Gynecological tumor postoperative management method and system

Abstract

The invention discloses a postoperative management method and system for gynecological tumors. The method comprises the following steps: acquiring physical sign monitoring data of a postoperative patient within a preset duration, wherein the physical sign monitoring data comprises bleeding parameters; determining a bleeding risk assessment result of the postoperative patient based on the bleeding parameters, and determining an incision management strategy of the postoperative patient based on the bleeding risk assessment result and the physical sign monitoring data; in the process of executing the incision management strategy, acquiring multi-dimensional vital sign data of the postoperative patient; potential complications of the postoperative patient are determined based on the multi-dimensional vital sign data, a postoperative management strategy is generated for the potential complications, and home nursing guidance corresponding to the postoperative management strategy is generated. The rehabilitation quality of gynecological tumor postoperative patients can be effectively improved, complications are reduced, and rapid recovery of the patients is promoted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of postoperative management, in particular to a postoperative management method and system for gynecological tumors. BACKGROUND

[0002] Postoperative management of gynecological tumors is a key link to ensure patient recovery, covering comprehensive measures such as vital sign monitoring, pain management, drainage tube nursing, early activity guidance, and complication prevention. Scientific and standardized management can effectively reduce the risk of infection, thrombosis, and promote functional recovery.

[0003] However, the existing postoperative management system still has significant deficiencies. On the one hand, the degree of refinement of incision management during hospitalization is insufficient, and there is a lack of dynamic adjustment scheme based on wound assessment. On the other hand, there is a serious lack of home nursing guidance after discharge, and patients often face poor incision healing and delayed recognition of abnormal symptoms due to the lack of professional guidance. SUMMARY

[0004] To solve the above technical problems, the embodiments of the present application provide a postoperative management method and system for gynecological tumors.

[0005] According to an aspect of an embodiment of the present application, a postoperative management method for gynecological tumors is provided, comprising: acquiring vital sign monitoring data of a postoperative patient within a preset time length, the vital sign monitoring data including a bleeding parameter; determining a bleeding risk assessment result of the postoperative patient based on the bleeding parameter, determining an incision management strategy for the postoperative patient based on the bleeding risk assessment result and the vital sign monitoring data; acquiring multi-dimensional vital sign data of the postoperative patient during the execution of the incision management strategy; determining potential complications of the postoperative patient based on the multi-dimensional vital sign data, generating a postoperative management strategy for the potential complications, and generating home nursing guidance corresponding to the postoperative management strategy.

[0006] According to an aspect of an embodiment of the present application, the determination of potential complications of the postoperative patient based on the multi-dimensional vital sign data comprises: inputting the multi-dimensional vital sign data into a trained neural network model for time series analysis to obtain a feature time series analysis result; determining a composite risk index of the postoperative patient based on the vital sign time series analysis result, the composite risk index including risk indices of the postoperative patient in multiple aspects, and determining potential complications of the patient based on the composite risk index.

[0007] According to an aspect of the embodiments of the present application, the method further comprises: triggering an infection warning if the sign time series analysis result represents that the inflammation marker exceeds the threshold value in the continuous multiple monitoring periods; determining a correlation analysis result between the heart rate variability and the blood oxygen saturation of the postoperative patient based on the sign time series analysis result, determining a thrombosis risk of the postoperative patient based on the correlation analysis result; comprehensively evaluating a composite risk index of bleeding, infection and thrombosis by a random forest algorithm, and determining a potential complication of the postoperative patient based on the composite risk index.

[0008] According to an aspect of the embodiments of the present application, the determination of the incision management strategy of the postoperative patient based on the bleeding risk assessment result and the sign monitoring data comprises: if the bleeding risk assessment result represents that the bleeding risk level of the postoperative patient is level one, a routine disinfection and compression bandaging strategy is adopted; if the bleeding risk assessment result represents that the bleeding risk level of the postoperative patient is level two, the application of hemostatic sponge is increased, and the frequency of incision evaluation is increased; if the bleeding risk assessment result represents that the bleeding risk level of the postoperative patient is level three, a strengthened management scheme including negative pressure drainage and local application of thrombin is started, wherein the evaluation of the bleeding risk level is related to the body temperature of the postoperative patient.

[0009] According to an aspect of the embodiments of the present application, the method further comprises: acquiring an incision image of the postoperative patient, and performing visual analysis on the incision image to determine red swelling proportion data corresponding to the incision and color characteristics of exudate of the incision according to the visual analysis result; acquiring pressure data of the tissue around the incision of the postoperative patient, and determining tension change data around the incision based on the pressure data; determining the incision management strategy of the patient based on the red swelling proportion data, the color characteristics of the exudate and the tension change data.

[0010] According to an aspect of the embodiments of the present application, the generation of the postoperative management strategy for the potential complication comprises: if the potential complication represents that the postoperative patient has an infection risk, an anti-infection scheme including an antibiotic selection tree and a timing of incision drainage is generated; if the potential complication represents that the postoperative patient has a bleeding risk, a hemostatic agent stepwise medication plan including coagulation function detection is generated; if the potential complication represents that the postoperative patient has a thrombosis risk, a prevention scheme including the use time length of lower limb compression device and the dose of anticoagulant drug is generated.

[0011] According to an aspect of the embodiment of the present application, the method further comprises: obtaining incision secretion of the postoperative patient, and culturing the incision secretion, and dynamically adjusting an anti-infection scheme including an antibiotic selection tree and an incision drainage timing based on a secretion culture result; determining a body temperature curve of the postoperative patient, and determining a use timing of an antipyretic and analgesic drug of the postoperative patient; and generating a multi-department consultation request if an infection index of the postoperative patient does not improve within a preset time length.

[0012] According to an aspect of the embodiment of the present application, the generating of the home nursing guidance corresponding to the postoperative management strategy comprises: determining a target medical resource map based on position information of the postoperative patient, and generating an emergency treatment video based on the vital sign data of the postoperative patient; configuring a reminding program of an intelligent medicine box based on a medication plan of the postoperative patient, and making an AR teaching video including a self-check gesture recognition of an incision; and generating the home nursing guidance corresponding to the postoperative management based on the target medical resource map, the reminding program, the emergency treatment video and the AR teaching video.

[0013] According to an aspect of the embodiment of the present application, the method further comprises: generating an augmented reality guide for a corresponding home nursing staff based on the AR teaching video, and correcting an operation posture of the home nursing staff based on the augmented reality guide; obtaining real-time state information of the postoperative patient based on a voice interaction pain assessment question and answer system, and triggering a vibration warning and displaying a standard operation comparison map if the real-time state information represents an incorrect nursing action.

[0014] According to an aspect of the above-mentioned embodiment, a postoperative management system for gynecological tumors is provided, and the system comprises a processor, an input device, an output device and a memory, which are connected to each other, wherein the memory is used to store a computer program, the computer program comprises program instructions, the processor is configured to invoke the program instructions, and the postoperative management method for gynecological tumors is executed.

