360 results about "Postoperative complication" patented technology

The invention relates to the technical field of vascular surgery, in particular to a vascular surgery interventional intracavity treatment postoperative complication prediction and management system which integrates data acquisition, processing, model training, prediction analysis, comprehensive decision and early warning management and is specially designed for vascular surgery interventional intracavity treatment. The system monitors the postoperative physiological indexes of a patient in real time through multiple sensors, combines clinical data and disease history, carries out physiological sequence prediction and image analysis by utilizing RNN and CNN models, and accurately evaluates the risk of complications. The comprehensive decision-making module generates a personalized management scheme, and the early warning management module monitors in real time and pushes early warning and suggestions in time when abnormity occurs. The system not only improves the prediction accuracy and the management effect, but also optimizes the medical resource configuration, enhances the patient experience, contributes to the development of precise medical treatment and intelligent management, and effectively solves the key problem of postoperative complication prediction and management.

The invention relates to the technical field of medical information, in particular to an intelligent prediction model and method for postoperative complications of anesthetized patients, and the method comprises the steps: collecting preoperative to postoperative complete-cycle clinical data of a patient through a medical data interface; analyzing operation codes to generate risk features, extracting vital sign dynamic features, and establishing a complication probability mapping relation through a multi-modal fusion network; combining the complication probability and pharmacokinetic parameters to construct an optimization model, and solving an individualized anesthetic dosage interval by using a gradient descent algorithm; vital signs are dynamically monitored in the operation, a dose re-optimization mechanism is triggered, the infusion rate is adjusted, and a closed-loop control link is formed; and generating a visual decision report. According to the method, through deep integration of complete-cycle clinical data and multi-modal feature modeling, preoperative physiological parameters, operation coding semantic information and intraoperative vital sign dynamic modes are subjected to fusion analysis, a nonlinear mapping relation between dosage and complication probability is constructed, and the risk prediction precision and individualized adaptability are remarkably improved.

The invention relates to the technical field of medical monitoring and early warning, and discloses a postoperative care risk early warning system for neurosurgical patients. According to the system, real-time monitoring flows of electroencephalogram, intracranial pressure and body surface myoelectricity of a patient are continuously received, and a dynamic interaction network of neuroelectrophysiology and intracranial mechanical states is constructed so as to represent the coupling relation among electroencephalogram rhythm, pressure conduction and postoperative skull windowing area tissue compliance. The system analyzes three types of core parameters of cerebral cortex excitability, cerebrospinal fluid circulation load and cranial cavity compensation space from the network, further calculates a brain tissue perfusion risk and a neural structure compression risk, and generates a corresponding nursing early warning signal and a physiological regulation path. According to the scheme, deep coupling and mechanism risk early warning of multiple physiological signals are achieved, and the postoperative complication risk can be recognized earlier and more accurately.

According to the puncture intervention robot end executing mechanism and system and the positioning method, the end executing mechanism comprises a clamping module and a guide needle module, and the clamping module comprises a carrying piece body, a development board, a connecting piece, a rotary driving mechanism, a clamping device, an ultrasonic probe and an electromagnetic sensor; the needle guide module comprises a guide rail, a sliding block, a needle guide device and displacement driving mechanisms, the coordinate system of the whole system is unified, meanwhile, calibration is conducted through an electromagnetic-visual registration method, operation planning is conducted after preoperative CT scanning is conducted on an affected part of a patient, and after a target pose is obtained through calculation of a constraint positioning algorithm, the target pose is determined to be the target pose. The rotation driving mechanism drives the needle guiding device to rotate, non-coplanar puncture channel path selection is achieved, the displacement driving mechanism drives the needle guiding device to move, the needle entering angle is adjusted, the puncture process and target spot information are monitored in real time based on real-time ultrasonic images, operation errors are reduced, the puncture precision is improved, and the operation safety is improved. And radiation exposure and postoperative complications of a patient are reduced.

