, independence constraint loss and classification loss, the model is forced to learn pure lesion feature vectors, lesion information is ensured to be effectively separated from imaging quality information, and the defect that a traditional model excessively depends on image integrity is overcome; the technical problem that the risk assessment effect of most existing DR risk assessment models based on deep learning is poor when a repaired complete eye fundus image is input is solved.

The invention provides a patient risk dynamic assessment method and device, relates to the field of medical health, and solves the technical problems of high assessment result subjectivity and low assessment efficiency caused by manual assessment when a critical patient is assessed in the prior art. The method comprises the steps that clinical index information of a patient is obtained, the clinical index information comprises subjective symptom information and objective index information, and the subjective symptom information comprises subjective evaluation information of a doctor on patient symptoms, subjective self-description information of the patient and / or patient treatment information; the objective index information comprises index information obtained by detecting multiple physiological indexes of the patient; the clinical index information is input into the four-level risk dynamic model, and the risk level of the patient is determined; the output result of the fourth-level risk dynamic model comprises a first-level risk, a second-level risk, a third-level risk or a fourth-level risk; different levels of risks correspond to different rehabilitation treatment schemes. The method is used in a patient risk dynamic assessment process.

The invention relates to the technical field of data processing, and discloses a risk assessment method and system based on multi-modal data, a terminal and a storage medium, and the method comprises the steps: obtaining the multi-modal data of a patient, carrying out the matching and calculation based on a preset VTE risk assessment rule according to the multi-modal data, and obtaining a basic risk level and a basic prevention strategy; obtaining dynamic time sequence data of a patient, adjusting the basic risk level according to the dynamic time sequence data, generating a plurality of dynamic risk weights, and performing fusion calculation on all the dynamic risk weights to obtain a scoring result; and obtaining an early warning trigger condition, determining a target intervention strategy in the basic prevention strategies according to the early warning trigger condition and the scoring result, and executing the target intervention strategy. According to the method, the VTE risk of the patient is iteratively optimized through a dynamic weight fusion mechanism, and the accuracy of risk assessment and intervention strategies is greatly improved.

The invention discloses an ophthalmology nursing grade automatic evaluation system based on multi-source data fusion, and belongs to the technical field of ophthalmology nursing, and the system comprises a multi-source data collection module which is used for collecting ophthalmology patient multi-source data; the data processing and fusing module is used for preprocessing and fusing the multi-source data of the ophthalmology patient to form ophthalmology patient fused data; and the intelligent analysis and evaluation module is used for constructing an ophthalmology patient risk evaluation model, analyzing and identifying the ophthalmology patient fusion data according to the ophthalmology patient risk evaluation model, determining an ophthalmology patient risk evaluation result, and automatically dividing nursing grades according to the ophthalmology patient risk evaluation result. The problems that the objectivity and accuracy of ophthalmology nursing grade evaluation are low and the medical quality and efficiency are reduced due to the fact that automatic evaluation of the ophthalmology nursing grade cannot be achieved in the prior art are solved. According to the invention, the automatic evaluation of the ophthalmic nursing grade can be realized, the objectivity and accuracy of the evaluation of the ophthalmic nursing grade can be improved, and the medical quality and efficiency are improved.

The invention discloses an intelligent pressure sore risk management and nursing decision support system and method. A patient risk file is established by collecting patient inspection index data, skin image data and Braden scale evaluation data, a normal index area is identified to form an index reference domain, and multi-part associated monitoring configuration is established; performing feature extraction on the inspection index data to form an index change curve, and performing high-risk combination mode recognition in combination with Braden scale assessment data to generate a grading risk assessment benchmark; executing time axis dynamic tracking on the skin image data to generate a skin state change sequence, and generating a burnishing positioning result through burnishing area detection; and matching the red pressing positioning result with the skin characteristic parameters to identify a risk area, executing risk level dynamic adjustment according to the risk area in combination with a grading risk assessment criterion to generate an injury degree value, matching personalized nursing measures to generate a nursing decision result, and providing intelligent decision support for clinical nursing.

An intelligent dosing platform incorporating comprehensive predictive analytics and forecasting capabilities for injectable medication administration. The platform includes predictive analytics modules configured to generate forecasts for financial costs, medication demand patterns, and adverse event probabilities using machine learning algorithms and statistical modeling techniques. The system features financial modeling modules that analyze cost patterns associated with medication procurement, administration infrastructure, and insurance reimbursements, while demand forecasting modules predict medication needs based on seasonal trends, patient population changes, and treatment protocol modifications. Adverse event prediction modules identify risk factors using patient risk profiles and clinical outcome histories. The platform incorporates machine learning capabilities that continuously improve prediction accuracy using patient outcome data and administration patterns. Comprehensive reporting modules generate customized predictive reports for healthcare administrators, pharmaceutical suppliers, and insurance providers, while optimization modules recommend actions for cost reduction and patient safety improvement based on predictive insights.

