24 results about "Postoperative risk" patented technology

The invention relates to the technical field of intelligent medical treatment, in particular to a urinary surgery postoperative risk layering intelligent nursing management system which comprises the steps of preprocessing dynamic sign flow data of a patient to obtain regular sign time sequence data; and separating a core vital sign track, a wound exudation quantitative sequence and an autonomous perception discomfort record through modal analysis. The feature calculation module compares the vital sign trajectory with a standard recovery trajectory library to generate a sign bias spectrum, analyzes the exudation sequence to obtain a wound humidity distribution vector, and calculates a time attenuation factor of the pain event according to the discomfort record. And the space construction and layering module combines the features to construct a multi-dimensional risk base space, and identifies boundary coordinates of high-risk, medium-risk and low-risk risk clusters through clustering. And the strategy generation module divides risk levels and allocates corresponding monitoring and intervention measures according to the risk levels. According to the system, dynamic and multi-dimensional quantitative evaluation of the postoperative recovery state and objective automatic layering of risks are achieved.

The invention discloses a glioma radiotherapy postoperative risk assessment method based on a magnetic resonance image, particularly relates to the field of glioma radiotherapy patient health risk assessment, and is used for solving the problem that an existing assessment mode depends on manual interpretation and is difficult to predict bad clinical outcomes in advance. The method comprises the following steps: performing clinical data gridding reconstruction on a corticoid use cycle of a patient and tumor molecular typing, and combining morphological characteristics of an edema region in a magnetic resonance image to generate time-aligned clinical comprehensive characteristic vectors; mining a frequent association item set between the comprehensive feature vector and the pathological process to construct a mapping relation model; establishing a probability graph reasoning model of the bad outcome based on the pathological process vector sequence and the probability weight; and integrating the two types of models to form a causal reasoning network, and inputting a target patient feature vector to calculate an accumulated risk value of reaching a bad outcome. According to the method, an interpretable individual risk assessment result can be output, and a basis is provided for postoperative follow-up visit and intervention.

The invention relates to the technical field of clinical medicine, and discloses an intertrochanteric fracture postoperative progressive rehabilitation system, which comprises an intertrochanteric fracture comprehensive data acquisition module, a dynamic data monitoring module, a rehabilitation scheme generation module, a rehabilitation execution module, a rehabilitation mode adjustment module, a diet structure adjustment module and a rehabilitation interaction feedback module, the intertrochanteric fracture comprehensive data acquisition module is responsible for acquiring static and semi-dynamic data of a rehabilitation full cycle, the dynamic data monitoring module monitors physiological and motion change data of a patient in daily activities in real time, the rehabilitation scheme generation module generates a personalized stage rehabilitation scheme, and the rehabilitation execution module executes a full-process rehabilitation action. The rehabilitation mode adjusting module is used for adjusting a rehabilitation exercise mode, the diet structure adjusting module is used for optimizing a diet structure, and the rehabilitation interaction feedback module is used for collecting feedback so as to correct the scheme.

The invention belongs to the technical field of medical information processing, and discloses a wound repair analysis method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining three-dimensional point cloud data, spectral data and patient physiological signals obtained through multiple times of scanning of a to-be-repaired wound; performing dynamic correction on the three-dimensional point cloud data based on the physiological signal of the patient and the spectral data to generate a standardized wound three-dimensional model; performing space-time dimension wound assessment based on the model and the dynamic data of the wound to obtain a quantitative assessment report; and finally, generating an individualized repair scheme through double-path driving of historical data and operator experience. According to the method, the scanning precision is improved through a dynamic correction algorithm fused with physiological signals, advanced prediction of postoperative risks is achieved through a space-time dimension evaluation model, the individual suitability of the scheme is improved through a double-path scheme generation mechanism, and the precision and success rate of plastic wound repair are remarkably improved.