[0015] In the technical solutions provided in the embodiments of the present application, by obtaining the sign monitoring data of the postoperative patient within a preset time length and containing the bleeding parameter, the bleeding risk assessment result of the postoperative patient can be accurately determined based on the bleeding parameter, and then the incision management strategy that fits the actual condition of the patient is formulated according to the result and the sign monitoring data, which helps to manage the incision in a targeted and refined manner, and reduces the probability of problems of the incision due to improper management; the multi-dimensional vital sign data of the postoperative patient is obtained in the process of executing the incision management strategy, and the potential complications are determined according to the multi-dimensional vital sign data, and the postoperative management strategy and the corresponding home nursing guidance are generated for the potential complications, so that the patient can also be nursed at home according to the professional guidance after being discharged from the hospital, and the possible complications can be prevented and coped with in advance, and the overall scheme forms a coherent and comprehensive management system from the incision management in the early postoperative period to the home nursing after being discharged from the hospital, which effectively improves the rehabilitation quality of the postoperative patient with gynecological tumor, reduces the occurrence of complications, and promotes the rapid recovery of the patient.

[0016] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0017] The drawings incorporated into the specification and forming part of the specification, show embodiments consistent with the present application, and together with the specification, serve to explain the principles of the present application. It is obvious that the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings from these drawings without creative labor. In the drawings: Figure 1 is a flowchart of a postoperative management method of gynecological tumor according to an exemplary embodiment of the present application; Figure 2 is a flowchart of a postoperative management method of gynecological tumor according to another exemplary embodiment of the present application; Figure 3 is a flowchart of a postoperative management method of gynecological tumor according to another exemplary embodiment of the present application; Figure 4 is a flowchart of a postoperative management method of gynecological tumor according to another exemplary embodiment of the present application; Figure 5 is a flowchart of a postoperative management method of gynecological tumor according to another exemplary embodiment of the present application; Figure 6 is a flowchart of a postoperative management method of gynecological tumor according to another exemplary embodiment of the present application; Figure 7 is a flowchart of a postoperative management method of gynecological tumor according to another exemplary embodiment of the present application; Figure 8 is a flowchart of a postoperative management method of a gynecological tumor according to another exemplary embodiment of the present application; Figure 9 is a flowchart of a postoperative management method of a gynecological tumor according to another exemplary embodiment of the present application; Figure 10 is a block diagram of a postoperative management system of a gynecological tumor according to an exemplary embodiment of the present application; Figure 11 shows a structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application. DETAILED DESCRIPTION

[0018] The exemplary embodiments will be described in detail with reference to the accompanying drawings. In the following description, same drawing reference numerals are used for the same elements across different several drawings. The following exemplary embodiments are not representative of all the embodiments consistent with the present application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0019] The block diagrams shown in the accompanying drawings are merely functional entities, and do not necessarily have to correspond to physically independent entities. That is, the functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0020] The flowcharts shown in the accompanying drawings are merely exemplary illustrations, and do not necessarily include all the contents and operations / steps, nor do they have to be executed in the described order. For example, some operations / steps can be further divided, and some operations / steps can be combined or partially combined, so that the actual execution order can be changed depending on the actual situation.

[0021] In the present application, "a plurality of" means two or more. The "and / or" describes the association between the associated objects, and means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone, among the three cases. The character " / " generally means that the associated objects before and after are in an "or" relationship.

[0022] First of all, it needs to be pointed out that in this critical stage after gynecological tumor surgery, scientific and refined management plays a decisive role in the recovery of patients. In the early postoperative period, medical staff will closely monitor the patient's vital signs, including heart rate, blood pressure, respiratory rate and body temperature, to ensure that they remain within a stable and normal range, so that they can promptly detect and handle possible emergencies such as bleeding, shock, etc. At the same time, incision management is the top priority of postoperative care, and medical staff will closely observe the incision for signs of bleeding, exudation, swelling, increased pain, etc. According to the specific situation of the incision, such as bleeding parameters, accurately assess the risk of bleeding, and then develop individualized incision management strategies, including reasonable selection of dressing change time, use of appropriate dressings, control of infection risk, etc. to promote good healing of the incision and reduce the risk of complications such as infection and dehiscence. In terms of pain management, given that postoperative pain not only causes physical discomfort to patients, but also affects their breathing, coughing, movement, etc., thereby increasing the risk of complications such as pulmonary infection and deep vein thrombosis, medical staff will use a multi-modal analgesic regimen that combines drug analgesia and non-drug analgesia methods, such as using analgesic pumps, providing psychological comfort, guiding patients to perform relaxation training, etc. to effectively relieve patients' pain and improve their comfort.

[0023] On the other hand, by obtaining the patient's multidimensional vital sign data after discharge, such as daily body temperature, blood pressure measurements, and observing for signs of abnormal vaginal bleeding, abdominal pain, abdominal distension, etc., potential signs of complications can be detected in a timely manner, and comprehensive and targeted postoperative management strategies can be generated for these potential complications, while being converted into easy-to-understand and highly operational home care guidance, covering aspects such as dietary adjustments, exercise recommendations, symptom monitoring methods, follow-up reminders, etc. so that patients and their families can also scientifically and normatively care for them at home, ensuring that patients continue to receive continuous and effective medical support after discharge, further improving the rehabilitation effect and quality of life of patients after gynecological tumor surgery, and helping them better return to normal life.

[0024] However, the existing postoperative management system still has significant shortcomings, on the one hand, the degree of refinement of incision management during hospitalization is insufficient, and there is a lack of dynamic adjustment scheme based on wound assessment, on the other hand, there is a serious lack of home care guidance after discharge, patients often face the difficulties of poor incision healing and delayed recognition of abnormal symptoms due to the lack of professional guidance.

[0025] In order to solve the above problems, the present application proposes a postoperative management method and system for gynecological malignant tumors, an electronic device, a computer readable storage medium and a computer program product, which will be described in detail below.

[0026] Please refer to Figure 1 , Figure 1is a flowchart of a postoperative management method for gynecological tumors according to an exemplary embodiment of the present application, which is shown in Figure 1 The postoperative management method for gynecological tumors includes at least steps S110-S140, which are described in detail as follows: In step S110, the body sign monitoring data of the postoperative patient within a preset time length is acquired, and the body sign monitoring data includes a bleeding parameter.