This invention discloses a cloud-based data interaction-based exercise monitoring and guidance system for venous thrombosis prevention, belonging to the field of medical and health technology. It includes a patient-side app for exercise monitoring, incentive feedback, intelligent error correction and guidance, and flexible reminders; a nurse-side app for patient risk stratification, personalized intervention plan generation, and closed-loop data management; a cloud server for data storage, processing, and analysis, and supports multi-terminal data synchronization and sharing; and a wearable device integrating high-precision sensors. This invention constructs a multi-terminal data interaction architecture based on a cloud server, achieving an efficient and stable data synchronization mechanism between the patient-side app, nurse-side app, and wearable device. Data synchronization latency is extremely short, and data upload frequency is reasonable and efficient. This innovative design ensures that patient exercise data can flow smoothly and in real-time between all terminals, enabling nurses to obtain the latest patient exercise status information immediately.

A patient dashboard display selectively shows bed status data, vital signs data, and patient risk data for a number of patients in a tile format or in a table format. In the tile format, less data is shown for each patient than in the table format. A series of thumbnails corresponding to patient rooms in a unit or ward of a healthcare facility are shown on the display simultaneously with the tiles. A number of thumbnails in the series of thumbnails is greater than a number of the tiles, with each thumbnail being smaller than each tile. The tiles and thumbnails are color-coded to indicate alert status for each patient or patient room.

The invention belongs to the technical field of chronic disease medical health management, and provides an artificial intelligence-based personalized health management method for cardiovascular and cerebrovascular chronic disease patients, and the method comprises the steps: collecting patient data through a wearable device, binding the wearable device with a follow-up visit app through Bluetooth connection, transmitting the data to a follow-up visit system, and carrying out the storage and recording according to the patient i d and a device identifier; the method comprises the following steps: forming a multi-dimensional vector from daily measurement data of a patient, pre-defining a standard range and an intervention strategy type of each dimension, calculating a risk assessment value of the patient, mapping a risk level, selecting an intervention strategy by utilizing a reinforcement learning model, performing adjustment according to a risk level sequence, and giving a specific numerical value; performing follow-up visit according to the risk level, calling recent measurement data of the patient for checking, calculating the follow-up visit weight of the patient needing door-to-door follow-up visit, and performing door-to-door follow-up visit according to the weight sequence; the change condition of daily measurement data of the patient is displayed in a follow-up visit system, the score condition executed by the patient is calculated, and corresponding incentive measures are given.

The invention relates to the technical field of health risk assessment, in particular to an enteral nutritional complication risk assessment system and an early warning method. The method comprises the following steps: acquiring a multi-modal risk factor of each enteral nutritional complication patient in a time sequence; determining a fluctuation coefficient according to the numerical fluctuation of the patient risk factor; determining the individuation degree by combining the fluctuation similarity of each type of risk factors of all patients; determining a particularity index for complication evaluation by combining the time sequence change amplitude of the same type of risk factors; according to the numerical fluctuation similarity of any two risk factors of the patient, the influence degree is determined; acquiring a risk factor chain of each complication; determining a chain reaction index according to the probability of occurrence of complications caused by the risk factors and the degree of influence; and in combination with a particularity index and a chain reaction index, determining a causal weight of a complication caused by the risk factor, and realizing risk assessment and early warning. According to the invention, the accuracy of risk assessment can be effectively improved.

An automated system that selects an optimal combination of risk models for a target patient population. The selected combination may be monitored by clinicians to determine which patients are at greatest risk for adverse events or clinical deterioration. The system may compare risk model data for hundreds or thousands of models to data collected on a target patient population to determine which combination of models is the best fit for this target group. An illustrative selection method may minimize a cost function that measures the deviation between a model combination and desired features for an optimal combination. Illustrative factors in the cost function may include differences between the predicted risk distributions for the target group, using the model risk function, and the risk distributions for the dataset used to train the model, and correlation among risks predicted by the models in the combination.

A method and system for determining a risk of an infection for a patient is disclosed. In one embodiment, the system and method includes extracting patient data from one or more databases corresponding to a pool of patients under treatment; using the extracted patient data with one or more predictive models to generate a respective patient risk score for each patient in the pool of patients for developing an infection within a selected time period; generating a report including at least a portion of an identified subset of the pool of patients and their respective patient risk scores; and transmitting the report to one or more healthcare institutions that further identify one or more patients from the portion of the identified subset of the pool of patients for an intervention treatment, consultation, training, or a combination thereof.

