Medical assistance method and device based on blood pressure detection and electronic equipment

Abstract

The invention discloses a medical assistance method and device based on blood pressure detection and electronic equipment. The method comprises the steps that blood pressure data, blood pressure complication data and operation risk triangular data of a user are obtained; performing data integration on the blood pressure data, the blood pressure complication data and the surgical risk triangular data to generate a blood pressure risk level of the user; determining an ambulatory blood pressure threshold range of the user based on the blood pressure risk level, and performing blood pressure management on the user according to the ambulatory blood pressure threshold range; due to the fact that multi-dimensional information such as user blood pressure data, blood pressure complication data and operation risk triangular data is integrated, the generated blood pressure risk level can reflect the individual blood pressure risk condition more comprehensively and accurately; the ambulatory blood pressure threshold range determined based on the risk level can better fit the actual risk level of the user, so that blood pressure management measures are more personalized and targeted, the accuracy and effectiveness of blood pressure control are effectively improved, and the risk of improper management caused by individual differences is reduced.

Description

Technical Field

[0001] This invention relates to the field of medical assistive technology, specifically to a medical assistive method, device, and electronic device based on blood pressure detection. Background Technology

[0002] Perioperative hypotension is a major factor leading to insufficient perfusion of multiple organs, secondary damage, and death in patients. Perioperative hypertension increases stress, cerebrovascular accidents, and intraoperative bleeding. Individualized blood pressure management can effectively reduce the incidence of perioperative cardiac, cerebral, pulmonary, and renal injuries and complications.

[0003] However, current technologies for perioperative blood pressure management often rely on ward blood pressure monitoring. After admission, changes in the environment, diet, and sleep patterns, coupled with stress and anxiety, make it difficult for ward blood pressure monitoring to accurately assess a patient's daily blood pressure. Furthermore, different patients have different comorbidities and undergo different surgeries, resulting in varying ranges of blood pressure regulation. Currently used blood pressure monitoring and management protocols are ill-suited to the differences between individuals undergoing different surgeries, leading to excessive perioperative blood pressure fluctuations and potentially causing cardiovascular and cerebrovascular complications.

[0004] Therefore, existing technologies suffer from low accuracy in blood pressure management. Summary of the Invention

[0005] This invention provides a medical assistance method, device, and electronic device based on blood pressure detection, aiming to solve the problem of low accuracy in blood pressure management and control in existing technologies.

[0006] To address the aforementioned technical problems, the embodiments of the present invention provide the following technical solutions: A medical assistance method based on blood pressure detection, comprising: Acquire users' blood pressure data, blood pressure comorbidity data, and surgical risk triangle data; The blood pressure data, the blood pressure comorbidity data, and the surgical risk triangle data are integrated to generate the user's blood pressure risk level. Based on the blood pressure risk level, the dynamic blood pressure threshold range of the user is determined, and the user's blood pressure is managed according to the dynamic blood pressure threshold range.

[0007] Optionally, the process of integrating the blood pressure data, the blood pressure comorbidity data, and the surgical risk triangle data to generate the user's blood pressure risk level includes: The blood pressure data, the blood pressure comorbidity data, and the surgical risk triangle data are respectively quantified and converted into scores to obtain blood pressure data scores, blood pressure comorbidity data scores, and surgical risk triangle data scores. The blood pressure data score, the blood pressure comorbidity data score, and the surgical risk triangle data score are weighted and fused to determine the user's blood pressure risk level.

[0008] Optionally, the blood pressure data includes the user's stable blood pressure data, deep sleep blood pressure data, morning wake-up blood pressure data, and blood pressure data at symptom triggers; the blood pressure data is quantified and converted into a score to obtain a blood pressure data score, including: The user's blood pressure fluctuation range is determined based on the deep sleep blood pressure data and the morning wake-up blood pressure data; Based on the blood pressure data at the time the symptoms were triggered and the range of blood pressure fluctuations, the abnormal blood pressure fluctuation index of the user at the time the symptoms were triggered was determined; Based on the stable blood pressure data and the blood pressure fluctuation range, determine the user's blood pressure stability index under stable conditions; The blood pressure data score is determined based on the abnormal blood pressure fluctuation index and the blood pressure stability index.

