67 results about "Postoperative risk" patented technology

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 relates to the technical field of postoperative risk prediction, in particular to a breast cancer postoperative bleeding risk prediction method and system based on machine learning, and the method comprises the steps: extracting feature variables for breast cancer postoperative bleeding risk prediction from personal data and clinical data through feature importance analysis; utilizing a plurality of machine learning models to construct a plurality of prediction models based on the characteristic variables, and performing performance evaluation to select a prediction model with optimal overall performance; and carrying out local index optimization on the prediction model with the optimal global performance by utilizing reinforcement learning to obtain an optimal prediction model. According to the invention, a plurality of prediction models for breast cancer postoperative bleeding risk prediction are constructed by using a plurality of machine learning models, performance evaluation is carried out on the plurality of prediction models to screen out an optimal prediction model, and then index optimization with insufficient precision is carried out on the optimal prediction model one by one in combination with reinforcement learning. And obtaining a prediction model which is optimized in performance and helps to identify high-risk patients.

The utility model relates to a suturing device. The suturing device comprises an outer sleeve and a suturing mechanism. A window allowing target tissue to enter is formed in the side wall of the far end of the outer sleeve, the window is communicated with an inner cavity of the outer sleeve, and the inner cavity is further used for being communicated with a negative pressure source. The suture mechanism is arranged in the outer sleeve and used for suturing the target tissue sucked into the window. When the suturing device is used for suturing a sampling channel, the suturing device extends into a preset part in the sampling channel to adsorb the target tissue on one side wall of the sampling channel, and the adsorbed target tissue protrudes out of the inner wall of the sampling channel; therefore, the suture mechanism can suture the target tissue sucked into the window. Therefore, a binding and pressing mode in the related technology does not need to be adopted, so that the rehabilitation time can be shortened, and the postoperative risk is reduced.

The invention relates to the technical field of medical care, in particular to a digestive endoscopy postoperative intelligent nursing system based on deep learning. The system comprises an endoscopic postoperative data acquisition and processing module, an endoscopic postoperative feature analysis module, an endoscopic postoperative recovery risk assessment module and a postoperative recovery risk intelligent nursing module, and can acquire physiological data of a patient after a digestive endoscopic surgery and digestive tract state image data after the endoscopic surgery; the long and short time memory network is used for obtaining the long and short time sequence characteristics of the physiological indexes after the endoscopic surgery, the convolutional neural network is used for identifying and obtaining the healing abnormal lesion characteristics after the endoscopic surgery, the characteristics are input into the preset deep neural network model for characteristic fusion, and postoperative recovery risk assessment calculation and postoperative intelligent nursing analysis are carried out. And generating digestive endoscopy postoperative patient recovery nursing suggestions so as to execute corresponding digestive endoscopy postoperative risk recovery nursing operation. According to the invention, postoperative data of the patient can be accurately analyzed, so that nursing decision-making work can be intelligently guided.

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 discloses an AI optimal storage system with an automatic storage function and capable of being connected with a hospital HIS system, and relates to the technical field of medical information management and data storage, the AI optimal storage system comprises a data input end, a data processing end, an HIS system docking module, an automatic storage strategy generation end, a cache management end, a data security and authority control module and a user interface end; the data input end is responsible for receiving and collecting medical data from all systems of a hospital, and the medical data comprise operation images, medical images, patient electronic medical records and monitoring data. The data processing end is used for preprocessing input data, and the preprocessing comprises format conversion, data cleaning, compression and duplicate removal. The intelligent degree of data storage is remarkably improved, remarkable effects are achieved in the aspects of data management efficiency, cross-system integration, postoperative risk management, data safety and the like, and the high-standard requirements of modern hospitals for operation data storage, analysis and management are met.

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 provides an intelligent decision-making method and system for free flap riser anastomosis based on hemodynamics. According to the method, firstly, the blood flow direction, the flow speed and the blood vessel wall tension parameter of a target blood vessel are collected, and a dynamic correlation model of intravascular fluid mechanics and external tissue deformation is established; a three-dimensional blood flow pressure gradient map is generated based on the model, then, a system automatically screens an anastomosis path meeting a blood flow resistance threshold value and the endothelial cell survival rate requirement, and the optimal anastomosis pose is determined by analyzing the blood vessel wall elasticity matching degree and the turbulence generation probability. Then, guiding the accurate adjustment of the clamping direction of the blood vessel and the suture needle pitch in the operation, ensuring that the pressure of the lumen of the anastomosed blood vessel is kept balanced when the anastomosed blood vessel is axially stretched, meanwhile, effectively inhibiting local vortex, and remarkably improving the long-term smoothness of the anastomosis of the blood vessel. According to the method, mechanical precise matching and blood flow stable control of free flap vascular anastomosis are achieved, and the postoperative risk is remarkably reduced.