The invention belongs to the technical field of biological medicine, and discloses a portal cholangiocarcinoma postoperative risk prediction model based on a gamma-glutamyltransferase (GGT) dynamic change track and a construction method thereof. The method comprises the following steps: (1) collecting clinical characteristic data and survival characteristic data (including survival time and survival state) of a patient suffering from porta hepatis cholangiocarcinoma subjected to radical resection; 2) on the basis of GGT detection results at different time points in a perioperative period, dividing patients into different GGT dynamic change track types by using a latent category hybrid model; and 3) taking the clinical characteristic data and the GGT dynamic change track type as independent variables, taking survival data as response variables, screening prediction factors through LASSO regression, and constructing a multi-factor Cox regression model, namely a postoperative risk prediction model. According to the method, the dynamic evolution rule of the GGT is introduced, so that the limitation of single-time static detection is made up, the quantitative evaluation of the survival risk is realized, and a scientific basis is provided for individualized treatment and follow-up visit management.

The application belongs to the technical field of molecular biology, and particularly relates to metabolic markers for evaluating the prognosis of patients with hypertrophic cardiomyopathy and application thereof. The application determines that one or more of 3-hydroxyrilotene, asparagine-lysine, cyclohexylamine, arachidonic acid, aconitic acid, propionyl carnitine, ceramide, tryptophan, 3-hydroxysebacic acid, traumatic acid, phosphatidylcholine, alanine-proline, lysophosphatidylcholine, taurine and S-garlicin cysteine are related to the prognosis of hypertrophic cardiomyopathy, can evaluate the prognosis of patients with hypertrophic cardiomyopathy after undergoing muscle resection of interventricular septum, and shows accurate judgment on all-cause death or rehospitalization, cardiovascular events and postoperative new-onset arrhythmia. Finally, all the prognostic biomarkers are reproduced in an independent cohort, which is helpful for postoperative risk evaluation of patients with hypertrophic cardiomyopathy, has high specificity and high sensitivity.

The application relates to the technical field of medical information processing, and discloses a method and system for dynamically evaluating post-renal biopsy occult bleeding risk based on multi-source data fusion, wherein the method comprises the following steps: constructing a standardized uniform observation vector sequence; constructing a short-time instant risk model; constructing a mechanism to establish a long-time lag risk transfer model; generating a hierarchical treatment decision and a review scheduling plan; gain correcting the risk probability; and outputting a final occult bleeding risk grade. Compared with the scheme for postoperative bleeding risk in the prior art, which only performs static risk evaluation based on a single time section index, especially in the case that postoperative vital signs are temporarily stable but there is delayed hemoglobin decline, the technical problem that the continuous prediction of the risk transfer process of'short-time stability-long-time lag deterioration' cannot be realized is solved. Since the time-varying state transfer probability matrix is introduced in the application, and the risk transfer acceleration is determined, the sensitivity of postoperative risk identification is improved.

The utility model discloses an anastomat assembly with a blood coagulation function. The anastomat assembly comprises a cutting knife assembly, a nail box inner core, a nail bin assembly and a nail abutting seat assembly. Wherein the cutting knife assembly comprises a cutting knife head, a cutting knife middle piece and a cutting knife side piece, the cutting knife head is located at the far end of the cutting knife middle piece, and the cutting knife head, the cutting knife middle piece and the cutting knife side piece are fixed together; the anastomat assembly further comprises a thermocouple assembly and an electric connection assembly, and the thermocouple assembly is connected with the electric connection assembly. The thermocouple assembly is arranged on the cutting tool bit and can heat the cutting tool bit; when the cutting knife assembly moves, the electric connection assembly and the thermocouple assembly keep a circuit closed. The endoscopic anastomat assembly is matched with an electric endoscopic anastomat to achieve a blood coagulation function on cut tissue capillaries, the amount of bleeding in an operation is reduced, postoperative recovery is faster, and the operation risk and the postoperative risk are reduced; the number of times for medical staff to clean errhysis at an operation site during an operation can be reduced, the labor of the medical staff is reduced, the operation duration can be shortened, and the working intensity of the medical staff is reduced.