The invention discloses a rigidity self-adaptive intravascular stent based on a three-dimensional virtual woven structure and a control method thereof. The method comprises the steps of reconstructing a preoperative tumor blood vessel three-dimensional model based on clinical iconography data, constructing a stent initialization model, starting a hexagonal knitting three-dimensional virtual algorithm, performing intravascular stent knitting modeling, realizing structural parameter optimization and adaptive stent expansion in combination with preoperative image data of a patient, and completing personalized deployment. The wire winding angle and the mesh density of each area of the stent are adjustable, the stent is adaptive to the complex intracranial aneurysm blood vessel form, accurate fitting and risk guiding are achieved, the treatment effect is improved, the postoperative complication risk is reduced, and the stent has good engineering suitability and clinical application prospects.

Disclosed herein are computer-implemented methods and systems for assessing a risk value of a target patient, the method comprising: receiving, at a server, target patient data via a connected device; at the server, one or more risk values associated with the target patient data are estimated using one or more risk assessment models. The target patient data may include historical data, patient population level data, and real-time data. The risk assessment model may include one or more of the following: a historical data risk assessment model, a real-time data risk assessment model or a combined risk assessment model. Methods for training such models are described. The described systems and methods preferably enable continuous patient monitoring based on patient's historical data and continuous real-time physiological data.

The invention relates to the technical field of medical information systems, in particular to an intelligent gastrointestinal tract postoperative complication early warning system and a method thereof.The intelligent gastrointestinal tract postoperative complication early warning system comprises a data acquisition module, a data preprocessing module, a deep learning model module in communication connection with the data acquisition module, and a knowledge distillation module in communication connection with the data preprocessing module; the expert knowledge fusion module is in communication connection with the deep learning model module and is used for fusing expert knowledge into a mixed deep learning model; generating a lightweight prediction model; the dynamic weight adjustment module is in communication connection with the deep learning model module and the knowledge distillation module; the multi-task learning module is in communication connection with the deep learning model module; the early warning output module is in communication connection with the knowledge distillation module and the multi-task learning module and is used for generating complication risk early warning based on an output result of the lightweight prediction model; an early warning result and interpretive analysis are displayed through a visual interface, so that the early warning accuracy is improved, and more comprehensive patient state evaluation is provided for doctors.

The invention relates to the technical field of medical clinical auxiliary diagnosis, in particular to an orthopedic postoperative complication early detection and early warning system based on data analysis, which comprises a multi-modal sign synchronous acquisition module, a sign dynamic difference quantification module, a complication feature association module, a grading early warning decision module and a dissection calibration visualization module. Wherein the multi-modal sign synchronous acquisition module is used for acquiring postoperative data of a patient; calculating a difference quantization parameter; the complication characteristic association module is used for identifying associated risk characteristics of the osteofascia ventricular syndrome, the deep venous thrombosis and the fat embolism; and the grading early warning decision module is used for generating a three-level early warning instruction based on the associated risk characteristics. According to the system and the method, through a multi-modal sign synchronous acquisition, difference quantification and grading early warning linkage mechanism, accurate recognition, risk grade division and visual intervention guidance of postoperative complications are realized, and the continuity of postoperative monitoring and the timeliness of early warning are improved.

The invention relates to a bioelectronic surgical suture for suture tension monitoring, the surgical suture comprises a cannula and a conductive material, the cannula is a hollow capillary tube, and the conductive material is arranged in the cannula through perfusion and is subjected to first laser scanning treatment after perfusion; a micro-nano structure is manufactured on the outer wall of the sleeve through second laser scanning. According to the principle of friction nanometer power generation, the tension of the surgical suture causes the change of the contact area between the suture and tissue and the change of an output electric signal in the collection process, the tension of the surgical suture can be reversely deduced, and the purpose of monitoring the surgical suture tension is achieved. The operation line is a self-powered sensor, and an external power supply is not needed. The operation line can provide different quantitative feedbacks according to different suturing modes and suturing parts, and is combined with clinical suturing experience of doctors to assist in establishing a tension quantitative model, so that the doctors are helped to optimize operation procedures, postoperative complications are reduced, and wound healing is promoted.

