Aortic dissection postoperative patient rehabilitation management method and related equipment

Abstract

The invention discloses an aortic dissection postoperative patient rehabilitation management method and related equipment. The method comprises the following steps: acquiring behavior data of a patient by using portable mobile terminal equipment carried by the aortic dissection postoperative patient; determining rehabilitation assessment supplementary data based on rehabilitation assessment theoretical data and the acquired behavior data, so as to generate an assessment questionnaire according to the rehabilitation assessment supplementary data; and performing rehabilitation evaluation on the patient according to feedback data of the patient in response to the evaluation questionnaire and the behavior data. The problems that traditional aortic dissection postoperative patient rehabilitation state evaluation mainly depends on telephone follow-up visit, patient self-report and regular physical examination results, subjectivity is high, information is lagged and incomplete, continuity and real-time performance are lacked, and quantitative evaluation is difficult to conduct can be solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of smart medical care. More specifically, the present invention relates to a method and related equipment for rehabilitation management of patients after aortic dissection surgery. Background Art

[0002] Aortic dissection is a serious cardiovascular emergency characterized by sudden onset, rapid progression, and high mortality. This group of people is at higher risk of recurrence, rupture, and cardiovascular disease of aortic dissection. The diseased aorta is almost always dilated and may experience greater inner wall stress. Patients after aortic dissection surgery often have problems such as poor quality of life, decreased physiological function and role function; 59% of patients after aortic dissection surgery are prone to mental health disorders such as anxiety, depression, or substance abuse; in addition, an increasing number of survivors are usually younger, and this group has a greater demand for high-quality life. Therefore, it is crucial to conduct long-term follow-up after the patient is discharged from the hospital after surgery, comprehensively and accurately evaluate and monitor the quality of life and health status of patients with aortic dissection, identify high-risk patients, and improve the patient's prognosis.

[0003] However, traditional assessment of the recovery status of patients after aortic dissection surgery mainly relies on telephone follow-up, patient self-reports and regular physical examination results, which are highly subjective. Patients may provide inaccurate information due to insufficient understanding of their condition, memory bias or the desire to avoid worry. Information is delayed and incomplete. Telephone follow-up and physical examinations are usually only conducted at specific time points, making it difficult to capture the dynamic changes in the patient's recovery process. The frequency of physical examinations is limited and usually focuses on basic physiological indicators. There is a lack of continuity and real-time nature. The evaluation method is mostly intermittent, and there is a lack of continuous monitoring of the patient's recovery process. It is difficult to conduct quantitative evaluation, and telephone follow-up and patient self-reports can often only provide qualitative descriptions. Summary of the invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention, which will be further described in detail in the Detailed Description of the Invention. The Summary of the Invention does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0005] In order to solve the problems that the traditional assessment of the rehabilitation status of patients after aortic dissection surgery mainly relies on telephone follow-up, patient self-report and regular physical examination results, which are highly subjective, information is lagging and incomplete, lacks continuity and real-time, and is difficult to quantitatively assess, in the first aspect, the present invention proposes a method for rehabilitation management of patients after aortic dissection surgery, the method comprising:

[0006] The patient's behavioral data is obtained by using the portable mobile terminal device carried by the patient after aortic dissection surgery;

[0007] Determining rehabilitation assessment supplementary data based on the rehabilitation assessment theoretical data and the acquired behavioral data, so as to generate an assessment questionnaire according to the rehabilitation assessment supplementary data;

[0008] A rehabilitation assessment is performed on the patient based on the patient's feedback data in response to the assessment questionnaire and the behavioral data.

[0009] Optionally, performing rehabilitation assessment on the patient based on the feedback data of the patient in response to the assessment questionnaire and the behavioral data includes:

[0010] constructing a digital phenotype of the patient based on the patient's feedback data in response to the assessment questionnaire and the behavioral data;

[0011] The patient is assessed for rehabilitation based on the digital phenotype.

[0012] Optionally, also include:

[0013] A rehabilitation program is adjusted based on the rehabilitation assessment results, wherein the rehabilitation program includes an exercise rehabilitation program.

[0014] Optionally, also include:

[0015] In the case where it is predicted based on the patient's behavior data that the patient is in a motion state, turning on a Bluetooth communication function of a portable mobile terminal device carried by the patient;

[0016] Based on the Bluetooth communication function, query other user terminal devices within the preset range of the patient;

[0017] In the case that there are no other user terminal devices within the preset range of the patient, an exercise prompt message is generated, wherein the exercise prompt message includes a suggestion to stop exercising or to move to an environment with people.

[0018] Optionally, the exercise rehabilitation program includes exercise type recommendations, and the exercise type includes a fixed-area exercise type. The method further includes:

[0019] The portable mobile terminal device carried by the patient obtains the current location information of the patient;

[0020] Query the location distribution information of medical institutions or AED equipment recorded on the network;

[0021] Based on the current location information and the location distribution information of the medical institution or AED equipment, an exercise area is recommended for the patient.

[0022] Optionally, the motion type includes a moving area motion type, and the method further includes:

[0023] Based on the current position information and the position distribution information of the AED devices, a recommended movement path is planned for the patient.