[0009] Optionally, the blood pressure comorbidity data includes the user's hypertension-related comorbidities and other systemic comorbidities; the blood pressure comorbidity data is quantified and converted into a score to obtain a blood pressure comorbidity data score, including: The first comorbidity score is determined based on the type and severity of the hypertension-related comorbidities data. The second comorbidity score is determined based on the correlation and degree of influence between the other systemic comorbidities and blood pressure; The blood pressure comorbidity score is obtained by weighted summing of the first comorbidity score and the second comorbidity score.

[0010] Optionally, the surgical risk triangle data includes surgical type, surgical duration, and potential blood loss; the surgical risk triangle data is quantified and converted into a score to obtain a surgical risk triangle data score, including: A surgical risk score is determined based on the risk level of the surgical type. A surgical duration score is determined based on the length of the surgical procedure. A surgical bleeding score is determined based on the magnitude of the potential bleeding. The surgical risk score, the surgical duration score, and the surgical bleeding score are weighted and calculated to obtain the surgical risk triangular data score.

[0011] Optionally, determining the user's dynamic blood pressure threshold range based on the blood pressure risk level includes: The maximum fluctuation value of the user is determined based on the blood pressure risk level; Based on the maximum fluctuation value and the user's optimal blood pressure value, determine the user's dynamic blood pressure threshold range; The optimal blood pressure value includes at least one of the user's stable blood pressure value and historical average blood pressure value.

[0012] Optionally, the process of managing the user's blood pressure based on the dynamic blood pressure threshold range further includes: Obtain the user's new symptom data; The ambulatory blood pressure threshold range is adjusted based on the newly added symptom data, and blood pressure management is performed on the user based on the adjusted ambulatory blood pressure threshold range.

[0013] Optionally, managing the user's blood pressure based on the dynamic blood pressure threshold range includes: When the user's real-time blood pressure value exceeds the dynamic blood pressure threshold range, an alarm signal is issued and / or alarm information is uploaded.

[0014] A medical assistive device based on blood pressure detection, comprising: The data acquisition module is used to acquire users' blood pressure data, blood pressure comorbidity data, and surgical risk triangle data; The blood pressure risk level generation module is used to integrate the blood pressure data, the blood pressure comorbidity data, and the surgical risk triangle data to generate the user's blood pressure risk level. The blood pressure management module is used to determine the dynamic blood pressure threshold range of the user based on the blood pressure risk level, and to manage the user's blood pressure according to the dynamic blood pressure threshold range.

[0015] An electronic device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps: Acquire users' blood pressure data, blood pressure comorbidity data, and surgical risk triangle data; The blood pressure data, the blood pressure comorbidity data, and the surgical risk triangle data are integrated to generate the user's blood pressure risk level. Based on the blood pressure risk level, the dynamic blood pressure threshold range of the user is determined, and the user's blood pressure is managed according to the dynamic blood pressure threshold range.

[0016] In this embodiment, by integrating multi-dimensional information such as user blood pressure data, blood pressure comorbidity data, and surgical risk triangle data, the generated blood pressure risk level can more comprehensively and accurately reflect the individual's blood pressure risk status. The dynamic blood pressure threshold range determined based on this risk level can better match the user's actual risk level, thereby making blood pressure management measures more personalized and targeted, effectively improving the accuracy and effectiveness of blood pressure control, and reducing the risk of mismanagement due to individual differences. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this embodiment, the accompanying drawings used in the description of the embodiment will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram illustrating a scenario of an embodiment of the medical auxiliary system based on blood pressure detection provided by the present invention. Figure 2 A schematic diagram illustrating another embodiment of the medical assistance system based on blood pressure detection provided by the present invention; Figure 3 This is a flowchart illustrating an embodiment of the medical assistance method based on blood pressure detection provided by the present invention. Figure 4 This is a schematic diagram illustrating the result of an embodiment of data integration provided by the present invention; Figure 5 This is a schematic diagram of an embodiment of the medical auxiliary device based on blood pressure detection provided by the present invention; Figure 6 This is a schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0019] The technical solutions in this embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the following description, specific embodiments of the invention will be illustrated with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be referred to several times as being performed by a computer, and computer execution as referred to herein includes operations by a computer processing unit representing electronic signals of data in a structured format. This operation transforms the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise alter the operation of the computer in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the invention described above are not intended to be limiting, and those skilled in the art will understand that many of the steps and operations described below can also be implemented in hardware.