The invention relates to the technical field of image recognition, in particular to a lung cancer postoperative care-oriented intelligent image auxiliary guidance system, which is characterized in that real change information values of lung clusters are determined by acquiring lung CT images of a lung cancer patient at each sampling moment after operation, so that suspected infected lung clusters are screened out, and the lung cancer postoperative care-oriented intelligent image auxiliary guidance system is obtained. And determining regularity of a lung cluster region boundary, adjusting the actual change information value according to the regularity, and determining an actual change result of a suspected infected lung cluster so as to determine the infection rate of the lung cancer patient at each sampling moment, thereby obtaining diffusion attributes of the lung cancer patient and giving different levels of nursing guidance suggestions to the lung cancer patient. By analyzing the lung CT images of the patient in different periods, the corresponding infection prediction diffusion results and the corresponding nursing guidance suggestions are obtained, the risk of postoperative complications is reduced, and the nursing accuracy is improved.

The invention discloses an integrated management system and method for postoperative care of orthopedic patients, and relates to the technical field of medical treatment. Comprising a bone mineral density dynamic monitoring value, six-dimensional mechanical parameters of a joint movement track, emotion spectrum characteristics in voice interaction, microenvironment temperature and humidity changes collected by wearable equipment and skin surface pressure distribution; inputting the recovery data into a multi-modal decision-making model based on orthopedic biomechanics improvement, and generating a nursing scheme including a skeleton stress safety interval, a virtual rehabilitation scene dynamic parameter and an interdisciplinary intervention priority; the scheme parameters are dynamically optimized through a layered reinforcement learning framework special for the orthopedics department, and the deformation logic of the 3D printing brace and the correction strategy of the nursing robot are synchronously controlled; based on the postoperative complication space-time prediction map, a multidisciplinary collaborative response instruction strictly matched with the rehabilitation stage of the patient is triggered.

The invention relates to the technical field of medical treatment, in particular to an intelligent general surgery preoperative risk assessment system which comprises a data acquisition module, a data fusion and processing module, a feature extraction module, a risk assessment module, a prediction and optimization module and a visual display module. The data fusion and processing module adopts a multi-modal data fusion algorithm to generate a patient comprehensive information model, the feature extraction module extracts cardiovascular health status, renal function and tumor marker key features, and the risk assessment module constructs a preoperative risk assessment model to generate a preoperative risk score; the prediction and optimization module predicts the risk of postoperative complications, and the visual display module displays an evaluation result and a prediction result in real time through a graphical interface; according to the method, the accuracy and scientificity of preoperative risk assessment and postoperative complication prediction are remarkably improved, and reliable support is provided for postoperative management of patients.

The invention discloses a thoracic surgery postoperative complication assessment method and system based on multi-dimensional analysis, and relates to the technical field of thoracic surgery postoperative complication. Key sign signals such as heart rate fluctuation amplitude Cxv, respiratory rhythm drift coefficient Chx, pulse waveform disturbance degree Cmd and epidermal temperature change rate Cbt of a postoperative patient are continuously collected and denoised through a postoperative sign data acquisition module, and a structured sign data set TW is formed. By means of the method, tiny physiological fluctuation of a postoperative patient within 24 hours can be captured, early risk signals are prevented from being neglected, and the time sensitivity of complication recognition is improved from the source. A dynamic sliding window regression technology is introduced to carry out trend modeling on the sign data, so that a miniature instability section can be accurately extracted; through the improvement, the system can capture precursor signals of potential infection and bleeding earlier, and the possibility of early intervention is improved.

The invention discloses a blood flow maintaining device, an aorta covered stent system and a windowing method of an aorta covered stent, the blood flow maintaining device comprises a conveying sheath and a blood flow maintaining net basket, the conveying sheath comprises a sheath tube, and the blood flow maintaining net basket comprises a net basket and a pushing assembly; the net basket has an unfolded state and a folded state; the pushing assembly is connected with the net basket, and the pushing assembly is used for pushing the net basket in the sheathing canal; when the mesh basket is in an unfolded state after being released outwards from the far-end opening of the sheath tube, the mesh basket is suitable for supporting the aorta covered stent and forming a covered stent external blood flow channel on the outer side of the aorta covered stent. When the blood flow maintaining net basket is applied to an aortic arch intracavity isolation treatment operation, the net basket can ensure that the blood flow of each branch is not interrupted before branch windowing is completed, the requirements for hospital conditions and the operation level of an operator are reduced, the operation time is shortened, meanwhile, postoperative complications are reduced, and operation popularization is facilitated.