[0024] Optionally, also include:

[0025] The rehabilitation assessment result is sent to the medical terminal associated with the patient.

[0026] In a second aspect, the present invention further provides a postoperative rehabilitation management device for patients with aortic dissection, comprising:

[0027] An acquisition unit, used for acquiring the patient's behavior data by using a portable mobile terminal device carried by the patient after aortic dissection surgery;

[0028] A determination unit, configured to determine the rehabilitation assessment supplementary data based on the rehabilitation assessment theoretical data and the acquired behavior data, so as to generate an assessment questionnaire according to the rehabilitation assessment supplementary data;

[0029] An evaluation unit is used to perform a rehabilitation evaluation on the patient based on the feedback data of the patient in response to the evaluation questionnaire and the behavior data.

[0030] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the method for rehabilitation management of patients after aortic dissection surgery as described in any one of the first aspects above when executing the computer program stored in the memory.

[0031] In a fourth aspect, the present invention further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for rehabilitation management of patients after aortic dissection surgery according to any one of the above items in the first aspect is implemented.

[0032] In summary, the rehabilitation management method for patients after aortic dissection surgery proposed in this application obtains the patient's behavioral data by using a portable mobile terminal device carried by the patient after aortic dissection surgery; determines the rehabilitation assessment supplementary data based on the rehabilitation assessment theoretical data and the acquired behavioral data, so as to generate an assessment questionnaire based on the rehabilitation assessment supplementary data; and conducts a rehabilitation assessment on the patient based on the patient's feedback data in response to the assessment questionnaire and the behavioral data. Thus, real-time data collection through portable devices makes up for the shortcomings of traditional intermittent assessments and realizes continuous monitoring of the patient's dynamic changes. Behavioral data provides objective assessments, and questionnaire feedback supplements subjective experience to form a complete assessment chain. Based on the patient's behavioral data and feedback, the assessment plan is dynamically adjusted to achieve personalized health management. Medical staff can quickly identify high-risk patients based on the assessment report, adjust the follow-up frequency and intervention measures, and reduce the risk of recurrence and complications.

[0033] The post-operative rehabilitation management method for patients with aortic dissection of the present invention, and other advantages, objectives and features of the present invention will be reflected in part through the following description, and in part will be understood by technicians in this field through research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0035] Figure 1 A schematic flow chart of a method for rehabilitation management of patients after aortic dissection surgery provided in an embodiment of the present application;

[0036] Figure 2 A schematic diagram of the structure of a rehabilitation management device for patients after aortic dissection surgery provided in an embodiment of the present application;

[0037] Figure 3 A schematic diagram of the structure of an electronic device for rehabilitation management of patients after aortic dissection surgery provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0039] In order to solve the problems that the traditional assessment of the recovery status of patients after aortic dissection surgery mainly relies on telephone follow-up, patient self-report and regular physical examination results, which are highly subjective, information is delayed and incomplete, lacks continuity and real-time, and is difficult to quantify, please refer to Figure 1, is a flow chart of a method for rehabilitation management of patients after aortic dissection surgery provided in an embodiment of the present application, which may specifically include: steps S110 to S130.

[0040] S110, obtaining the patient's behavior data using a portable mobile terminal device carried by the patient after aortic dissection surgery.

[0041] S120, determining rehabilitation assessment supplementary data based on the rehabilitation assessment theoretical data and the acquired behavior data, so as to generate an assessment questionnaire according to the rehabilitation assessment supplementary data.

[0042] S130, conducting a rehabilitation assessment on the patient based on the feedback data of the patient in response to the assessment questionnaire and the behavioral data.

[0043] It is understandable that the patient's behavioral data (such as exercise volume, sleep quality, heart rate, activity trajectory, etc.) can reflect their physical state in daily life. Real-time collection of these data through portable mobile terminal devices (such as smart bracelets, smart phones) can provide continuous and real-time health monitoring. Based on the theory of rehabilitation after aortic dissection surgery (including functional recovery, changes in physiological indicators, psychological state, etc.), design adaptive evaluation indicators and methods. Dynamically evaluate the patient's recovery status by analyzing behavioral data and questionnaire feedback data. Combine behavioral data with traditional evaluation indicators to generate more comprehensive and accurate evaluation results. Based on individual differences among patients, dynamically adjust the evaluation method to improve the pertinence and operability of the evaluation.