[0027] For example, a dressing with a pressure sensor can be used at the incision site of the patient. This dressing can monitor the bleeding at the incision site in real time and convert the bleeding parameters such as the amount of bleeding and the bleeding speed into electrical signals. At the same time, a drainage tube can be left in the patient's body, which is connected to a monitoring device with a flow sensor for accurately measuring the blood content in the drainage fluid, thereby obtaining key data related to bleeding. In the ward environment, a multifunctional monitor can be installed, which can continuously monitor the patient's heart rate, blood pressure, respiratory rate and other vital signs. These data are correlated with the bleeding parameters to provide a basis for comprehensive assessment of the patient's postoperative state. The collection of monitoring data is a continuous process. Within a preset time length, these monitoring devices will automatically collect data at preset time intervals. In units of minutes, the pressure sensor on the dressing will continuously sense the pressure changes at the incision site, and once there is bleeding, the pressure change information will be transmitted to the data collection terminal in real time. The drainage tube monitoring device also reads the blood content data in the drainage fluid at regular intervals. The multifunctional monitor continuously collects the patient's heart rate, blood pressure and other data, and transmits all collected data to the central data processing system through wired or wireless means. During data transmission, encryption technology and data verification mechanism are used to ensure the accuracy and security of the data. Encryption technology can prevent data from being illegally obtained or tampered with during transmission, and the data verification mechanism can detect whether errors occur during data transmission. Once an error is found, the system will automatically require retransmission of the data to ensure the integrity of the data. In addition, the corresponding central data processing system receives the data and performs a series of processing and analysis. First, the bleeding parameters are processed by a special algorithm, for example, according to the change trend of the blood content in the drainage fluid, combined with the data of the dressing pressure sensor, to determine whether the bleeding is within the normal range or there is an abnormal bleeding risk. At the same time, the bleeding parameters are analyzed in association with other vital sign data, such as when the heart rate increases and the blood pressure decreases, combined with the change of the bleeding parameters, the overall health status of the patient is comprehensively evaluated.

[0028] In step S120, the bleeding risk assessment result of the postoperative patient is determined based on the bleeding parameter, and the incision management strategy of the postoperative patient is determined based on the bleeding risk assessment result and the body sign monitoring data.

[0029] For example, after surgery, medical staff can obtain bleeding parameters in various ways, such as using dressings with pressure sensors to monitor bleeding at the incision site, changes in pressure of which can reflect the amount and speed of bleeding; connecting a drainage tube to a flow sensor to accurately measure the blood content in the drainage fluid; at the same time, combining with the vital sign data such as heart rate and blood pressure monitored by the multifunctional monitor, these data together constitute the basic information for bleeding risk assessment. Taking the blood content in the drainage fluid as an example, if the blood content in the drainage fluid increases sharply within a short time and exceeds the pre-set normal threshold, and at the same time the dressing pressure sensor shows that the pressure at the incision site continues to abnormally rise, it may indicate that the incision has an active bleeding risk. At this time, referring to the patient's vital signs such as heart rate and blood pressure, if the heart rate increases and the blood pressure decreases, it further supports the judgment that bleeding may cause serious consequences such as shock, so as to assess the bleeding risk as high risk. Conversely, if the blood content in the drainage fluid fluctuates within the normal range, the dressing pressure sensor shows that the pressure at the incision site is stable, and the patient's vital signs are stable, then the bleeding risk is assessed as low risk. After obtaining the bleeding risk assessment result, the incision management strategy is determined in combination with comprehensive sign monitoring data. For patients with high risk of bleeding, the incision management strategy will be more strict and active. In terms of wound care, pressure bandaging method will be used, using elastic bandage or special pressure dressing to reduce bleeding at the incision site, and at the same time closely observing whether the dressing is soaked with blood and replacing the soaked dressing in time.

[0030] In terms of drug use, hemostatic drugs such as vitamin K, tranexamic acid, etc. will be considered for use according to the cause and degree of bleeding to promote blood coagulation and reduce bleeding. In addition, the monitoring frequency of the patient's vital signs will be increased, such as measuring blood pressure and heart rate every half hour, and closely observing the changes in the color and temperature of the skin around the incision to discover and handle possible complications in time. For patients with low risk of bleeding, the incision management strategy is relatively relaxed but still needs to be vigilant, and in terms of wound care, the operation is carried out according to the regular dressing change frequency to keep the incision clean and dry. In terms of activity guidance, patients are encouraged to appropriately carry out bed activities such as turning over, limb flexion and extension exercises, but strenuous exercise or excessive force should be avoided to prevent the incision from being pulled and causing bleeding. At the same time, the patient's vital signs and incision condition continue to be monitored, and if any abnormal signs are found, the management strategy will be adjusted in time. During the execution of the entire incision management strategy, medical staff will continuously record the patient's various data and reactions and dynamically adjust the strategy according to the actual situation. For example, if during the execution of the management strategy, the patient's vital signs fluctuate, such as sudden increase in heart rate, and at the same time the incision site bleeds more, the bleeding risk will be re-evaluated immediately, and the incision management measures will be adjusted, such as increasing the dose of hemostatic drugs, further pressure bandaging, etc., to ensure the safety of the patient, promote good healing of the incision, and reduce the risk of postoperative complications.

[0031] Step S130, in the process of executing the incision management strategy, the multi-dimensional vital sign data of the postoperative patient is obtained.

[0032] Step S140, based on the multi-dimensional vital sign data, the potential complications of the postoperative patient are determined, and the postoperative management strategy is generated for the potential complications, and the corresponding home nursing guidance of the postoperative management strategy is generated.

[0033] For example, the basic vital sign data of the patient, such as heart rate, blood pressure, respiratory rate, and blood oxygen saturation, is monitored in real time. These data are transmitted to the central monitoring system in real time through wired or wireless means, and medical staff can view the changes of the patient's vital signs at the monitoring station in the ward at any time. At the same time, special monitoring equipment will be used for special complications that may occur in postoperative patients, such as infection and deep vein thrombosis. For example, a dressing with temperature and humidity sensors may be used at the incision site to monitor changes in skin temperature and humidity around the incision, as local temperature rise and humidity increase often occur when the incision is infected. In addition to equipment monitoring, medical staff will also obtain patient symptom information through daily ward rounds and inquiries, such as whether the patient has symptoms such as abdominal pain, abdominal distension, fever, and dyspnea. These subjective symptom information is also an important part of multi-dimensional vital sign data. After obtaining the multi-dimensional vital sign data, professional analysis methods and clinical experience are used to determine the potential complications of the postoperative patient. Taking incision infection as an example, if the skin temperature around the incision increases and the humidity increases, and the patient complains of increased incision pain, and through blood routine examination, it is found that the white blood cell count is increased, these data combined together indicate that there may be a potential risk of incision infection. For deep vein thrombosis, if lower extremity venous ultrasound monitoring shows that the blood vessel diameter is thickened and the blood flow velocity is slowed down, and the patient has symptoms such as lower extremity swelling and pain, it can be judged that the patient has a potential complication of deep vein thrombosis. Once the potential complications are determined, postoperative management strategies will be generated for these complications. For incision infection, the management strategy may include strengthening incision dressing change, using antibiotics for treatment, and if necessary, performing incision secretion culture and drug sensitivity test to select more effective antibiotics.

[0034] On the other hand, while generating the postoperative management strategy, it is converted into the corresponding home nursing guidance. For the home nursing guidance of incision infection, the patient and family members are informed how to correctly perform incision dressing change, including disinfection method, dressing change frequency, etc.; the patient is reminded to pay attention to personal hygiene to avoid water from touching the incision; the patient is guided to observe the incision condition, such as redness and swelling, exudation, and increased pain, and seek medical treatment in time. For the home nursing guidance of deep vein thrombosis, the patient is informed to strictly rest in bed and avoid strenuous exercise; the patient is guided to correctly use anticoagulant drugs and pay attention to observation of bleeding tendency, such as gum bleeding and skin ecchymosis; the patient is reminded to regularly measure the circumference of the lower extremities and observe the swelling condition, and contact the responsible medical staff in time if there is any abnormality.