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 application discloses a kind of nerve electrophysiological monitoring equipment, the nerve electrophysiological monitoring equipment includes: urethra detection device, urethra detection device is provided with bladder pressure detection piece and first electromyography detection piece;Rectum detection device, rectum detection device is provided with rectal pressure detection piece and second electromyography detection piece;Monitoring module, monitoring module is electrically connected with bladder pressure detection piece, first electromyography detection piece, rectal pressure detection piece and second electromyography detection piece respectively.According to the nerve electrophysiological monitoring equipment of the application, the nerve electrophysiological monitoring equipment can simultaneously detect the intravesical pressure and intrarectal pressure, the electromyographic activity of urethral external sphincter and the electromyographic activity of anal external sphincter, without multiple monitoring instruments for detection, thereby improving the integration of nerve electrophysiological monitoring equipment and the safety of operation, reduce postoperative risk.

The utility model provides a hip and leg braking device which can be worn on the hip and the unilateral lower limb of a patient and comprises a hip fixator, a thigh fixator, a shank fixator and a supporting plate. The hip fixator is used for wrapping the hip of a patient, the thigh fixator is used for wrapping the thigh on one side of the patient, and the shank fixator is used for wrapping the shank on the side of the patient; the supporting plate penetrates through the hip fixator, the thigh fixator and the shank fixator, is located on the inner sides of the hip fixator, the thigh fixator and the shank fixator and is used for preventing the lower limbs of the patient from bending. According to the hip and leg braking device, the hip, the thighs and the shanks of a patient can be braked at the same time, so that the lower limbs of the patient are effectively prevented from bending, and the postoperative risk is reduced.

The invention provides a postoperative risk prediction method and system based on multi-modal data fusion. The method comprises the following steps: acquiring image data and a clinical index data set; dividing the image data into a plurality of image blocks, and determining a plurality of tumor image blocks from the plurality of image blocks based on an image recognition technology; inputting the plurality of tumor image blocks into a preset first risk prediction model, so that the first risk prediction model performs feature extraction and linear mapping on each tumor image block based on a multi-instance learning framework to generate a corresponding first risk score; inputting the clinical index data set into a preset second risk prediction model, so that the second risk prediction model generates a corresponding second risk score according to a preset prediction factor and the clinical index data set; and performing weighted fusion on the first risk score and the second risk score through a preset statistical model to generate a postoperative risk prediction result, thereby improving the accuracy and reliability of postoperative risk prediction.

The invention discloses a colorectal cancer postoperative risk assessment method, device and equipment and a storage medium, and relates to the technical field of colorectal cancer postoperative assessment, and the colorectal cancer postoperative risk assessment method comprises the following steps: based on a multi-head attention mechanism, segmenting a centrum horizontal CT image of a preoperative and postoperative third lumbar vertebra of a target object, and obtaining a centrum horizontal CT image of a second lumbar vertebra of the target object; obtaining a skeletal muscle area SMA, a visceral fat area VFA and a subcutaneous fat area SFA corresponding to preoperative and postoperative operations; respectively calculating a skeletal muscle index SMI and a visceral fat to subcutaneous fat ratio VSR according to SMA, VFA and SFA before and after the operation; according to the gender of the target object and the preoperative and postoperative SMI and VSR, judging whether the target object is sarcopenia and / or visceral fat obesity; and dividing risk levels according to preoperative and postoperative judgment results. Based on a multi-head attention mechanism, automatic accurate segmentation and risk assessment of the colorectal cancer postoperative body composition are realized, and the analysis efficiency and accuracy are remarkably improved.

The invention provides a left auricle plugging device and a left auricle plugging device. The left auricle plugging device comprises a fixing disc and a plugging disc, the fixing disc is connected with the plugging disc, the fixing disc comprises a first support, the plugging disc comprises a second support, the first support and the second support are both of a net-shaped structure, and the radial size of the second support is gradually increased in the direction close to the first support. The left auricle plugging device solves the problem that in the prior art, after a left auricle plugging device is implanted, adverse postoperative risks are likely to be generated.