[0044] It is understood that the mobile data in the behavioral data may include continuous collection and aggregation of data streams passively generated from accelerometer information (average time of active period, average physical activity time, distance covered per day, duration of inactive period), GPS information (places visited per day, hospital visits), and gyroscopes. Social data in behavioral data may include continuous capture and aggregation of the frequency of phone duration, the frequency and length of text messages, Internet browsing time, screen on / off time, etc. Exemplary, wearable devices (such as smart bracelets), smart phones, or portable health monitoring instruments. The collected data may include physiological data: heart rate, blood oxygen saturation, blood pressure; behavioral data: daily steps, exercise intensity, activity trajectory, sleep duration and quality; psychological data: indirect assessment of psychological state through mobile phone usage patterns (such as application usage time, social media interaction). The device can automatically record at a preset frequency (such as every minute, every hour) to avoid manual intervention. For example, a patient using a smart bracelet has a decrease in daily steps and abnormal fluctuations in heart rate, which may indicate a decrease in physical strength or cardiovascular risk. Decreased sleep quality may be related to postoperative anxiety, and this information can be captured by sleep monitoring equipment. Based on the characteristics of postoperative rehabilitation for aortic dissection, key indicators (such as functional recovery, physiological stability, and psychological adaptability) are extracted. Regarding data processing, data cleaning can be included, that is, removing outliers and noise data (such as equipment mismeasurement data). Data integration is to combine the collected behavioral data with the evaluation theoretical model. Data analysis is to use machine learning models or statistical analysis to generate supplementary data for rehabilitation evaluation. If the exercise target defined in the rehabilitation theory is 6,000 steps per day, and the behavioral data shows that the patient has only reached 3,000 steps, the generated supplementary data will indicate the risk of "insufficient exercise". If the heart rate is continuously high and is not related to the amount of activity, it may indicate psychological tension or the risk of complications. Combine behavioral data with theoretical models to dynamically generate questionnaires related to the individual condition of patients. The questionnaire can be divided into modules such as motor function, physiological indicators, and psychological state. According to the behavioral data, increase or decrease the number and content of questionnaire questions. Include multiple-choice questions, rating scales, and open-ended questions to help capture patients' subjective experience and feedback. For example, if the patient's behavioral data indicates that they are not exercising enough, the questionnaire may include: "Have you felt a decrease in physical strength recently?" (yes / no); "How much exercise have you done compared to a week ago?" (increase / decrease / no change). If the sleep quality is poor, the questionnaire may include: "Have you often felt tired or anxious recently?" (score 1-5). Combining the questionnaire feedback data with the behavioral data, a mathematical model is used to evaluate the recovery status. Data clustering can be performed to divide patients into different recovery status categories (good, attention required, high risk). And analyze the time trend of the recovery status. Then generate a personalized health assessment report, including behavioral data trend charts, risk predictions, and improvement suggestions. The report is sent to patients and medical staff via mobile phone applications or emails.For example, if a patient reports a "significant decrease in physical strength", combined with the behavioral data of "reduced number of steps" and "abnormally increased heart rate", the system will determine that the patient is in a "high-risk" state and recommend an immediate review or adjustment of the rehabilitation plan.

[0045] In summary, the rehabilitation management method for patients after aortic dissection surgery provided in the embodiment of the present application obtains the patient's behavioral data by using a portable mobile terminal device carried by the patient after aortic dissection surgery; determines the rehabilitation assessment supplementary data based on the rehabilitation assessment theoretical data and the acquired behavioral data, so as to generate an assessment questionnaire based on the rehabilitation assessment supplementary data; and conducts a rehabilitation assessment on the patient based on the patient's feedback data in response to the assessment questionnaire and the behavioral data. Thus, real-time data collection through portable devices makes up for the shortcomings of traditional intermittent assessments and realizes continuous monitoring of the patient's dynamic changes. Behavioral data provides objective assessments, and questionnaire feedback supplements subjective experience to form a complete assessment chain. Based on the patient's behavioral data and feedback, the assessment plan is dynamically adjusted to achieve personalized health management. Medical staff can quickly identify high-risk patients based on the assessment report, adjust the follow-up frequency and intervention measures, and reduce the risk of recurrence and complications.

[0046] It should be noted that the data sources collected by digital phenotypes can be divided into two subcategories, namely active data and passive data. Behavioral data can correspond to passive data, and questionnaire feedback is active data. Active data refers to data that requires direct input and expression from users to generate, such as various questionnaires, audio data, etc. Active data usually comes directly from users, so it can directly reflect the user's true intentions, needs and feelings. Passive data is data generated by monitoring and collection systems such as mobile phones, sensors and wearable devices, without the need for active input or participation by users, such as GPS trajectories, movement status, voice call records, SMS logs, and data related to screen interactions and user events. Passive data does not require intervention and is automatically collected by the system, capturing the rich and subtle behavioral information of users in daily use in an efficient way. Combining active and passive data collection methods can provide a more comprehensive understanding of individual characteristics, behaviors and health status. Active data provides information about individual subjective feelings and opinions, while passive data provides information about individual objective behaviors and physiological status. By combining these two types of data, a more accurate digital phenotype model can be constructed to provide strong support for clinical diagnosis and personalized treatment.

[0047] According to some embodiments, performing rehabilitation assessment on the patient based on the patient's feedback data in response to the assessment questionnaire and the behavioral data includes:

[0048] constructing a digital phenotype of the patient based on the patient's feedback data in response to the assessment questionnaire and the behavioral data;

[0049] The patient is assessed for rehabilitation based on the digital phenotype.

[0050] It is understandable that the digital phenotype is a comprehensive digital description of the patient constructed by collecting multi-dimensional behavioral, physiological and psychological data of the patient using an algorithm model, which is used to comprehensively reflect the individual's health status and pathological characteristics. In the rehabilitation assessment after aortic dissection surgery, personalized and dynamic rehabilitation management can be achieved by integrating questionnaire feedback data and behavioral data into the digital phenotype.