[0035] In some embodiments of the present application, the real-time evaluation of bleeding risk and precise management of incisions are achieved by dynamically integrating postoperative patient sign monitoring data (including bleeding parameters), and further combining multi-dimensional vital sign data to predict potential complications, generate personalized postoperative management strategies and home care guidance, thereby constructing a whole-cycle risk prevention and control system from hospital to home, effectively reducing the incidence of postoperative complications, shortening the recovery period, and at the same time improving the self-management ability of patients and the utilization efficiency of medical resources.

[0036] Further, based on the above embodiments, please refer to Figure 2 In one of the example embodiments provided in the present application, the specific implementation process of determining the potential complications of the postoperative patient based on the multi-dimensional vital sign data can further include steps S210 and S220, which are described in detail as follows: Step S210, inputting the multi-dimensional vital sign data into the trained neural network model for time series analysis to obtain a characteristic time series analysis result; Step S220, determining a composite risk index of the postoperative patient based on the sign time series analysis result, the composite risk index including risk indexes of the postoperative patient in multiple aspects, and determining the potential complications of the postoperative patient based on the composite risk index.

[0037] For example, the data is sorted in chronological order to form time series data. Then, the sorted time series data is input into the trained neural network model. The neural network model is trained on a large amount of clinical data and has the ability to perform time series analysis on vital sign data. Inside the model, the law and trend of data change over time are analyzed through complex neuron calculation and weight adjustment. For example, for heart rate data, the model analyzes its fluctuation frequency, amplitude, and associated changes with other vital signs such as blood pressure; for skin temperature and humidity data around the incision, it identifies whether there is an abnormal rising or falling trend. Through such time series analysis, the model can extract key feature information and obtain feature time series analysis results. Based on the feature time series analysis results, the composite risk index of the postoperative patient is further determined. This composite risk index takes into account the patient's risk in multiple aspects, such as incision infection risk, deep vein thrombosis risk, and pulmonary infection risk. Taking the incision infection risk as an example, if the skin temperature around the incision continues to rise and the humidity increases, while the heart rate accelerates and the white blood cell count rises, these features show a specific correlation pattern in the time series analysis results, and the model will assign corresponding risk weights according to these patterns to calculate the incision infection risk index. Similarly, for the risk of deep vein thrombosis, the risk index is determined according to the time series changes of the characteristics such as the slowing of lower limb venous blood flow velocity and the degree of lower limb swelling. Integrating the risk indexes of various aspects, the composite risk index of the patient is formed. Finally, the potential complications of the postoperative patient are determined according to the composite risk index. When the risk index of a certain aspect exceeds the preset threshold, it is determined that the patient has potential complications in that aspect. For example, if the incision infection risk index reaches a high risk level, it is determined that the patient has potential complications of incision infection; if the deep vein thrombosis risk index exceeds the threshold, it is determined that the patient has potential complications of deep vein thrombosis.

[0038] In some embodiments of the present application, by using a neural network model to perform time series analysis on multi-dimensional vital sign data, the postoperative risk of the patient can be more accurately and comprehensively evaluated, potential complications can be discovered in time, and strong evidence can be provided for subsequent development of targeted postoperative management strategies, which helps to improve the rehabilitation quality of gynecological tumor postoperative patients and reduce the incidence of complications.

[0039] Further, based on the above embodiments, please refer to Figure 3 In one of the example embodiments provided in the present application, the implementation process of the above-mentioned postoperative management method for gynecological tumors can further include steps S310 to S330, which are described in detail as follows: Step S310: If the time series analysis result represents that the inflammation marker exceeds the threshold value in consecutive monitoring periods, an infection warning is triggered. Step S320, determining the correlation analysis result between the heart rate variability and the blood oxygen saturation of the postoperative patient based on the result of the sign time series analysis, and determining the thrombosis risk of the postoperative patient based on the correlation analysis result; Step S330, comprehensively evaluating the composite risk index of bleeding, infection and thrombosis by a random forest algorithm, and determining the potential complications of the postoperative patient based on the composite risk index.

[0040] For example, in terms of sign monitoring, the multifunctional monitor is used to collect the basic vital sign data of the patient in real time, such as heart rate, blood pressure, respiratory rate, blood oxygen saturation, etc., and at the same time, special blood detection equipment is used to regularly monitor inflammatory markers such as C-reactive protein and white blood cell count, etc. These data are sorted into time series data in chronological order. After inputting these time series data into the trained neural network model for time series analysis, if the analysis result shows that the inflammatory markers exceed the preset threshold value in continuous multiple monitoring periods, the system will automatically trigger an infection warning. This warning mechanism can timely remind medical staff to pay attention to the possible infection risk of the patient, so as to take further diagnosis and treatment measures. Then, based on the same sign time series analysis result, statistical methods are used to analyze the correlation between heart rate variability and blood oxygen saturation. Heart rate variability reflects the stability of heart rhythm, while blood oxygen saturation reflects the carrying capacity of oxygen in the blood. By analyzing the change trend and correlation degree of the two in the time series data, the correlation analysis result is obtained. For example, if it is found that the heart rate variability decreases while the blood oxygen saturation also shows a downward trend, and this correlation persists in multiple monitoring periods, it may indicate that the patient has a risk of thrombosis. Because thrombosis can affect blood circulation, increase the load on the heart, and thus affect heart rate variability and blood oxygen saturation. Then, the infection warning information obtained by the above analysis, the thrombosis risk assessment result, and the previous evaluation data of the bleeding risk are used as input features, and a random forest algorithm is used for comprehensive evaluation. The random forest algorithm can consider the complex relationship between multiple risk factors by constructing multiple decision trees and integrating the results of these decision trees, so as to calculate the composite risk index of bleeding, infection and thrombosis. This composite risk index takes into account the risk status of the patient in multiple aspects, and more comprehensively reflects the overall health risk of the patient. Finally, the potential complications of the postoperative patient are determined according to the calculated composite risk index.

[0041] Optionally, in some implementable embodiments, the inflammation marker time series data matrix corresponding to the sign data of the postoperative patient can be generated based on the inflammation markers in the sign data of the postoperative patient wherein, is the number of monitoring periods, is the type of markers, and the threshold value vector can be set as Then, the inflammation markers of each standard are detected for a plurality of consecutive cycles, and if any marker exceeds the corresponding threshold, an infection warning signal is triggered. Among them, the heart rate variability (HRV) time series and the blood oxygen saturation (SpO2) time series of the postoperative patient can be obtained from the results of the sign time series analysis Then, the heart rate variability and the blood oxygen saturation are time series aligned and standardized to obtain: Then, the correlation coefficient can be calculated by a sliding window , which is expressed as: wherein, is the mean value in the window. Then, the negative correlation peak value of the correlation coefficient (i.e., low blood oxygen concentration accompanied by low heart rate variability thrombosis risk) is taken: wherein, is the empirical weight.