The invention relates to the field of medical instruments, in particular to a nail anvil device and an anastomat, the nail anvil device comprises a nail anvil main body, the nail anvil main body comprises a nail anvil head and a nail anvil rod arranged on the nail anvil head, the nail anvil head is provided with a nail anvil surface with a plurality of nail pits, and the axis of the nail anvil rod is perpendicular to the nail anvil surface; the intestinal tract protection assembly is located on the side, away from the nail anvil face, of the nail anvil head and comprises a similar casing and a clamp, the similar casing is loaded on the nail anvil body, the similar casing is tubular, and the clamp can clamp the similar casing so that the similar casing can be fixed in the intestinal tract. According to the nail anvil device with the intestinal tract protection assembly, the intestinal tract protection assembly is placed in the intestine in the anastomotic operation process, the operation time is shortened, the operation process is simplified, and postoperative complications such as anastomotic leakage are reduced.

The invention discloses a TAVR postoperative blood flow parameter complementing method based on CT-MRI fusion, and belongs to the technical field of crossing of medical image processing and computational fluid mechanics. A CT valve frame registration technology is adopted, an invisible artificial valve leaflet three-dimensional structure is reconstructed through valve frame information in a CT image and is coupled with a dynamic heart model, a complete valve mechanical and functional simulation platform is constructed, the problem of an imaging blind area is solved, hemodynamic parameters of an inlet and an outlet of a postoperative area are further collected in combination with 4D Flow MRI, and the artificial valve leaflet three-dimensional structure is obtained. The blood flow conditions of the upstream and the downstream of the artificial valve area at each stage of contraction and relaxation of the TAVR postoperative part are comprehensively restored, the temporal-spatial resolution and the physical authenticity of the boundary condition in fluid-solid coupling simulation are greatly improved, and the more real passive response of the valve leaflet under the action of the blood flow is obtained; and a reliable basis is provided for quantitative and mechanical risk analysis of postoperative complications such as thrombosis, perivalvular leakage and valve leaflet fatigue.

The invention discloses a thoracic surgery postoperative drainage monitoring data processing method and a thoracic surgery postoperative drainage monitoring data processing system. The method comprises the following steps: synchronously acquiring thoracic respiration dynamic characteristics of a patient and fluid dynamic characteristics of a thoracic closed drainage system through a data acquisition module; the central processing server establishes a time sequence mapping relationship between the two, performs feature fusion analysis, and calculates to obtain a thoracic cavity-pipeline coupling efficiency index; performing logic judgment on the postoperative recovery state of the patient on the basis of the index, and identifying a chest-pipeline fluid transmission state including breathing work limitation, drainage channel blockage and pleural cavity air leakage; and dynamically generating a grading equipment response strategy including adjusting training intensity and triggering pipeline maintenance early warning or analgesia intervention according to an identification result. According to the method, accurate recognition and closed-loop management of postoperative complications are achieved by quantitatively evaluating the linkage relation between respiration driving and drainage response.

The invention discloses a device and method for simulating eyelid scraping and intraocular pressure loading and monitoring suture tension, and relates to the technical field of medical instrument testing, the device comprises an eyeball environment simulation part and a suture cornea loading part, the suture cornea loading part comprises a scraping assembly and a transmission assembly, the eyeball environment simulation part comprises an intraocular pressure bubbling instrument, a hydraulic pipe, a T-shaped pipe and a tear pipe, the tear pipe is communicated with the scraping assembly, an opening in the lower end of one side of the T-shaped pipe is connected with the intraocular pressure bubbling instrument through the hydraulic pipe, and an opening in the lower end of the other side of the T-shaped pipe is provided with a hydraulic sensor. According to the device and method for simulating eyelid scraping, intraocular pressure loading and suture tension monitoring, the uniformity of cornea suture is achieved, secondary cornea damage caused by uneven suture is reduced, the risk of postoperative complications is reduced, and an experimental basis is provided for research and development of cornea substitutes.