[0051] Exemplary data sources include: evaluation questionnaire feedback data, including patient subjective feedback (such as pain scores, emotional states, perceived activity levels) and health reports (such as medication compliance). Behavioral data, continuous data collected through portable devices, including: physiological indicators: heart rate, blood oxygen, blood pressure, etc.; activity indicators: number of steps, activity duration, sleep quality; psychological indicators: application usage patterns, sentiment analysis (through questionnaires or voice sentiment analysis). Data preprocessing, including cleaning and filtering, to remove noise and abnormal data (such as device misreading). Standardization processing to normalize data of different dimensions for unified modeling. Use machine learning models or statistical analysis methods to integrate multi-source data to construct a digital phenotype for patients. Phenotypic dimensions include: physiological dimension: postoperative blood pressure control, risk of aortic dilatation; behavioral dimension: daily activity level, exercise recovery; psychological dimension: anxiety and depression risk. The digital phenotype can be dynamically updated based on data that changes over time to reflect the patient's health status in real time. According to the digital phenotype, key indicators related to rehabilitation are extracted, such as: activity recovery indicators: step growth trend, daily activity distribution. Physiological stability indicators: heart rate variability (HRV), blood pressure fluctuations. Psychological state indicators: questionnaire emotion scores, abnormal behavior patterns. Construct a comprehensive rehabilitation scoring model to calculate the patient's current rehabilitation level based on multiple indicators. If a weighted scoring method is used: rehabilitation score = w 1 Physiological indicators + w 2 Behavioral indicators + w 3 ·Psychological indicators, among which w 1 ,w 2 ,w 3is the weight of each indicator, and the weight can be adjusted dynamically according to the characteristics of the patient's condition. Combining the digital phenotype and rehabilitation assessment results, the patient's rehabilitation status is classified: Good status: The rehabilitation score is high and the behavioral data is close to the target value. Attention status: Individual indicators deviate from the normal range and require appropriate intervention. High-risk status: The comprehensive indicators are abnormal, indicating the need for immediate review or adjustment of the treatment plan. As a result, the digital phenotype integrates physiological, behavioral and psychological data, comprehensively depicts the patient's health status, and provides personalized assessments based on individual characteristics. The continuous collection of data and the dynamic update of the digital phenotype overcome the lag of traditional assessments and can capture changes in the rehabilitation process in a timely manner. The rehabilitation assessment results are presented in the form of quantitative scores and classification results, which intuitively reflect the patient's rehabilitation status and facilitate decision-making by patients and medical staff. The risk prediction ability based on the digital phenotype can help identify potential problems in a timely manner, formulate targeted rehabilitation plans, and reduce the risk of complications.

[0052] In some examples, this also includes:

[0053] A rehabilitation program is adjusted based on the rehabilitation assessment results, wherein the rehabilitation program includes an exercise rehabilitation program.

[0054] In some examples, this also includes:

[0055] In the case where it is predicted based on the patient's behavior data that the patient is in a motion state, turning on a Bluetooth communication function of a portable mobile terminal device carried by the patient;

[0056] Based on the Bluetooth communication function, query other user terminal devices within the preset range of the patient;

[0057] In the case that there are no other user terminal devices within the preset range of the patient, an exercise prompt message is generated, wherein the exercise prompt message includes a suggestion to stop exercising or to move to an environment with people.

[0058] It is understandable that, based on the patient's rehabilitation assessment results, a personalized exercise plan suitable for the patient's current physical condition is formulated, including: Exercise type: such as light walking, aerobic exercise, and low-intensity strength training. Exercise intensity: Dynamically adjust exercise goals based on data such as heart rate and number of steps. Exercise frequency: The number and duration of exercises per day or week. Through real-time collected behavioral data (such as exercise intensity and heart rate changes), the patient's adaptability to the current rehabilitation plan is evaluated, and the intensity, frequency or type of exercise is adjusted according to the actual situation. For example, the rehabilitation assessment results of a patient show that the heart rate variability is low and the heart rate is too fast after the activity. The system automatically adjusts its exercise plan to a lower intensity walk and extends the rest time between each exercise. The patient's exercise pattern is monitored in real time through the accelerometer and gyroscope of the portable device. Combined with heart rate and cadence, a machine learning model is used to predict whether the patient is in a state of exercise. When it is predicted that the patient is in a state of exercise, the device automatically triggers the Bluetooth communication function to further query the safety of the patient's environment. For example, the smart bracelet worn by a patient monitors a high cadence and an increase in heart rate for 5 consecutive minutes, predicting that the patient is walking fast, and the device starts the Bluetooth communication function. The portable device scans other user terminal devices within a preset range (such as within 10 meters) through the Bluetooth communication protocol. Check whether there are other Bluetooth devices (such as smartphones, headphones, etc.) to indirectly determine whether the patient is accompanied by someone or in a public environment. Preset range and parameters: The scanning range is related to the patient's exercise type, for example: brisk walking or running: the range can be set to 5-10 meters. Static state: reduced to 1-2 meters. For example, when the patient is walking, the Bluetooth scan finds that there are no other Bluetooth devices around, which may indicate that the patient is in an unmanned environment. If the Bluetooth scan does not find other devices, the system determines that the patient may be in an unmanned environment and generates a safety reminder message. It can be a stop exercise suggestion: remind the patient to stop exercising to reduce the safety risks caused by being unaccompanied. It can also be a move suggestion: recommend the patient to go to an environment with people, especially when exercising at night or in remote areas. The prompt message can be conveyed to the patient through the screen of the portable device, voice broadcast or vibration reminder. At the same time, if the patient is bound to the device of a family member or medical staff, the prompt message can also be sent to the family member at the same time. A patient was walking fast at night, and the Bluetooth scan did not detect any surrounding devices. The system generated a prompt message: "You are currently in an unmanned environment. It is recommended to stop exercising or go to a place with someone." The rehabilitation plan is dynamically adjusted based on real-time evaluation results to ensure the effectiveness of exercise rehabilitation and the adaptability of the patient. The Bluetooth communication function is used to detect the environment, identify potential risks, provide patients with timely safety advice, and reduce the incidence of accidents. Through automatic monitoring, prediction, and prompts by the equipment, manual intervention is reduced, and the convenience and timeliness of rehabilitation management are improved. The patient's behavioral data is combined with environmental factors for evaluation to provide more comprehensive rehabilitation management.