[0042] Further, the bleeding risk, infection risk, thrombosis risk and demographic characteristics (such as age, BMI, history of underlying diseases, etc.) of the postoperative patient are input into the random forest algorithm model as input parameters. The training data of each decision tree is obtained by Bootstrap Aggregating (Bagging), and at each node split of the tree, a subset of features is randomly selected, the Gini Index is calculated and the optimal split feature is selected to maximize the information gain. A plurality of decision trees (such as 100-500) are trained to form a random forest, which reduces the risk of overfitting. Each tree outputs the prediction probability of bleeding, infection and thrombosis for the test sample, and the average of the prediction results of all trees is taken to obtain the comprehensive risk probability. Then, based on the comprehensive risk probability and the independent probability of complications, the potential complication type and risk level of the postoperative patient are determined.

[0043] In some embodiments of the present application, the abnormal fluctuations of the inflammation markers are dynamically captured by the sign time series analysis to trigger the infection warning in real time, the correlation between the heart rate variability and the blood oxygen saturation is quantified to quantify the thrombosis risk, and the random forest algorithm is used to fuse the multi-dimensional risk factors to generate the bleeding-infection-thrombosis composite risk index, so as to realize the early screening, accurate stratification and dynamic intervention of postoperative complications, effectively reduce the incidence of serious complications, optimize the allocation of medical resources, and at the same time provide interpretable multi-modal data support for clinical decision-making.

[0044] Further, based on the above embodiments, please refer to Figure 4 In one of the example embodiments provided in the present application, the specific implementation process of determining the incision management strategy of the postoperative patient based on the bleeding risk assessment result and the sign monitoring data can further include steps S410 to S430, which are described in detail as follows: Step S410, if the bleeding risk assessment result represents that the bleeding risk level of the postoperative patient is level one, then the routine disinfection and compression bandaging strategy is adopted; Step S420, if the bleeding risk assessment result represents that the bleeding risk level of the postoperative patient is level two, then the application of hemostatic sponge is increased, and the frequency of incision assessment is increased; Step S430, if the bleeding risk assessment result represents that the bleeding risk level of the postoperative patient is level three, then the intensified management scheme including negative pressure drainage and local application of thrombin is started, wherein the evaluation of the bleeding risk level is related to the body temperature of the postoperative patient.

[0045] For example, in the postoperative management of gynecological tumor, the assessment of the bleeding risk level of the postoperative patient and the implementation of the corresponding strategy are a dynamic and precise process. First, data related to bleeding risk are collected through various means, such as monitoring the pressure values at different time points of the incision site using a dressing with a pressure sensor to reflect the amount and speed of bleeding, accurately measuring the blood content and total amount of drainage fluid through a flow sensor connected to the drainage tube and calculating the bleeding ratio, and continuously monitoring the patient's heart rate, blood pressure, and other vital signs with the help of a multifunctional monitor. Based on these data, combined with clinical experience and preset assessment criteria, the bleeding risk is classified into levels, with body temperature as an important influencing factor participating in the assessment. When the bleeding risk assessment result is level one, it indicates that the bleeding risk is relatively low, at which time the routine disinfection and compression bandaging strategy is adopted. Routine disinfection can effectively prevent incision infection, and compression bandaging can apply appropriate pressure to the incision site to reduce bleeding. If the bleeding risk assessment result is level two, it means that the bleeding risk has increased, in addition to routine disinfection and compression bandaging, the application of hemostatic sponge is increased. Hemostatic sponge has good hemostatic performance and can further control bleeding, while increasing the frequency of incision assessment to timely discover changes in bleeding conditions and take corresponding measures. When the bleeding risk assessment result is level three, it indicates that the bleeding risk is extremely high, at which time the intensified management scheme including negative pressure drainage and local application of thrombin is started. Negative pressure drainage can timely drain the blood and exudate in the incision, reduce local pressure, and promote wound healing; local application of thrombin can directly act on the bleeding site to accelerate blood clotting and effectively control bleeding.

[0046] In some embodiments of the present application, by constructing a bleeding risk grading system (grade 1 to grade 3) dynamically associated with body temperature, implementing a ladder management strategy from routine disinfection compression dressing to hemostatic sponge intensive intervention, to negative pressure drainage combined with thrombin application, the precise stratified response and dynamic regulation of postoperative bleeding risk are realized, which not only avoids the over-treatment of low-risk patients, but also ensures the timely intervention of high-risk patients, thereby effectively reducing the incidence of postoperative bleeding complications, shortening the incision healing time, and optimizing the allocation of medical resources and the work efficiency of medical staff.

[0047] Further, based on the above embodiments, please refer to Figure 5 In one of the example embodiments provided in the present application, the specific implementation process of the postoperative management method of gynecological tumors can further include steps S510 to S530, which are described in detail as follows: Step S510, acquiring the incision image of the postoperative patient, and performing visual analysis on the incision image to determine the redness proportion data corresponding to the incision and the color feature of the exudate of the incision according to the visual analysis result; Step S520, acquiring the pressure data of the tissue around the incision of the postoperative patient, and determining the tension change data around the incision based on the pressure data; Step S530, determining the incision management strategy of the patient based on the redness proportion data, the color feature of the exudate, and the tension change data.

[0048] For example, the incision image is pre-processed, including denoising, adjusting brightness and contrast, etc., to improve the image quality. Then, an image segmentation algorithm is used, such as a color threshold-based or texture feature-based segmentation method, to accurately identify the incision area. Within the incision area, color analysis techniques are used, such as converting the image to a hue, saturation, value (HSV) color space, setting a redness color range threshold, counting the number of pixels that meet the threshold, and dividing the total number of pixels in the incision area to obtain the redness ratio data. At the same time, for the incision exudate area, similar color analysis methods are used to extract its color features, such as determining whether it is purulent (yellow or yellow-green) or bloody (red). Then, pressure sensors are arranged around the incision tissue, which collect real-time pressure data and transmit them to the data acquisition and analysis system. The system filters the pressure data to remove noise interference, then calculates the rate of change of pressure over time to analyze the tension change data around the incision, which reflects the tension of the tissue around the incision. Finally, the redness ratio data, exudate color features, and tension change data are combined to develop an incision management strategy. If the redness ratio is high, the exudate is purulent, and the tension change is large, it indicates that the incision may have a serious infection and poor healing risk, and immediate disinfection, antibiotic use, and re-suturing should be considered; if the redness ratio is low, the exudate is a small amount of blood, and the tension change is normal, only routine disinfection and observation are needed. Through this comprehensive analysis method based on multi-dimensional data, the incision management strategy can be accurately developed to effectively promote incision healing and reduce the incidence of complications.

[0049] In some embodiments of the present application, by fusing the incision image visual analysis technology (precise quantification of redness ratio and exudate color features) and the incision surrounding tissue pressure monitoring (dynamic capture of tension change data), a multi-modal incision state evaluation system is constructed, which realizes early identification and differentiated intervention of postoperative incision infection, poor healing and other risks, avoids the lag of traditional subjective evaluation, provides an objective basis for clinical development of individualized dressing frequency, pressure scheme or surgical intervention, thereby significantly reduces the incidence of incision complications, shortens the hospitalization period, and improves the postoperative life quality of patients and nursing efficiency.