The invention discloses a chest surgery postoperative complication early warning system. The chest surgery postoperative complication early warning system comprises a data acquisition layer, a data transmission layer, a data processing and storage layer, an analysis and early warning layer and a user interface and feedback layer. The data acquisition layer is used for acquiring vital signs of a patient, laboratory inspection, image data and information manually input by medical staff; the data transmission layer transmits the collected data to the data processing and storage layer in real time; the data processing and storage layer preprocesses, stores and manages the collected data; the analysis and early warning layer analyzes the data through a predefined rule and a machine learning model, calculates a risk score in real time, and triggers early warning; and the user interface and feedback layer provides a graphical operation interface, mobile terminal pushing and alarm feedback. According to the invention, the change of the condition of the patient after the thoracic surgery can be accurately monitored in real time, the risk of complication can be early warned in time, decision support is provided for medical personnel, and the postoperative recovery effect of the patient is improved.

The puncturing, flushing and suturing assembly comprises a puncture outfit, an expander, a flusher and a suturing instrument, the puncture outfit comprises a sheath shell and a puncture sheath needle which can be separated from the sheath shell, the sheath shell comprises a cylindrical puncture cylinder, and a hollow channel is axially formed in the puncture cylinder; the dilator comprises an elastic balloon, and the elastic balloon can extend out of the puncture end of the hollow channel; the flusher comprises a flushing pipe extending in the axial direction. The stitching instrument comprises a main body part, the insertion end of the main body part is provided with a rotating piece capable of being unfolded towards the outer side, and the outer circumferential face of the main body part is provided with a puncture groove for a stitching needle to be inserted. The puncture outfit is designed into the sheath shell and the puncture sheath needle which can be separated, the puncture sheath needle is taken down after puncture, and the hollow channel of the sheath shell is used as an access channel for an expander, a flusher, a stitching instrument, an endoscope and surgical instruments, so that different instruments are prevented from being repeatedly inserted into a skin incision in the surgical process; and the risk of postoperative complications such as wound bleeding and incisional hernia is reduced.

The invention provides a thoracic surgery respiratory training system and method, and the method comprises the steps: constructing a respiratory-physiological feature tensor through the time sequence change of a respiratory airflow parameter during respiratory training in combination with the fluctuation feature of blood oxygen saturation in the respiratory training process, and carrying out the space-time convolution fusion of the respiratory-physiological feature tensor and a chest respiratory movement track, obtaining a respiratory efficiency index; constructing a taboo risk vector based on the risk relationship map of the thoracic surgery postoperative complications and the electronic medical record of the target user, and generating a complication risk entropy of the target user under the current respiratory training according to the taboo risk vector in combination with the pain tolerance physiological data; and according to the respiratory efficiency index and the complication risk entropy, determining a dynamic adaptation margin of the target user to current respiratory training, and based on the dynamic adaptation margin, dynamically adjusting the training resistance of the self-adaptive resistance adjustment respiratory training device. By the adoption of the scheme, a multi-dimensional respiratory training feedback mechanism can be constructed to conduct self-adaptive adjustment on the training resistance.

The invention relates to the technical field of medical information, and discloses a chest surgery postoperative complication early warning system which comprises a screening module and an early warning module. The screening module is used for performing multi-dimensional feature integration on the initial physiological data, and covering vital sign feature data (such as heart rate variability parameter grouping tags and blood pressure fluctuation parameter clustering tags), medical record feature data and postoperative monitoring feature data; and the early warning module performs dynamic noise filtering on the real-time physiological data stream, identifies an abnormal mode through a multi-source fusion layer, and generates a complication early warning instruction. The multi-source fusion layer comprises a data normalization module and a feature recombination module, and the feature recombination module realizes multi-dimensional weight fusion and dynamic threshold optimization based on the physiological parameter analysis layer, the risk self-adaption layer and the complication trigger layer. Through space-time correlation analysis, compensation decision matrix modeling and high-risk scene positioning, the real-time performance and accuracy of complication early warning are remarkably improved, and the method is suitable for multi-parameter cooperative monitoring of thoracic surgery postoperative patients.