[0021] The terms "module" or "unit" as used herein can be considered as software objects executing on the computing system. The different components, modules, engines, and services described herein can be considered as implementation objects on the computing system. The apparatus and methods described herein are preferably implemented in software, but can also be implemented in hardware, both of which are within the scope of this invention.

[0022] This invention provides a medical assistance method, device, and electronic device based on blood pressure detection.

[0023] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an embodiment of the blood pressure detection-based medical assistance system provided by the present invention. The system may include a client 100 and a server 200, which are connected via a network. The server 200 integrates a blood pressure detection-based medical assistance device. The server 200 may be a work platform server (i.e., a server loaded with a work platform), such as... Figure 1 In this embodiment, the server 200 is accessed by the client 100. The server 200 is primarily used to acquire the user's blood pressure data, blood pressure comorbidity data, and surgical risk triangle data; to integrate the blood pressure data, blood pressure comorbidity data, and surgical risk triangle data to generate the user's blood pressure risk level; to determine the user's dynamic blood pressure threshold range based on the blood pressure risk level; and to manage the user's blood pressure according to the dynamic blood pressure threshold range.

[0024] In this embodiment, the server 200 can be a standalone server, a server network, or a server cluster. For example, the server 200 described in this embodiment includes, but is not limited to, computers, network hosts, single network servers, multiple network server sets, or cloud servers composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing. In this embodiment, communication between the server and the client can be achieved through any communication method, including but not limited to mobile communication based on the 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE), and Worldwide Interoperability for Microwave Access (WiMAX), or computer network communication based on the TCP / IP Protocol Suite (TCP / IP) and User Datagram Protocol (UDP).

[0025] It is understood that the client 100 used in this embodiment can be understood as a client device. A client device includes both receiving and transmitting hardware, that is, a device with receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a client device may include: cellular or other communication devices, having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the client 100 may be a desktop terminal or a mobile terminal, specifically a mobile phone, tablet computer, laptop computer, etc.

[0026] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include those that are more specific to this application. Figure 1 The number of more or fewer servers shown, or the server network connectivity relationships, for example... Figure 1 Only one server and two clients are shown in the diagram. It is understood that the blood pressure monitoring-based medical assistance system may also include one or more other servers, and / or one or more clients connected to the server network, which is not limited here.

[0027] In some embodiments of the present invention, the working platform may be an enterprise office platform, such as WeChat for Business. Taking server 200 as an example, it may further include an enterprise office platform contact server, an enterprise office platform configuration management server, and a web management server. Enterprise users or developers can access the web management server using a web browser terminal to configure the field configuration information on the enterprise office platform configuration management server, and set and store the enterprise user information of enterprise employees of the enterprise office platform on the enterprise office platform contact server.

[0028] In addition, such as Figure 2 As shown, Figure 2 This is a schematic diagram of another embodiment of the blood pressure detection-based medical auxiliary system provided by the present invention. The blood pressure detection-based medical auxiliary system may also include a storage terminal 300 for storing data, such as a storage object database. The object database stores object data, which may include application templates (such as approval templates, attendance templates, and other application templates), file data (such as Word files, Excel files, or PPT files in various formats), image data (such as images in various formats such as jpg, png, and bmp), and other types of data. Correspondingly, the object database may also be divided into multiple types of data, such as an application database, a file database, or an image database.