[0050] Further, based on the above embodiments, please refer to Figure 6 In one of the example embodiments provided by the present application, the specific implementation process of generating a postoperative management strategy for potential complications can further include steps S610 to S630, which are described in detail as follows: Step S610, if the potential complication indicates that the patient has an infection risk, an anti-infection scheme including an antibiotic selection tree and a incision drainage timing is generated; Step S620, if the potential complication characterizes the postoperative patient as having a risk of bleeding, a hemostatic agent ladder medication plan is generated, which includes coagulation function detection; Step S630, if the potential complication characterizes the postoperative patient as having a risk of thrombosis, a prevention program is generated, which includes the duration of use of lower limb compression devices and the dose of anticoagulant drugs.

[0051] For example, first, various data of the postoperative patient are obtained through various monitoring means, such as monitoring the pressure change of the incision site by using a dressing with a pressure sensor, measuring the composition of the drainage fluid by connecting a flow sensor to the drainage tube, monitoring vital signs by using a multifunctional monitor, etc. The composite risk index of bleeding, infection and thrombosis is comprehensively evaluated by combining the time sequence analysis of the signs and the random forest algorithm, and the potential complications are determined based on the index. If the potential complication characterizes the patient as having a risk of infection, an anti-infection program is generated. According to the severity of the infection, the possible type of pathogen (such as gram-positive bacteria, negative bacteria, etc.), and the patient's allergy history, liver and kidney function, etc., an antibiotic selection tree is constructed. The selection tree starts with broad-spectrum antibiotics, and gradually adjusts to narrow-spectrum and targeted antibiotics according to the results of bacterial culture and drug sensitivity test. At the same time, the timing of incision drainage is determined according to the amount of incision exudate, the degree of redness and swelling, etc. For example, when the exudate is abundant and the drainage is not smooth, drainage is performed in time, and the drainage device is replaced regularly. If the potential complication characterizes the patient as having a risk of bleeding, a hemostatic agent ladder medication plan is generated. First, coagulation function detection is performed, including prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen, etc. According to the test results, the first ladder hemostatic agent is used for mild coagulation dysfunction, such as tranexamic acid; if the coagulation function is further deteriorated, the second ladder hemostatic agent is used, such as vitamin K, prothrombin complex, etc.; the third ladder hemostatic agent is used for severe bleeding, such as recombinant human coagulation factor, etc., and the dose and interval of administration are adjusted according to the bleeding situation. If the potential complication characterizes the patient as having a risk of thrombosis, a prevention program is generated. According to the patient's age, weight, activity ability, etc., the duration of use of lower limb compression devices is determined, such as using intermittent pneumatic compression devices for several hours a day for long-term bedridden patients. At the same time, the dose of anticoagulant drugs is adjusted according to the patient's renal function, bleeding risk, etc., such as low molecular weight heparin, which calculates the initial dose according to the body weight, and then adjusts the dose according to the coagulation index to ensure effective prevention of thrombosis while reducing the risk of bleeding.

[0052] In some embodiments of the present application, by intelligent classification based on the type of potential complications (infection / bleeding / thrombosis), precise anti-infection strategies including antibiotic selection trees and incision drainage timing, ladder hemostatic schemes guided by coagulation function detection, and thrombosis prevention plans combining lower limb compression devices and anticoagulant drugs are generated respectively, realizing individualization, dynamization and full-process coverage of postoperative complication management, effectively reducing the incidence of serious complications, shortening the patient's recovery period, and optimizing the efficiency of medical resources and the scientificity of clinical decision-making.

[0053] Further, based on the above embodiments, please refer to Figure 7 In one of the example embodiments provided in the present application, the specific implementation process of the postoperative management method for gynecological tumors can further include steps S710 to S730, which are described in detail as follows: Step S710, obtaining incision secretion of the postoperative patient, culturing the incision secretion, and dynamically adjusting the anti-infection scheme including the antibiotic selection tree and the incision drainage timing based on the secretion culture result; Step S720, determining the body temperature curve of the postoperative patient, and based on the use timing of the antipyretic analgesic drug of the postoperative patient; Step S730, if the infection index of the postoperative patient does not improve within a preset time length, generating a multi-department consultation request.

[0054] For example, in the postoperative management of gynecological tumors, first, the incision secretion sample of the postoperative patient is obtained through aseptic operation, and the sample is sent to the laboratory for culture. During the culture process, laboratory personnel will select appropriate culture medium and culture conditions according to the characteristics of the secretion to promote the growth of possible pathogenic microorganisms. After the culture is completed, the culture results are identified and analyzed to determine the types of pathogenic bacteria and their sensitivity to different antibiotics. Based on these results, an antibiotic selection tree is constructed, starting with broad-spectrum antibiotics and gradually adjusting to more precise antibiotics according to the drug sensitivity test results. At the same time, combined with indicators such as incision exudate volume and degree of swelling, the timing of incision drainage is dynamically adjusted, such as when the secretion increases, drainage is not smooth, or the incision appears obvious swelling, timely drainage operation is performed, and the drainage device is replaced regularly. In terms of body temperature monitoring, the multifunctional monitor is used to continuously record the body temperature data of the postoperative patient, and the body temperature curve is drawn. According to the change of the body temperature curve, combined with the pain degree and symptom of the patient, the use timing of the antipyretic analgesic drug is determined. For example, when the body temperature exceeds 38.5℃ and the patient has obvious pain or discomfort, the antipyretic analgesic drug is given in time. In addition, a preset time length, such as 3 to 5 days, is set to continuously monitor the infection index of the patient, such as white blood cell count, C-reactive protein, etc. If the infection index does not show improvement within the preset time length, the system will automatically generate a multi-department consultation request, inviting experts from the infection department, surgery department and other related departments to jointly consult, comprehensively analyze the patient's condition, and develop more effective treatment plans to improve treatment effect and promote patient recovery.

[0055] In some embodiments of the present application, by dynamically integrating incision secretion culture results to accurately optimize antibiotic selection and drainage timing, combining body temperature curve to intelligently control analgesic drug use nodes, and automatically triggering multidisciplinary consultation mechanism when infection indicators continue to improve, a closed-loop, precise, and collaborative prevention and control system for postoperative infection management is constructed, which effectively improves the success rate of early intervention of infection, reduces the risk of drug-resistant bacteria infection, shortens the antibiotic use cycle and hospitalization time, and ultimately realizes the dual optimization of patient safety and medical resource utilization.

[0056] Further, based on the above embodiments, please refer to Figure 8 In one of the example embodiments provided by the present application, the specific implementation process of generating the home care guidance corresponding to the postoperative management strategy can further include steps S810 to S830, which are described in detail as follows: Step S810, determining a target medical resource map based on the location information of the postoperative patient, and generating an emergency treatment video based on the vital sign data of the postoperative patient; Step S820, configuring a reminder program of an intelligent medicine box based on the medication plan of the postoperative patient and making an AR teaching video containing incision self-check gesture recognition; Step S830, generating the home care guidance corresponding to the postoperative management based on the target medical resource map, the reminder program, the emergency treatment video and the AR teaching video.