The invention relates to the technical field of medical information analysis, in particular to a thoracic surgery postoperative complication assessment method and system based on multi-dimensional analysis, and the method comprises the following steps: S1, obtaining a postoperative multi-dimensional monitoring data set; s2, performing standardized preprocessing on the multi-dimensional monitoring data set to generate standardized monitoring data; s3, classifying and integrating the standardized monitoring data into physiological function data, drainage feature data and motion perception data according to data types; s4, chest dynamic pressure change analysis is carried out, and pressure gradient change parameters are calculated; s5, obtaining an abnormal metabolite ratio index; and S6, generating a complication risk assessment report. According to the method, through standardized integration and weighted analysis of the multi-dimensional postoperative monitoring data, accurate quantitative evaluation and graded output of the thoracic surgery postoperative complication risk are realized, and the reliability of early warning and the timeliness of clinical intervention are improved.

The invention relates to the technical field of medical information processing, in particular to an intelligent prediction system and method for postoperative complications of anesthetized patients, and the system comprises a data collection module, a difficult case-sensitive preprocessing module, a double-level prediction module, a parameter adaptive optimization module, a multi-modal feature fusion module and a time sequence-sensitive risk assessment module. The data acquisition module is responsible for collecting intraoperative and personal information of anesthesia patients, the difficult case sensitive preprocessing module preprocesses data, identifies samples difficult to predict and allocates high weights, and the double-level prediction module comprises a basic prediction unit and a fine adjustment unit and is used for performing preliminary feature extraction, risk assessment and prediction adjustment. The parameter self-adaptive optimization module monitors performance indexes and dynamically adjusts network parameters, the multi-modal feature fusion module fuses different types of data features, the time sequence sensitive risk assessment module divides the anesthesia process, dynamically captures risk changes and generates the risk probability of postoperative complications, and the prediction sensitivity of high-risk postoperative complications is improved.

The invention belongs to the technical field of lung tissue lesion positioning, and particularly relates to a lung tissue lesion position positioning system and method based on three-dimensional reconstruction. According to the method, overall and local two-stage registration is combined, whole-lung expansion errors are corrected, local deformation is compensated, the lesion position and image alignment is achieved, the three-dimensional lesion position is displayed through the augmented reality technology, doctors are helped to visually master spatial distribution of the focus in the body of a patient, the safety and the success rate of operation or needle biopsy are improved, and the operation efficiency is improved. Based on a breathing model and an elastic registration method, the method can adapt to lung compliance differences and breathing movement conditions of different patients, is particularly suitable for severe or old patients difficult to keep stable breathing, reduces the risk of blind penetration or excessive resection through accurate positioning, and reduces tissue damage and the occurrence rate of postoperative complications.

The invention discloses a gynecological general anesthesia postoperative complication prediction modeling method based on pathological data analysis, and relates to the technical field of postoperative complication prediction, and the method comprises the following steps: collecting preoperative, intraoperative and postoperative multi-dimensional pathological and clinical data of gynecological general anesthesia; establishing a space-time incidence matrix of anxiety psychology, intraoperative pathology and complications; and constructing a gynecological general anesthesia postoperative complication prediction model based on a multi-modal deep learning prediction architecture based on the space-time incidence matrix. Through combination of multi-dimensional data fusion, dynamic correlation analysis and deep learning, transformation from an empirical decision to a data-driven decision is realized. The time-space incidence matrix provides standardized risk assessment logic for doctors; the prediction capability of the model on rare complications fills the prediction blank in a small sample scene. Finally, through real-time risk early warning and individualized intervention suggestions, the safety of the gynecological general anesthesia operation is remarkably improved, the postoperative recovery time of a patient is shortened, and the method has important clinical transformation value.