[0059] In some examples, during the postoperative rehabilitation of aortic dissection, big data analysis technology is used to combine the patient's real-time location and exercise time to recommend the safest exercise path for the patient, ensuring that the patient is always in an environment with someone accompanying him. This solution not only improves the safety of the patient's rehabilitation exercise, but also creates a sense of security, helps patients overcome psychological barriers, and more actively participate in rehabilitation exercises.

[0060] It is understandable that the crowd distribution data can come from public platforms (such as map services, urban transportation systems) or third-party data providers. Data types can include crowd density distribution in different time periods and different areas. Environmental feature data can include road types (sidewalks, parks, urban roads); lighting conditions (nighttime lighting distribution); safety information (such as monitoring coverage). Analyze crowd data by time period (such as morning, afternoon, and evening) to extract regular distribution patterns. Divide the city or target area into several grids and mark the safety level of each grid (such as crowd flow and lighting conditions). Update data in real time to reflect the actual crowd distribution at the current time point. Use the positioning function of the patient's portable mobile terminal device to obtain the patient's current location in real time. Combined with the time point, query the real-time crowd density distribution near the location. It can include fixed target points, that is, the target location set by the patient in the rehabilitation plan (such as parks near home, fitness trails). It can also include dynamic target points, that is, the end point of the movement recommended by the system based on the current time and environment to ensure safety. Multi-objective optimization algorithms can be used for path planning. For example, goal 1: high crowd density along the path to ensure that the patient is always in a human environment. Goal 2: The path length is reasonable and in line with the patient's rehabilitation exercise plan. Goal 3: The environment is highly safe, such as giving priority to paths with lighting at night. The safety score of each candidate path is calculated based on big data, and the safety score = q 1 ·Crowd density + q 2 Environmental safety +q 3· Path convenience, where q is a weight parameter that can be adjusted according to the patient's condition. The system selects the path with the highest score as the recommended path and presents the path visually to the patient. During exercise, the patient's position and environmental changes along the way are monitored in real time, and the path is replanned if necessary. The recommended path is presented in the form of voice, text or map navigation. Dynamically remind the patient to follow a safe path and prompt the environmental features that are about to be entered (such as high-density areas or open spaces). When a low-density area suddenly appears along the path (such as environmental changes, abnormal weather leading to reduced traffic), the system promptly reminds the patient to adjust the direction or terminate the exercise. If the patient deviates from the recommended path, the system issues a warning and suggests replanning the path or returning to the original route. Therefore, through real-time crowd monitoring and safe path recommendation, the potential safety risks of patients during exercise are reduced. Dynamic environmental monitoring and safety prompts allow patients to participate in rehabilitation exercises with greater confidence, which is especially important for patients with anxiety or fear. Combined with the patient's location, time and rehabilitation goals, highly personalized path recommendations and dynamic adjustments are provided. By providing clear and safe exercise suggestions, patients are more willing to accept and adhere to exercise rehabilitation plans.

[0061] In some examples, the exercise rehabilitation program includes exercise type recommendations, the exercise type includes a fixed-area exercise type, and the method further includes:

[0062] The portable mobile terminal device carried by the patient obtains the current location information of the patient;

[0063] Query the location distribution information of medical institutions or AED equipment recorded on the network;

[0064] Based on the current location information and the location distribution information of the medical institution or AED equipment, an exercise area is recommended for the patient.

[0065] It is understandable that in the rehabilitation after aortic dissection surgery, exercise safety is an important consideration in formulating exercise rehabilitation programs. By integrating the patient's real-time location, medical institutions or AED equipment (automatic external defibrillator) location distribution information, more suitable fixed-area exercise types can be recommended for patients to ensure that patients are always within the coverage of medical support during rehabilitation exercises.