The utility model relates to an adjustable bent bone cement filler which comprises a sleeve, an injection hole and a threading hole are formed in the sleeve, and a traction wire is arranged in the threading hole. The upper end of the bent pipe is fixedly connected with the lower end of the sleeve, and the lower end of the bent pipe is provided with a fixed pipe connected with the lower end of the traction wire; the handle is fixedly arranged at the upper end of the sleeve, a bone cement injection opening is formed in the handle, and the injection hole is connected with the bone cement injection opening; the sliding block is movably arranged on a handle, and the sliding block is connected with the upper end of the traction wire; and the sliding block pulls the traction wire upwards, so that the lower end of the traction wire pulls the fixed pipe, and the bent pipe is driven to deform from a bent shape to a straight shape. The bent pipe can be adjusted to different angles through the sliding block on the handle, so that bone cement can be conveniently filled into the cavity in a short time, and the postoperative risk is reduced.

The invention provides a postoperative risk assessment system and device, a storage medium and a program product, and relates to the technical field of computers, and the system comprises an extraction module which is used for extracting independent risk factors of early decline of a vascular bridge from postoperative data of a patient; the score determination module is used for determining the score of each factor in the independent risk factors based on a score scale in the column graph model; the column graph model is constructed based on independent risk factors; and the prediction module is used for predicting risk information of early decline of the vascular bridge after coronary artery bypass transplantation based on the risk coefficient corresponding to the total score of each factor in the independent risk factors. Based on Lasso regression analysis and Logistic regression analysis results, a vascular bridge early decline prediction model with risk factors such as QFR greater than 0.80, PI greater than 3.0, complications and non-left trunk stenosis as main bodies is constructed, so that the accuracy of risk assessment of vascular bridge early decline of postoperative patients is improved.

The invention discloses an intelligent evaluation method for screening multiple diseases of lumbar vertebra based on deep learning, and relates to the technical field of intelligent screening of lumbar vertebra diseases, and the method comprises the steps: collecting multi-source data, and obtaining lumbar vertebra CT, MRI, bone mineral density images, clinical diagnosis and treatment data and operation related parameters; preprocessing data, denoising images, segmenting key structures, and standardizing non-image data; a deep learning model is constructed, image features are extracted through CNN branches, non-image features are processed through Transform branches, and disease classification, severity assessment and risk prediction are achieved in combination with multi-task learning; integrating the multi-modal features through a feature fusion optimization algorithm, and outputting an evaluation result; doctor interaction correction and model iteration are supported, and the generalization ability of the system is improved. According to the method, multi-source data are integrated, the accuracy and efficiency of lumbar vertebra multi-disease assessment are improved, postoperative risk prediction is optimized, clinical diversified requirements are met, and powerful support is provided for precise diagnosis and treatment and rehabilitation.

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 provides a medical diagnosis guide method and system based on a knowledge graph. According to the method, physical sign data of emergency orthopedic patients and processing precision data of orthopedic implants are obtained, the physical sign data and the processing precision data comprise dimensional tolerance, surface roughness, material strength and other parameters, the physical sign data and the processing precision data are subjected to multi-modal association according to fracture types, and a dynamic knowledge graph containing patient skeleton features, implant processing precision and inventory state nodes is constructed; and a dynamic priority queue is set. And based on the dynamic knowledge graph, matching the fracture characteristics of the patient with the precision requirement of the implant, and generating a hospital guide path set covering the surgical plan compatibility, the resource topological relation and the postoperative risk prediction. And when the implant processing precision or the inventory state changes, the system triggers priority queue reconstruction, and the hospital guide path set information is updated in real time. According to the technical scheme provided by the invention, the efficiency and precision of medical diagnosis guide can be improved.