[0057] For example, the location information of the postoperative patient is obtained through the positioning device carried by the patient, combined with the information of the surrounding medical resources stored in the hospital database, such as the location, service range and specialty of hospitals, clinics, pharmacies, etc., the target medical resource map is drawn by using GIS technology, and the distribution of available medical resources around the patient is clearly displayed. At the same time, by connecting the wearable devices or home medical monitoring instruments of the patient, the vital sign data such as heart rate, blood pressure, body temperature, etc. is collected in real time, and artificial intelligence algorithm is used to analyze these data in real time. Once abnormal signs are found, the corresponding processing video is retrieved from the preset emergency treatment video library. The video content covers the preliminary judgment of abnormal signs, emergency treatment steps and information on when to see a doctor, etc. In terms of medication management, according to the medication plan made by the doctor for the patient, the reminder program of the intelligent medicine box is configured, which can set the medication time and dose reminder, and remind the patient through the connection with the patient's mobile phone or other smart devices in the form of sound, vibration or pop-up window, etc. In addition, in order to help the patient better perform incision self-check, an AR teaching video containing incision self-check gesture recognition is made, and the patient scans the incision site through a mobile phone or tablet computer, etc. The video will display the correct self-check gestures and steps in the form of augmented reality, and real-time feedback whether the self-check result is correct.

[0058] Optionally, the target medical resource map, intelligent medicine box reminder program, emergency treatment video and AR teaching video can be integrated into a comprehensive postoperative management home nursing guidance system. Patients can access the system through a mobile application or web page. The system provides comprehensive home nursing guidance in an intuitive and convenient manner, including medical resource query, medication reminders, emergency treatment guidance, and incision self-checking teaching, to ensure that patients receive scientific and effective care at home.

[0059] In some embodiments of the present application, by integrating the dynamic medical resource map positioning of postoperative patient location information, the individualized emergency treatment video guidance driven by sign data, the interactive nursing support of intelligent medicine box reminder program and incision self-checking AR teaching video, a home nursing closed-loop system covering the whole scene of "resource acquisition-emergency disposal-medication management-self monitoring" is constructed, which significantly improves the postoperative self-management ability and emergency response efficiency of patients, reduces the risk of rehospitalization due to improper home nursing, and realizes the extension of medical services outside the hospital and precise health management.

[0060] Further, based on the above embodiments, please refer to Figure 9 In one of the example embodiments provided in the present application, the specific implementation process of the postoperative management method for gynecological tumors can further include steps S910 and S920, which are described in detail as follows: Step S910, generating an augmented reality guide for the home nursing staff based on the AR teaching video, and correcting the operation posture of the home nursing staff based on the augmented reality guide; Step S920, acquiring real-time state information of the postoperative patient based on a voice interaction pain assessment question and answer system, and if the real-time state information represents an incorrect nursing action, triggering a vibration warning and displaying a standard operation comparison chart.

[0061] For example, in the process of postoperative home care of gynecological tumors, first, an AR teaching video containing incision self-check gesture recognition is made, and the home care personnel watch the video by wearing AR glasses or using a mobile device with AR function. The augmented reality technology in the video will superimpose the correct nursing operation gestures and steps in the form of virtual images in the actual environment, generating corresponding augmented reality guidance, and the home care personnel can operate according to these guidelines. At the same time, the built-in posture recognition algorithm of the device is used to monitor the operation posture of the nursing personnel in real time. Once it is found that the posture does not match the standard operation, the system will issue a voice prompt through the AR device to inform the home care personnel of the correct operation direction and adjustment method, and dynamically display the correct operation posture through virtual images to help the home care personnel correct the operation posture in time. On the other hand, a pain assessment question and answer system based on voice interaction is constructed, and the postoperative patient interacts with the system through voice to answer questions about the degree of pain, location, duration, etc. The system will convert these answers into real-time state information. If the real-time state information represents an error in nursing action, the system will immediately trigger the vibration warning function of the AR device to remind the home care personnel to pay attention to the operation error. At the same time, a standard operation comparison chart is displayed on the screen of the AR device, which presents the current error operation of the home care personnel and the standard operation in a side-by-side comparison manner, allowing the home care personnel to intuitively see the differences and correct the errors in time, ensuring that the postoperative patient receives correct and effective home care.

[0062] In some embodiments of the present application, the AR augmented reality technology provides real-time three-dimensional operation guidance and posture correction for home care personnel, and the voice interaction pain assessment system dynamically captures the patient's state, and immediately triggers the vibration warning and standard operation comparison feedback when an error in nursing action is detected, thus constructing a closed-loop home care training system of "intelligent sensing-precise guidance-immediate error correction", effectively reducing the risk of complications caused by improper operation, improving the quality of postoperative rehabilitation and nursing compliance of patients, and reducing the burden of remote guidance of professional medical personnel.

[0063] Please refer to Figure 10 In an exemplary embodiment of the present application, a postoperative management system for gynecological tumors is also proposed, which includes a processor, an input device, an output device and a memory, and the processor, the input device, the output device and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the postoperative management method for gynecological tumors according to any one of the above embodiments.

[0064] It should be noted that the postoperative management system of gynecological tumors provided in the above embodiments and the postoperative management method of gynecological tumors provided in the above embodiments belong to the same concept, and the specific manner in which each module and unit performs operations has been described in detail in the method embodiments, which will not be repeated here. The postoperative management system of gynecological tumors provided in the above embodiments can be used in actual applications, and the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0065] Embodiments of the present application also provide an electronic device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the electronic device implements the postoperative management method of gynecological tumors provided in each of the above embodiments.

[0066] Based on the above method embodiments and device embodiments, the present application further provides an electronic device. Referring to Figure 11 , a structural schematic diagram of an electronic device provided in an embodiment of the present application. Figure 11 The electronic device shown can at least include a processor 1101, an input interface 1102, an output interface 1103, and a computer storage medium 1104. Among them, the processor 1101, the input interface 1102, the output interface 1103 and the computer storage medium 1104 can be connected through a bus or other means.

[0067] The computer storage medium 1104 can be stored in the memory of the electronic device, and the computer storage medium 1104 is used to store a computer program, the computer program includes program instructions, and the processor 1101 is used to execute the program instructions stored by the computer storage medium 1104. The processor 1101 (or CPU (Central Processing Unit, Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, and is suitable for loading and executing one or more instructions to realize the above postoperative management method of gynecological tumors or corresponding functions.

[0068] The embodiment of the present application further provides a computer storage medium (Memory). The computer storage medium is a memory device in an electronic device, and is used for storing programs and data. It can be understood that the computer storage medium herein can include a built-in storage medium in the terminal, and of course can include an expansion storage medium supported by the terminal. The computer storage medium provides a storage space, and the storage space stores an operating system of the terminal. Furthermore, one or more instructions suitable for being loaded and executed by the processor 1101 are stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer storage medium herein can be a high-speed random access memory (RAM) memory, or a non-volatile memory such as at least one disk memory. Optionally, the computer storage medium can be at least one computer storage medium located away from the processor.