[0066] Exemplarily, the patient's geographic location information is obtained in real time through the GPS function of a portable mobile terminal device (such as a smart bracelet or mobile phone). Outdoor environments are based on precise GPS positioning. Indoor environments combine Wi-Fi signals and Bluetooth beacons to achieve auxiliary positioning. Update the patient's location information in real time, especially during the patient's movement, to ensure the continuity of location tracking. Obtain the location information of nearby hospitals, clinics and other medical institutions from public databases (such as government health departments or medical service platforms). Obtain the location of AED equipment installed in public places (such as parks, shopping malls, and schools) from emergency rescue platforms or community management agencies. Each location point contains the following information: institution / equipment name, specific address and coordinates, and available time period (such as working hours of medical institutions, available time period of AED equipment). Synchronize the location database regularly to ensure that the information is accurate and reliable. Priority is given to sports areas close to medical institutions or AED equipment to ensure that patients can quickly obtain medical assistance in an emergency. Consider environmental characteristics, such as lighting conditions, monitoring coverage, and completeness of public facilities. The recommended area should be suitable for the patient's current exercise type needs at the rehabilitation stage (such as walking, jogging in the park, etc.). Based on the patient's current location information, calculate the straight-line distance and actual path distance between the patient and the medical institution or AED device. Set recommendation thresholds, for example: Medical institution: The exercise area is no more than 1 km away from the medical institution. AED equipment: The exercise area is no more than 500 meters away from the AED device. Recommend multiple exercise areas that meet the thresholds, and further optimize the recommendations based on patient preferences. Dynamically adjust exercise intensity recommendations based on the patient's rehabilitation stage and behavioral data (such as heart rate, number of steps). For example, in the early stage of rehabilitation: walking or light jogging is recommended, and the time is controlled within 20-30 minutes. In the middle and late stages of rehabilitation: moderate-intensity exercise is recommended, such as long-distance brisk walking or jogging. In this way, ensure that the patient's exercise area is close to the medical institution or AED device to reduce the risk of sudden exercise. Dynamically recommend exercise areas and types based on the patient's location, time and rehabilitation goals, and provide more practical exercise recommendations. Utilize the automatic positioning and data query functions of portable devices, without the need for patients to manually set, greatly improving the user experience. On the basis of ensuring safety, help patients overcome psychological barriers and participate more actively in sports rehabilitation.

[0067] In some examples, the motion type includes a moving area motion type, and the method further includes:

[0068] Based on the current position information and the position distribution information of the AED devices, a recommended movement path is planned for the patient.

[0069] It is understandable that the GPS coordinates of the patient are obtained in real time using a portable mobile terminal device (such as a smartphone or a wristband). Ensure that the positioning accuracy meets the requirements of motion path planning (the error is controlled within 10 meters). Query the location information of the AED devices around the patient in real time from the public database or medical service platform, including: device coordinates, distance information (straight-line distance from the patient's current location information), and usage status (available / under maintenance / unavailable). During the exercise, regularly refresh the AED device distribution data to ensure that the path planning is always based on the latest data. Give priority to paths that can cover multiple AED devices to ensure that the patient is always within the emergency medical support range during the exercise. Set the coverage radius, for example: at least one AED device covers every 500 meters. There is no area in the path that is not covered by the device for more than 500 meters. The total length of the path is adjusted according to the patient's rehabilitation stage, for example: Early rehabilitation: recommended length 1-2 kilometers. Mid-rehabilitation: recommended length 3-5 kilometers. Ensure that the path design meets the patient's current exercise goals (such as continuous exercise duration, number of steps, etc.). Give priority to environmentally friendly areas such as parks and open roads, and avoid remote sections or complex road conditions. The path planning model is constructed by comprehensively considering safety, path length and movement goals. Combined with the patient's real-time movement position, the path planning is adjusted in real time. If the patient deviates from the recommended path, the system recalculates the optimal path. If the environmental data is updated (such as an AED device is unavailable), the path is dynamically adjusted to cover other devices. The planned movement path is intuitively presented to the patient in the form of a map, including: the starting point and the end point, the location and distance of the AED devices along the way, and the recommended exercise intensity and duration. For example, the patient's current location is displayed. The path is a circular path passing through 3 AED devices, and the recommended exercise time is 30 minutes. Real-time navigation prompts guide patients to exercise along the recommended path. Dynamic voice or vibration prompts can be provided, such as reminders when passing an AED device. Warnings are issued when deviating from the path or entering an area without device coverage.

[0070] In some examples, the rehabilitation program also includes a lifestyle rehabilitation program, and the above method also includes: obtaining the user's arm posture and movement frequency based on the wearable terminal device, and evaluating whether the user has smoking behavior based on the arm posture and movement frequency.

[0071] It is understandable that lifestyle rehabilitation is an important part of the postoperative rehabilitation of aortic dissection, especially smoking cessation, which is crucial to reduce the risk of postoperative recurrence and improve prognosis. By real-time monitoring of the patient's arm posture and movement frequency through wearable terminal devices, it is possible to assess whether the patient has smoking behavior, thereby providing a scientific basis for lifestyle intervention.