The invention provides a postoperative risk intelligent monitoring system and method based on multi-parameter fusion. The method comprises the following steps: acquiring ultrasonic echo data of a postoperative area; determining a plurality of apodization functions, and performing multi-parameter beam forming on the ultrasonic echo data according to all apodization functions; enhancing the ultrasonic echo data through the optimal apodization function to obtain an optimized ultrasonic signal; determining a total-graph coherence coefficient according to the ultrasonic echo data, determining conditional coherence factors for optimizing the ultrasonic signals, and performing multi-parameter fusion on all the conditional coherence factors based on the total-graph coherence coefficient to obtain a plurality of composite weighting coefficients; generating an optimized ultrasonic image according to the composite weighting coefficient and the optimized ultrasonic signal; and inputting the optimized ultrasonic image into a risk identification model to identify a risk target of the postoperative area. By adopting the scheme of the invention, multi-parameter optimization can be carried out on the ultrasonic image by considering the contrast ratio and noise suppression in a complex postoperative environment, so that accurate postoperative risk monitoring is realized.

The present application relates to the technical field of postoperative risk grading identification, in particular to a system for grading the adverse risk of distal remodeling after aortic dissection surgery. The system first determines a thrombus permeation resistance coefficient according to the gray level difference of CT images of the patient at different times, and extracts the thrombus isolation thickness; based on the thrombus permeation resistance coefficient, solves the anisotropic potential function equation to determine the cumulative leakage cost; performs structure tensor analysis on the gradient in the cumulative loss field formed by the cumulative leakage cost to determine the jet channel consistency factor; combines the cumulative leakage cost, the jet channel consistency factor and the thrombus isolation thickness to calculate the fluid impact load borne by the outer wall of the blood vessel; and based on the fluid impact load and the jet channel consistency factor, determines the risk level of different connected domains in the CT image. The present application improves the accuracy of jet impact risk identification.

The invention provides a postoperative concurrent risk prediction method, electronic equipment and a storage medium, and relates to the technical field of image processing. The method comprises the following steps: constructing an initial model based on a Transform architecture, and training the initial model based on a preset training data set to obtain a multi-modal deep learning radiology model; acquiring preoperative medical image data and clinical data of the to-be-detected object; based on the preoperative medical image data, radiomics features are extracted; carrying out feature fusion on the radiomics features and clinical data to obtain multi-modal input features; and inputting the multi-modal input features into a multi-modal deep learning radiology model, and sequentially processing the multi-modal input features by a multi-head attention module, a feedforward neural network module and a classification head module in the multi-modal deep learning radiology model to obtain the risk probability of the target postoperative complications of the to-be-detected object. The automatic prediction from the original image to the postoperative risk probability is realized, the human intervention is reduced, and the clinical availability is improved.

The invention discloses a colorectal cancer postoperative risk prediction method, device and equipment and a storage medium, and relates to the technical field of colorectal cancer postoperative prediction, based on a 3D convolutional neural network model, CT medical image data of a target object is segmented, and a segmentation result of a body composition area and a tumor primary focus area is obtained; extracting body composition indexes and radiomics characteristics of the tumor primary focus area from the segmentation result; screening the clinical pathological risk factors and the body composition indexes to obtain screening factors related to prognosis of the target object; and integrating the screening factor and the radiomics characteristics of the tumor primary lesion area so as to comprehensively evaluate the postoperative total lifetime prediction result of the target object. According to the method, the body composition and the tumor area are automatically segmented, and the column graph is constructed in combination with the radiomics characteristics and the clinical pathological risk factors, so that individualized accurate prediction of the total postoperative lifetime of the colorectal cancer patient is realized, and the efficiency and the prediction precision are remarkably improved.