The invention relates to the technical field of postoperative risk prediction, in particular to a cardiovascular intervention postoperative nursing risk prediction system based on physiological feedback. The method comprises the following steps: firstly, acquiring physiological data of historical patients and risk values of postoperative care, and grouping; the aggregation of the risk values of the patients is further analyzed, and the initial typical degree is obtained in combination with the risk values; further analyzing the reference weight of other groups for each physiological data of the target group, analyzing the correlation between the physiological data of other groups and the maximum initial typical degree in other groups, and obtaining the prediction importance of each physiological data of the target group; the final typical degree is further obtained; and finally, based on the final canonical degree fusion risk value of the patients in each group, obtaining a risk optimization value and constructing a prediction decision tree, thereby improving the representativeness and individualized prediction capability of the decision tree.

The present invention provides a postoperative risk assessment system, device, storage medium and program product, relating to the field of computer technology. The system includes: an extraction module for extracting independent risk factors for early saphenous vein graft failure from the postoperative data of a patient; a score determination module for determining the scores of each factor in the independent risk factors based on the scoring scale in the nomogram model, where the nomogram model is constructed based on the independent risk factors; and a prediction module for predicting the risk information of early saphenous vein graft failure after coronary artery bypass grafting based on the risk coefficient corresponding to the total score of each factor in the independent risk factors. Based on the results of Lasso regression analysis and Logistic regression analysis, the present invention constructs a prediction model for early saphenous vein graft failure with risk factors such as QFR > 0.80, PI > 3.0, complications, and non-left main stenosis as the main body, thereby improving the accuracy of the risk assessment of early saphenous vein graft failure in postoperative patients.

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 invention relates to the technical field of medical care information, in particular to an orthopaedic spine post-operation imaging auxiliary evaluation system, which comprises a region acquisition module, a region correction module, a callus growth coefficient acquisition module and a network training module, the method comprises the following steps: acquiring an initial vertebra fusion region in a spinal image of a historical patient after a spinal fusion operation, and correcting the initial vertebra fusion region to obtain a real vertebra fusion region; determining a plurality of first intervertebral gradient regions and second intervertebral gradient regions in the real vertebra fusion region, and determining a callus growth coefficient of the real vertebra fusion region according to position distribution and pixel gray levels of the first intervertebral gradient regions and the second intervertebral gradient regions; and forming a training set by utilizing the callus growth coefficients of all historical patients and the postoperative risk grades calibrated in the initial vertebra fusion region, and training the postoperative risk classification network. According to the invention, the classification accuracy of the postoperative risk classification network is effectively improved.

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 present invention discloses a clamping and locking medical device and a method for using the same, comprising: a first connecting element, one end of which has a clamping portion and the other end of which has a clamping component extending axially; a second connecting element, a side wall of which is provided with a connecting device connected to the first connecting element; an inner hole is provided at its axis; a rod, the rod passes through the second connecting element and the clamping component in sequence and is connected to the first connecting element; the rod pulls the first connecting element to move forward and backward to adjust the clamping angle, and after the human tissue is clamped in place, the clamping component is released to fix the angle of the clamping portion, and then the rod is released to complete the clamping. The present invention can quickly clamp human tissue, improve surgical efficiency, and is easy to operate; it also avoids the problem of overall rotation of the device caused by the use of a threaded rotation adjustment method; the newly designed clamping structure is adopted to reduce the axial size of the device, reduce the weight of the medical device, the patient's load, and the postoperative risk.

The invention provides a postoperative risk prediction method and system based on neighbor component analysis, and a storage medium. The method comprises the following steps: S1, sampling data; s2, forming a patient instance representation with semantic information, and mapping the patient instance representation to a low-dimensional space by using a nonlinear neural network to obtain a representation vector with semantic information; s3, obtaining a target patient instance, and performing coding processing to obtain a target patient instance vector; s4, calculating the distance between the target patient instance vector and each instance vector in the representation vector by using cosine similarity to form a distance vector; and S5, predicting an outcome event of the target patient instance according to the distance vector. According to the method, the extra retrospective patient data can be efficiently utilized for postoperative risk prediction, the patient instance characterization is optimized, and the accuracy and reliability of the prediction result are improved.