[0029] It should be noted that, Figure 1-2 The illustrated scenario diagram of the blood pressure detection-based medical assistance system is merely an example. The blood pressure detection-based medical assistance system and scenario described in the embodiments of the present invention are intended to more clearly illustrate the technical solutions of the embodiments of the present invention and do not constitute a limitation on the technical solutions provided by the present invention. As those skilled in the art will know, with the evolution of blood pressure detection-based medical assistance systems and the emergence of new business scenarios, the technical solutions provided by the present invention are also applicable to similar technical problems.

[0030] The following detailed description is based on specific embodiments.

[0031] In this embodiment, the description will be from the perspective of a blood pressure detection-based medical assistive device, which can be integrated into the server 200.

[0032] This invention provides a medical assistance method based on blood pressure detection. Please refer to [link / reference]. Figure 3 , Figure 3 A flowchart illustrating an embodiment of the medical assistance method based on blood pressure detection provided by the present invention includes: S301: Obtain the user's blood pressure data, blood pressure comorbidity data, and surgical risk triangle data; In one specific embodiment, the user can be a patient, a test subject, or a medical professional. The user's data can be collected through wearable devices, hospital information systems, or manual entry, or other methods, which are not limited here.

[0033] Comorbidity data specifically refers to conditions that a user has that may affect blood pressure values ​​or cause changes in blood pressure control requirements, such as hypertension, coronary heart disease, cerebral infarction, and other vascular-related diseases.

[0034] S302: Integrate blood pressure data, comorbidity data, and surgical risk triangle data to generate the user's blood pressure risk level; In one specific embodiment, data integration refers to the unified collection and standardized processing of all user data to obtain a personalized blood pressure risk assessment result for that user. All blood pressure risks can be divided into different levels, such as low risk, medium risk, and high risk, presenting the user's blood pressure health status in a quantitative form. That is, blood pressure risk can be normalized and integrated into multiple levels, each level corresponding to different risk management strategies and intervention measures. For example, a low-risk level may simply require regular blood pressure measurement and a healthy lifestyle; a medium-risk level may recommend increasing the monitoring frequency and having a doctor regularly evaluate whether medication intervention is needed; a high-risk level requires real-time blood pressure monitoring and immediately triggering an early warning mechanism when abnormal data is detected, etc., without further limitation.

[0035] A user's blood pressure risk level refers to the risk level that corresponds to the user's current health status, which is determined by the targeted integration of blood pressure data, blood pressure comorbidity data, and surgical risk triangle data, among multiple generalized levels of blood pressure risk. In other words, a user's blood pressure risk level reflects the user's relative level in big data statistics.

[0036] S303: Determine the user's dynamic blood pressure threshold range based on blood pressure risk level, and manage the user's blood pressure according to the dynamic blood pressure threshold range.

[0037] In one specific embodiment, the dynamic blood pressure threshold range refers to the upper and lower limits of blood pressure determined based on the user's current blood pressure risk level. The dynamic blood pressure threshold range is not only related to the user's blood pressure risk level, but also to the user's own physical characteristics and status (such as gender and age). Therefore, the dynamic blood pressure threshold range will be adjusted in real time as the user's health status changes, which not only ensures the user-specificity of blood pressure management, but also ensures the accuracy and adaptability of the management strategy.

[0038] In this embodiment, by integrating multi-dimensional information such as user blood pressure data, comorbidity data, and surgical risk triangle data, the generated blood pressure risk level can more comprehensively and accurately reflect the individual's blood pressure risk status. The dynamic blood pressure threshold range determined based on this risk level can better match the user's actual risk level, thereby making blood pressure management measures more personalized and targeted, effectively improving the accuracy and effectiveness of blood pressure control, and reducing the risk of mismanagement due to individual differences.

[0039] In one specific embodiment, in S301, the user's blood pressure data specifically includes the user's daily blood pressure data, such as blood pressure at times when falling asleep, deep sleep, and waking up in the morning, and can also be obtained through dynamic blood pressure values ​​continuously collected by wearable devices, thereby obtaining the user's blood pressure fluctuation characteristics at different physiological states and different time points.