[0072] Exemplarily, the sensor function of the wearable device may include an inertial measurement unit (IMU) sensor, such as an accelerometer, a gyroscope, and a magnetometer, for real-time detection of the motion state of the user's arm. The data acquisition frequency can be set to 10-50 times per second to ensure the accuracy of motion capture. The spatial angle and movement trajectory of the arm are obtained through the accelerometer and gyroscope. The key posture feature may be that the arm is raised and close to the mouth. Through the action cycle analysis (such as the frequency of raising-lowering), the repetitive movements of the hand and the mouth are judged. Smoking usually manifests itself in the following specific patterns: the arm is raised close to the mouth: the arm is raised from a drooping state to near the mouth. Maintaining the posture for a short time: the arm stays near the mouth for a few seconds (smoking action). The arm is lowered to the initial position: the arm returns to the drooping state. Repeated action: the whole process is cycled at a certain frequency (for example, 6-10 times per minute). Features of the arm motion trajectory: a small range of reciprocating motion trajectory is formed with the mouth as the center. The angular velocity, acceleration, and displacement of the arm are analyzed through sensor data. Use machine learning models (such as random forests or support vector machines) to extract action features and identify smoking behavior. Behavior classification: The key features that distinguish smoking actions from other similar actions (such as drinking water and eating) are the action cycle and the position of the hand. Analyze the user's daily action data to evaluate the frequency and duration of smoking behavior. The data results include: the number of cigarettes smoked per day, the duration of a single smoking session, and the total smoking duration and frequency trend. Based on the collected arm motion data, match it with the smoking behavior pattern: if multiple consecutive actions meet the characteristics of smoking and have a typical frequency and trajectory, it is determined to be a smoking behavior. Filter out false positives (such as drinking water or other behaviors). The analysis results are fed back to the user through the mobile application of the wearable device. For example, a reminder message is generated: "It is detected that you may have smoking behavior today. Please pay attention to controlling tobacco use to promote recovery." Data visualization can also be performed to show the number of daily smoking times and trends in the form of charts.

[0073] In some examples, the rehabilitation program also includes a lifestyle rehabilitation program, and the above method also includes: obtaining the user's arm posture, movement frequency and heart rate based on the wearable terminal device, and evaluating whether the user has drinking behavior based on the arm posture and movement frequency.

[0074] Exemplarily, the drinking action features include arm raising, where the arm is raised from a naturally drooping or tabletop position and close to the mouth. Staying action, the hand stays near the mouth for 1-3 seconds (simulating the drinking action). The arm is lowered, and the arm returns to the initial position after the action is completed. Repeating cycle, the above action is repeated in a short time. The drinking action trajectory is characterized by a fixed trajectory from the bottom to the mouth with a relatively small amplitude. The repeatability of the action path is high. Drinking behavior is usually accompanied by heart rate changes to facilitate the distinction from drinking behavior: the heart rate increases in the early stage of drinking (more than 10% increase over the resting heart rate). In the later stage of drinking, there may be a trend of unstable heart rate fluctuations. Joint analysis of heart rate and action features can significantly improve the accuracy of drinking behavior detection. Extracting key features from arm movements can include: the amplitude and angle of arm raising, the length of stay and the fixedness of the trajectory, and the action cycle and frequency. Use machine learning algorithms such as support vector machines (SVM) to distinguish drinking behavior from other similar actions (such as drinking water and eating). The action classification model combined with heart rate data can further improve the ability to distinguish. Therefore, combined with arm movements and heart rate characteristics, drinking behavior can be effectively distinguished from other similar actions, improving detection accuracy. The system can provide instant reminders when drinking behavior occurs, helping patients intervene in behavior at an early stage to avoid negative impacts on recovery. By continuously monitoring drinking behavior, providing trend reports and personalized guidance, it is helpful to formulate a scientific alcohol cessation plan. Through timely intervention in drinking behavior, the risk of postoperative recurrence or complications caused by alcohol intake is reduced. With the automatic monitoring function of wearable devices, it does not interfere with patients' daily lives and increases patient compliance.

[0075] See also Figure 2 , an embodiment of the rehabilitation management device for patients after aortic dissection surgery in the embodiment of the present application may include:

[0076] An acquisition unit 21 is used to acquire the patient's behavior data using a portable mobile terminal device carried by the patient after aortic dissection surgery;

[0077] A determination unit 22, configured to determine the rehabilitation assessment supplementary data based on the rehabilitation assessment theoretical data and the acquired behavior data, so as to generate an assessment questionnaire according to the rehabilitation assessment supplementary data;

[0078] The evaluation unit 23 is used to perform a rehabilitation evaluation on the patient according to the feedback data of the patient in response to the evaluation questionnaire and the behavior data.