The invention discloses a heart valve postoperative death risk prediction method based on physiological index interaction, and relates to the technical field of medical engineering. The method specifically comprises the steps that S1, variable importance analysis is carried out to reduce interference of unimportant feature physiological indexes; s2, analyzing a survival curve for determining the influence of the key feature physiological indexes on the postoperative survival state of the patient; s3, constructing an intelligent discrimination model, and constructing the model by adopting a classification machine learning algorithm; and S4, intelligent discrimination and prediction: screening the postoperative death risk prediction model of the heart valve patient for predicting the postoperative death risk of the heart valve patient, and reducing the discrimination difficulty. According to the method, by analyzing the synergistic effect and antagonism among different characteristic physiological indexes, the heart valve postoperative death risk prediction model with high accuracy and good robustness is screened, and physiological data is provided for postoperative risk analysis and evaluation of heart valve patients.

The invention relates to the technical field of postoperative risk prediction, in particular to a dental pulp postoperative risk prediction method based on data analysis, and the method comprises the steps: obtaining a detection sample of a surgical site, continuously obtaining state characterization data, and periodically obtaining body temperature data; carrying out flora cultivation on the detection sample, and determining a characteristic time period according to historical data and occurrence conditions of postoperative risks; constructing a skin temperature time sequence image and a skin surface time sequence image; determining a fluctuation discrete parameter of the skin surface time sequence image, and judging a postoperative risk occurrence type according to the fluctuation discrete parameter in combination with the body temperature data; predicting the postoperative risk according to the occurrence type of the postoperative risk in combination with other data; determining the type of post-operation risk early warning according to the prediction result and the flora cultivation result, and sending out early warning; according to the method, the prediction sensitivity and precision are improved, normal inflammatory response and early infection can be effectively distinguished, and the defect of isolated analysis of clinical data is avoided.

The invention relates to the technical field of post-anesthesia complication monitoring, in particular to an intelligent pre-warning system for post-anesthesia complication of a surgical patient. The system obtains an intraoperative risk degree according to the size and change of intraoperative body index data of a patient in each first preset time period and past medical history scores and allergy history scores of the patient, and further judges an intraoperative anesthesia complication patient and an intraoperative normal patient in real time; according to the size of the post-operation body index data, the consciousness state score, the nervous system score, the wound recovery score and the waking state of the normal patient in each second preset time period and the post-operation risk degree of the normal patient in the operation, the post-operation risk degree is obtained, and then the post-operation anesthesia complication patient and the normal patient in the operation are judged in real time. According to the method, the intraoperative risk degree and the postoperative risk degree are accurately obtained in real time, so that the post-anesthesia complications of the patient are accurately detected in time, measures are taken in time, and the harm of the post-anesthesia complications is effectively reduced.

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 application discloses a myopia surgery scheme generation method, a generation system and a storage medium, and comprises the following steps: collecting historical surgery data, wherein the historical surgery data comprises preoperative data, intraoperative data and postoperative data of a plurality of patients, the preoperative data comprises eye parameters, living habit data, previous medical history data and patient basic information; preprocessing the historical surgery data to obtain a training set; constructing a dynamic Bayesian network model based on the training set; collecting preoperative data of a patient to be diagnosed, inputting the dynamic Bayesian network model, and generating a myopia surgery scheme of the patient to be diagnosed based on a surgery scheme library. Through multi-modal data fusion to construct a dynamic Bayesian network, preoperative individualized scheme recommendation, intraoperative real-time parameter optimization and postoperative risk tracking are realized, and the surgery success rate and patient satisfaction are improved.

The application relates to the technical field of postoperative early warning, and particularly discloses a postoperative risk early warning method and system based on machine learning, which belongs to the technical field of postoperative early warning, and the method comprises the following steps: acquiring historical patient data of a plurality of patients who have undergone a to-be-early-warned operation, determining an adverse prognosis set of the to-be-early-warned operation and adverse data of each adverse prognosis in the adverse prognosis set; determining an influence factor vector of each adverse prognosis in the adverse prognosis set and an independent prognosis factor set; determining a plurality of preset non-independent factor sets of each adverse prognosis in the adverse prognosis set; determining a non-independent prognosis factor set of each adverse prognosis in the adverse prognosis set; and realizing postoperative risk early warning. The postoperative risk early warning method can comprehensively cover the postoperative risk influence dimension, accurately capture data correlation, realize intelligent transformation from data to risk prediction, provide more accurate and comprehensive postoperative risk early warning for the clinic, and improve the scientificity, accuracy and reliability of postoperative risk early warning.

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.