The application relates to the technical field of preoperative risk prediction, in particular to a preoperative risk prediction method based on a knowledge graph, preoperative vital sign change data is extracted, and an event sequence description is generated; adjacent event pairing is verified based on a medical knowledge graph to identify a sequence abnormal unit; a corresponding postoperative risk state set is found along a graph correlation relationship; a common state connection section is extracted by comparing a risk node correlation path; and a preoperative risk prediction result is generated by mapping a patient data sequence by using the path section. According to the application, preoperative signs are abstracted into a time sequence event structure, a risk trigger clue based on change connection abnormality is introduced, identification is focused on shape deviation instead of a single-point intensity, multiple constraint checks are carried out through stable state combination of a risk path, risk evolution basis is determined, and a result is embedded in a real change sequence, so that the time continuity, individual adaptability and explanation consistency of prediction are enhanced.

The invention provides a kidney transplantation post-operation risk prediction and intervention method and system, a storage medium and electronic equipment, and the method specifically comprises the steps: obtaining multi-source follow-up visit data, and forming a first data set; preprocessing the first data set to form a second data set; performing feature extraction on the second data set to form a feature vector V; inputting the feature vector V into a risk prediction model, processing through the risk prediction model, outputting a risk probability value of the at least one adverse event, and generating a risk prediction result based on the risk probability value; wherein the risk prediction result is a risk score or a risk level; carrying out interpretability analysis on the contribution degree of each feature in the feature vector V according to a risk prediction result to form a risk interpretation report; according to the risk prediction result and the risk explanation report, a suggestion scheme is generated, risk early warning and information pushing are carried out according to the suggestion scheme, and intelligent prediction of the postoperative risk of the kidney transplantation patient is achieved.

The invention relates to the technical field of medical monitoring, and discloses a monitoring system based on a medical bracelet, which is used for solving the problems of false alarm and missing alarm caused by the adoption of a fixed threshold value in the existing postoperative monitoring. The system comprises a data management module, a baseline modeling module, a risk identification module and an early warning execution module. The method comprises the following steps: analyzing preoperative data of a user through a baseline modeling module to establish an individualized physiological baseline and a fluctuation threshold; and the risk identification module continuously calculates the offset between the real-time data and the baseline after the operation, and carries out risk judgment and early warning based on a rule that the offset continuously exceeds an individualized threshold value. According to the invention, through dynamic association and comparison of the postoperative monitoring data and the individual preoperative baseline, conversion from general threshold alarm to individualized trend early warning is realized, and the accuracy and foresight of postoperative risk identification are improved.

The present application relates to the technical field of postoperative body temperature early warning, in particular to a postoperative hypothermia early warning method and system. The method comprises the following steps: acquiring and preprocessing patient multi-source data, extracting features from the multi-source data, adopting a cross-modal attention mechanism, calculating the weight of each extracted feature vector in real time for adaptive feature fusion, performing constraint optimization on the fused features, and outputting a high-dimensional fused feature vector; constructing a hybrid time series prediction model based on the high-dimensional fused feature vector to output the core body temperature prediction value and the hypothermia risk index RI of the patient at a certain future time period; and based on the core body temperature prediction value and the hypothermia risk index RI, formulating a risk standard and a quantitative intervention strategy to realize postoperative hypothermia early warning of the patient. The present application effectively solves the information island problem existing in the traditional single measurement of body surface temperature data, expands the monitoring dimension, and thus improves the accuracy of postoperative risk assessment.

The invention relates to the technical field of artificial intelligence, and discloses a vertebral fracture postoperative risk assessment system based on machine learning. The system constructs a structured feature set including inspection indexes, vital sign sequences, image measurement parameters, medication records and activity behaviors by collecting multi-source postoperative data of a patient; hierarchical expression of multi-source data is realized by adopting an ordered sparse auto-encoder with an explicit semantic sequence, bidirectional time sequence modeling is performed on a sparse hidden space sequence in combination with BiGRU, and a postoperative overall risk index containing various risk scores is generated. The system displays risk levels and key feature contribution information at a doctor workstation and a nursing terminal through a hospital local area network, and pushes a management prompt under a high-risk condition. Unified modeling and prospective risk assessment of complex and multi-mode postoperative data are achieved, and the method is suitable for vertebral fracture postoperative informatization management.