[0079] In summary, the rehabilitation management device for patients after aortic dissection surgery provided in the embodiment of the present application obtains the patient's behavioral data by utilizing the portable mobile terminal device carried by the patient after aortic dissection surgery; determines the rehabilitation assessment supplementary data based on the rehabilitation assessment theoretical data and the acquired behavioral data, so as to generate an assessment questionnaire based on the rehabilitation assessment supplementary data; and conducts a rehabilitation assessment on the patient based on the patient's feedback data in response to the assessment questionnaire and the behavioral data. Thus, real-time data collection through portable devices makes up for the shortcomings of traditional intermittent assessments and realizes continuous monitoring of the patient's dynamic changes. Behavioral data provides objective assessments, and questionnaire feedback supplements subjective experience to form a complete assessment chain. Based on the patient's behavioral data and feedback, the assessment plan is dynamically adjusted to achieve personalized health management. Medical staff can quickly identify high-risk patients based on the assessment report, adjust the follow-up frequency and intervention measures, and reduce the risk of recurrence and complications.

[0080] like Figure 3 As shown, the embodiment of the present application further provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any one of the above-mentioned methods for rehabilitation management of patients after aortic dissection surgery are implemented:

[0081] The patient's behavioral data is obtained by using the portable mobile terminal device carried by the patient after aortic dissection surgery;

[0082] Determining rehabilitation assessment supplementary data based on the rehabilitation assessment theoretical data and the acquired behavioral data, so as to generate an assessment questionnaire according to the rehabilitation assessment supplementary data;

[0083] A rehabilitation assessment is performed on the patient based on the patient's feedback data in response to the assessment questionnaire and the behavioral data.

[0084] Since the electronic device introduced in this embodiment is a device used to implement a rehabilitation management device for patients after aortic dissection surgery in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application will not be described in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0085] In the specific implementation process, when the computer program 311 is executed by the processor, it can achieve Figure 1 Any implementation method in the corresponding embodiment:

[0086] The patient's behavioral data is obtained by using the portable mobile terminal device carried by the patient after aortic dissection surgery;

[0087] Determining rehabilitation assessment supplementary data based on the rehabilitation assessment theoretical data and the acquired behavioral data, so as to generate an assessment questionnaire according to the rehabilitation assessment supplementary data;

[0088] A rehabilitation assessment is performed on the patient based on the patient's feedback data in response to the assessment questionnaire and the behavioral data.

[0089] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0090] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0091] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0094] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 The process of rehabilitation management for patients after aortic dissection surgery in the corresponding embodiment.

[0095] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0096] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0097] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0098] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0099] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0100] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0101] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for rehabilitation management of patients after aortic dissection surgery, characterized in that: include: Using portable mobile terminal devices carried by patients after aortic dissection surgery to obtain patients' behavioral data; Determining rehabilitation assessment supplementary data based on the rehabilitation assessment theoretical data and the acquired behavioral data, so as to generate an assessment questionnaire according to the rehabilitation assessment supplementary data; A rehabilitation assessment is performed on the patient based on the patient's feedback data in response to the assessment questionnaire and the behavioral data.

2. The method according to claim 1, characterized in that The step of performing rehabilitation assessment on the patient based on the patient's feedback data in response to the assessment questionnaire and the behavioral data comprises: constructing a digital phenotype of the patient based on the patient's feedback data in response to the assessment questionnaire and the behavioral data; The patient is assessed for rehabilitation based on the digital phenotype.

3. The method according to claim 1 or 2, characterized in that Also includes: A rehabilitation program is adjusted based on the rehabilitation assessment results, wherein the rehabilitation program includes an exercise rehabilitation program.

4. The method according to claim 3, characterized in that Also includes: In the case where it is predicted based on the patient's behavior data that the patient is in a motion state, turning on a Bluetooth communication function of a portable mobile terminal device carried by the patient; Based on the Bluetooth communication function, query other user terminal devices within the preset range of the patient; In the case that there are no other user terminal devices within the preset range of the patient, an exercise prompt message is generated, wherein the exercise prompt message includes a suggestion to stop exercising or to move to an environment with people.

5. The method according to claim 4, characterized in that The exercise rehabilitation program includes exercise type suggestions, and the exercise type includes a fixed-area exercise type. The method further includes: The portable mobile terminal device carried by the patient obtains the current location information of the patient; Query the location distribution information of medical institutions or AED equipment recorded on the network; Based on the current location information and the location distribution information of the medical institution or AED equipment, an exercise area is recommended for the patient.

6. The method according to claim 5, characterized in that The motion type includes a moving area motion type, and the method further includes: Based on the current position information and the position distribution information of the AED devices, a recommended movement path is planned for the patient.

7. The method according to claim 1, characterized in that Also includes: The rehabilitation assessment result is sent to the medical terminal associated with the patient.

8. A rehabilitation management device for patients after aortic dissection surgery, characterized in that: include: An acquisition unit, used for acquiring the patient's behavior data by using a portable mobile terminal device carried by the patient after aortic dissection surgery; A determination unit, configured to determine the rehabilitation assessment supplementary data based on the rehabilitation assessment theoretical data and the acquired behavior data, so as to generate an assessment questionnaire according to the rehabilitation assessment supplementary data; An evaluation unit is used to perform a rehabilitation evaluation on the patient based on the feedback data of the patient in response to the evaluation questionnaire and the behavior data.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the method for rehabilitation management of patients after aortic dissection surgery as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for rehabilitation management of patients after aortic dissection surgery as described in any one of claims 1-7 is implemented.

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