[0069] In one embodiment, the processor 1101 can load and execute one or more instructions stored in the computer storage medium, and the corresponding steps of the method in the embodiment of the postoperative management method of gynecological tumors. In a specific implementation, the processor 1101 loads and executes one or more instructions in the computer storage medium to implement the steps in the embodiment of the postoperative management method of gynecological tumors.

[0070] The embodiment of the present application provides a computer program product or a computer program, which includes computer instructions stored in a computer readable storage medium. The processor of the electronic device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to enable the electronic device to perform the above-mentioned postoperative management method of gynecological tumors. The computer readable storage medium can be a disk, an optical disc, a read-only memory (ROM) or a random access memory (RAM).

[0071] It should be noted that the computer-readable medium in the embodiments shown in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may, for example, be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable signal medium can include a data signal propagating in a baseband or as a carrier wave in a propagated data signal, in which the computer-readable computer program is carried. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate or transmit programs for use by or in connection with an instruction execution system, device or component. The computer program contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, or the like, or any suitable combination of the above.

[0072] The flowcharts and block diagrams in the drawings illustrate the possible implementation architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In the flowcharts or block diagrams, each block can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different order than that shown in the drawings. For example, two blocks represented in succession can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0073] The units described in the embodiments of the present application can be implemented in the form of software, or can be implemented in the form of hardware, and the described units can also be arranged in a processor. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0074] Another aspect of the present application also provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the postoperative management method of gynecological tumors as described above. The computer readable storage medium can be included in the electronic device described in the above embodiments, or can exist separately without being assembled into the electronic device.

[0075] Another aspect of the present application also provides a computer program product or a computer program, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to enable the computer device to perform the postoperative management method of gynecological tumors provided in the above embodiments.

[0076] The above is only a preferred exemplary embodiment of the present application, and is not intended to limit the implementation of the present application. Those skilled in the art can easily make corresponding modifications or variations according to the main concept and spirit of the present application. Therefore, the protection scope of the present application should be subject to the protection scope required by the claims.

Claims

1. A postoperative management method for gynecological tumors, characterized in that, include: Acquire postoperative vital sign monitoring data of the patient within a preset time period, the vital sign monitoring data including bleeding parameters; Based on the bleeding parameters, the postoperative bleeding risk assessment result of the patient is determined, and the incision management strategy of the patient is determined based on the bleeding risk assessment result and the vital sign monitoring data. During the implementation of the incision management strategy, multidimensional vital sign data of the postoperative patient are acquired; Based on the multidimensional vital sign data, potential complications of the postoperative patient are identified, and postoperative management strategies are generated for the potential complications, along with corresponding home care guidance.

2. The method as described in claim 1, characterized in that, The determination of potential postoperative complications based on the multidimensional vital sign data includes: The multidimensional vital sign data is input into the trained neural network model for time series analysis to obtain the feature time series analysis results; Based on the time-series analysis results of the vital signs, a composite risk index is determined for the postoperative patient. The composite risk index includes risk indices for the postoperative patient in multiple aspects, and potential complications of the postoperative patient are determined based on the composite risk index.

3. The method as described in claim 2, characterized in that, The method further includes: If the time-series analysis results of the vital signs indicate that the inflammatory markers exceed the threshold within multiple consecutive monitoring periods, an infection warning will be triggered. Based on the time-series analysis results of the vital signs, the correlation analysis results between the postoperative patient's heart rate variability and blood oxygen saturation were determined, and the risk of thrombosis in the postoperative patient was determined based on the correlation analysis results. The composite risk index of bleeding, infection, and thrombosis was comprehensively evaluated using the random forest algorithm, and the potential complications of the postoperative patients were determined based on the composite risk index.

4. The method as described in claim 1, characterized in that, The postoperative incision management strategy for the patient, determined based on the bleeding risk assessment results and the vital signs monitoring data, includes: If the bleeding risk assessment result indicates that the postoperative patient's bleeding risk level is level one, then routine disinfection and pressure bandaging strategies shall be adopted; If the bleeding risk assessment results indicate that the postoperative patient's bleeding risk level is level two, then the application of hemostatic sponges should be increased, and the frequency of incision assessment should be increased. If the bleeding risk assessment result indicates that the postoperative patient's bleeding risk level is level three, then an intensive management plan including negative pressure drainage and local application of thrombin is initiated, wherein the evaluation of the bleeding risk level is related to the postoperative patient's body temperature.

5. The method as described in claim 4, characterized in that, The method further includes: The incision image of the postoperative patient is acquired, and the incision image is visually analyzed to determine the percentage of redness and swelling corresponding to the incision and the color characteristics of the exudate from the incision based on the visual analysis results. Acquire pressure data of the tissues surrounding the incision of the postoperative patient, and determine the tension change data around the incision based on the pressure data; The incision management strategy for the patient is determined based on the data on the percentage of redness and swelling, the color characteristics of the exudate, and the data on the changes in tension.

6. The method as described in claim 1, characterized in that, The postoperative management strategy for the potential complications includes: If the potential complications indicate that the postoperative patient is at risk of infection, an anti-infection regimen is generated that includes an antibiotic selection tree and the timing of incision drainage. If the potential complications indicate that the postoperative patient is at risk of bleeding, a hemostatic agent stepwise dosing plan including coagulation function testing is generated. If the potential complication indicates that the postoperative patient is at risk of thrombosis, a prevention plan is generated that includes the duration of use of the lower limb compression device and the dosage of anticoagulants.

7. The method as described in claim 6, characterized in that, The method further includes: The incision secretions of the postoperative patients were obtained and cultured. Based on the secretion culture results, the anti-infection regimen, which included an antibiotic selection tree and the timing of incision drainage, was dynamically adjusted. Determine the postoperative patient's body temperature curve based on the timing of the administration of antipyretic and analgesic drugs. If the postoperative patient's infection indicators do not improve within a preset time period, a multidisciplinary consultation request will be generated.

8. The method as described in claim 1, characterized in that, The process of generating home care guidance corresponding to the postoperative management strategy includes: A target medical resource map is determined based on the postoperative patient's location information, and an emergency response video is generated based on the postoperative patient's vital signs data. Based on the postoperative patient's medication plan, configure a reminder program for the smart pillbox and create AR teaching videos that include incision self-examination gesture recognition; Based on the target medical resource map, the reminder program, the emergency response video, and the AR teaching video, home care guidance corresponding to the postoperative management is generated.

9. The method as described in claim 8, characterized in that, The method further includes: Augmented reality guidance is generated for home care workers based on the AR teaching video, and the operation posture of the home care workers is corrected based on the augmented reality guidance. The pain assessment question-and-answer system based on voice interaction obtains the real-time status information of the postoperative patient. If the real-time status information indicates an incorrect nursing action, a vibration warning is triggered and a comparison chart of standard operation is displayed.

10. A postoperative management system for gynecological tumors, characterized in that, The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the postoperative management method for gynecological tumors as described in any one of claims 1 to 9.

Images