Disclosed herein are systems and methods for planning a knee arthroplasty procedure. A method may include registration of patient anatomy, including specific registration of the patella; anatomy modelling based on the registration; automated generation of anatomical landmarks; morphological characterization; risk classification; and a planning optimization process based on the morphological characterization and the risk classification. The risk classification may include a determination of potential patellar complications based on patient characteristics, such as patient demographic information and / or patient anatomical information compared with historical information of patient outcomes. The planning optimization process may determine knee implant components and / or the configuration thereof within the patient based on the patient risk classification.

The invention relates to the technical field of risk assessment, and discloses a lung transplantation patient postoperative risk auxiliary judgment method based on data analysis, and the method comprises the steps: S1, carrying out the model training according to collected data, obtaining a risk calibration model, and obtaining a real-time risk value according to the risk calibration model and current data; s2, according to a critical threshold value of the real-time risk value and the change characteristics, performing preliminary judgment on the postoperative risk, and when a preliminary judgment result is high risk, sending out first early warning information; and S3, comparing the graph change track of the numerical value of the real-time risk value with a preset risk change track, performing depth judgment according to a comparison result, and sending second early warning information when a depth judgment result is high risk. The postoperative risk is preliminarily judged according to the critical threshold value and the change characteristics of the real-time risk value, depth judgment is performed according to the graphic change track of the numerical value of the real-time risk value and the preset risk change track, and the risk of the patient can be continuously, timely and accurately judged.

The invention discloses a grading intelligent evaluation system and method for patients in an anesthesia recovery period, belongs to the technical field of anesthesia, and solves the problems that subtle changes of numerous physiological parameters of the patients are difficult to comprehensively and continuously monitor by adopting a traditional evaluation mode and depending on self-evaluation of indexes of a monitor and the like at present; the method comprises the following steps: collecting physical sign monitoring data of a patient in real time based on a sensor and an Internet of Things technology, carrying out multi-dimensional parallel evaluation analysis on the physical sign monitoring data of the patient by an anesthesia recovery period patient evaluation model, and outputting an evaluation analysis result of the patient. Judging whether the real-time state evaluation level exceeds a preset patient risk threshold value or not; according to the method, the anesthesia recovery period patient evaluation model is used for carrying out multi-dimensional parallel evaluation analysis on the physical sign monitoring data of the patient, the nursing grading requirement of the patient is judged in an intelligent mode, objectivity and scientificity of nursing grading decision making are ensured, nursing quality is improved, complication risks are reduced, and meanwhile the comfort degree and satisfaction degree of the patient are improved.

Disclosed are systems and methods for training a risk stratification model for estimating patient risk levels. Weights are extracted from a source model, the weights defining a relationship between patient data and patient risk level. The weights are used to calculated a score for each patient in a target population, and the source model is then refitted to the target population, using a transfer learning estimator based on the calculated scores. The refitted source model can then be outputted for use.

Providing information processing systems and methods suitable for patient care. [Solution] The information processing system includes: an acquisition unit that acquires first data associating a clinical department with a risk set representing the risk of patients related to that clinical department; a processing unit that, when it acquires information indicating that a patient hospitalized in the first clinical department is receiving treatment from the second clinical department, performs an evaluation process for each of the one or more risks included in the second risk set, which is the risk set associated with the second clinical department in the first data; and an output processing unit that presents the results of the evaluation process for the second risk set to the nurses of the first clinical department.

The invention discloses a patient risk prediction method and system for congenital heart disease, belongs to the technical field of medical big data analysis, and safely obtains and integrates comprehensive data of a patient from a multi-source heterogeneous medical system. Comprising the following steps: structuring an electronic medical record, a medical image text report, a time sequence physiological signal and optional genome and lifestyle data to form a unified patient time sequence data set, and carrying out deep feature extraction through multi-modal feature engineering: calculating time domain, frequency domain and nonlinear features from the physiological signal; key clinical semantic features are extracted from a text report by using a deep learning model based on an attention mechanism, an ensemble learning algorithm is adopted to train a risk prediction model, a model interpretation technology is introduced to quantify the contribution degree of each feature to a prediction result, and patient individualized risk probability values and driving factor analysis are output; and a transparent and explainable quantitative basis is provided for clinical decisions. According to the invention, the risk of the congenital heart disease patient can be accurately and dynamically evaluated.