The invention relates to a postoperative complication prediction method and device based on a two-way map neural network, and the method comprises the steps: collecting ICG video images before and after cerebrovascular bypass surgery, and obtaining the clinical feature data of a patient; preprocessing the collected ICG video image, extracting a blood vessel network by adopting an image segmentation algorithm, and generating corresponding blood vessel graph structures before and after bridging; extracting blood vessel features, blood flow dynamic features and clinical features from the collected ICG video images and clinical feature data, and constructing a multi-modal high-dimensional feature set; and taking the multi-modal high-dimensional feature set and the corresponding vascular graph structures before and after bridging as input, and generating a complication prediction score through a pre-trained prediction model based on a two-way graph neural network. Compared with the prior art, the method has the advantages that the multi-modal feature information is fused, the graph neural network structure is optimized, the model performance is stable, the accuracy is high, and the method has important clinical application value.

The invention relates to the technical field of medical informatization and intelligent nursing, in particular to a general surgery department patient postoperative complication risk prediction and nursing decision-making system, which comprises a baseline manifold construction module for initializing an individualized steady state manifold in a preset multi-dimensional state space based on historical operation parameters and basic state data; the perturbation response topology evaluation module is used for calculating a recovery attenuation coefficient after the real-time phase trajectory deviates from the individualized steady state manifold so as to generate a steady state dissipation index and a growth rate; the offset course quantity decoding module is used for adjusting a dynamic threshold value based on a time delay parameter and an embedded dimension parameter on the basis of the basic state data; the risk prediction module is used for weighting, merging and normalizing the growth rate of the steady-state dissipation index and the modulus length of the course deviation vector to generate a risk prediction evaluation result; the decision closed-loop module is used as a secondary perturbation variable and sends the secondary perturbation variable to the perturbation response topology evaluation module for continuous monitoring; according to the invention, the baseline anchoring precision in different clinical scenes is improved.

The utility model provides an endoscope puncture needle with biopsy and ablation functions. The endoscope puncture needle comprises an operation main body, a biopsy needle, a catheter mechanism and a forceps channel fixer, the operation body comprises a limiting handle and a pushing rod, the limiting handle is provided with a first end and a second end which are opposite, one end of the pushing rod movably penetrates through the limiting handle through the first end, and the other end of the pushing rod is provided with a connecting part used for being externally connected with a negative pressure device or an electrode device; a needle inserting depth limiting mechanism matched with the push rod is arranged in the limiting handle, one end of the catheter mechanism is arranged in the limiting handle in a penetrating mode through a second end and communicated with the push rod, and the other end of the catheter mechanism is communicated with the biopsy needle; the forceps channel fixator is movably arranged on the catheter mechanism in a sleeving mode and used for fixing the catheter mechanism and a forceps channel opening of the endoscope. According to the utility model, the problem that the risk of postoperative complications of a patient is increased due to the increase of the number of times of puncture because the needle biopsy procedure and the ablation procedure need to be operated separately and punctured for multiple times in the existing medical procedure needing needle biopsy and interventional ablation treatment can be solved.

The invention relates to a construction method of a model for preoperative evaluation of postoperative complications of hepatic resection. Performing high-order interactive modeling on the plurality of preoperative characteristic variables, and identifying the potential influence of the multi-factor joint effect on the postoperative complications; comprising the interaction between hepatic insufficiency and diabetes and the relationship between preoperative anemia and malnutrition; using a decision tree model to screen out features matched with the complication risk by calculating each variable and interaction item; for the modeling of the nonlinear relationship between the variables, capturing the nonlinear relationship between the variables by adopting a support vector machine (SVM) method; the prediction of the increase of the preoperative liver hardness is in a non-linear relationship with the risk of postoperative complications; constructing a multi-level regression model including hierarchical Bayesian regression, performing risk assessment on different levels including an individual level and a group level, and integrating individual differences and group features of patients; and each layer of model independently evaluates different risk factors, and integrates data of different layers for prediction.