[0040] It is important to emphasize that the user's blood pressure data contains basic information such as gender and age. That is, this application does not limit itself to specifically obtaining basic physiological parameters such as gender and age of users, but rather indirectly reflects individual differences through the distribution characteristics and variation patterns of blood pressure data, thereby achieving accurate risk stratification without the need to collect additional personal information.

[0041] Blood pressure comorbidity data specifically includes whether the user has underlying diseases that affect blood pressure control, such as diabetes, chronic kidney disease, or cardiovascular disease, as well as clinical symptoms (dizziness, chest tightness, syncope, etc.) to determine whether the user is at risk of blood pressure abnormalities, and then to manage them in a targeted manner and achieve personalized intervention.

[0042] The surgical risk triangle data specifically includes the type of surgery to be performed, the duration of the surgery, and the potential amount of bleeding. By analyzing and evaluating the surgical risk triangle data, the extent to which the surgery affects the user's blood pressure fluctuations can be determined, thereby predicting the risk of abnormal blood pressure during and after surgery. In other words, by comprehensively analyzing the surgery, the safe blood pressure range that needs to be maintained during or after the surgery can be determined, thus providing users with better preoperative, intraoperative, and postoperative blood pressure management strategies.

[0043] In one specific embodiment, in S302, blood pressure data, blood pressure comorbidity data, and surgical risk triangle data are integrated to generate the user's blood pressure risk level. This includes: quantifying and converting the blood pressure data, blood pressure comorbidity data, and surgical risk triangle data into scores, respectively, to obtain blood pressure data scores, blood pressure comorbidity data scores, and surgical risk triangle data scores; and weighting and fusing the blood pressure data scores, blood pressure comorbidity data scores, and surgical risk triangle data scores to determine the user's blood pressure risk level.

[0044] It should be noted that during the process of converting quantitative scores, the score of each data point is determined based on its correlation with blood pressure risk. The stronger the correlation, the more likely the indicator is to cause abnormal blood pressure, and the higher its corresponding score.

[0045] Specifically, blood pressure data includes the user's stable blood pressure data, deep sleep blood pressure data, morning wake-up blood pressure data, and blood pressure data at symptom triggers. The blood pressure data is quantified and converted into a score, including: determining the user's blood pressure fluctuation range based on deep sleep and morning wake-up blood pressure data; determining the user's abnormal blood pressure fluctuation index at symptom triggers based on the blood pressure data and blood pressure fluctuation range; determining the user's blood pressure stability index under stable conditions based on stable blood pressure data and blood pressure fluctuation range; and determining the blood pressure score based on the abnormal blood pressure fluctuation index and the blood pressure stability index.

[0046] It should be noted that blood pressure data during deep sleep is generally lower and can represent the user's lowest blood pressure value, while blood pressure data upon waking in the morning is usually higher, reflecting the morning blood pressure peak and representing the user's highest blood pressure value (blood pressure may be even higher during exercise). Therefore, the blood pressure fluctuation range reflects the user's general blood pressure fluctuation range. In other embodiments, the most accurate blood pressure fluctuation range can be obtained by real-time monitoring of the user's blood pressure, or by statistical analysis of historical data, etc., which are not limited here.

[0047] Blood pressure data triggered by symptoms refers to the blood pressure value measured when a user experiences clinical symptoms such as dizziness, chest tightness, palpitations, or fainting. It is used to assess the correlation between symptoms and blood pressure fluctuations. If the blood pressure at the time of symptom trigger is significantly higher or lower than the blood pressure fluctuation range, it indicates a high correlation between the symptom and abnormal blood pressure fluctuations, requiring closer attention. In other words, the greater the deviation of the blood pressure data from the blood pressure fluctuation range at the time of symptom trigger, the higher the corresponding abnormal blood pressure fluctuation index, indicating a stronger correlation between the symptom and abnormal blood pressure.

[0048] Stable blood pressure data refers to the average blood pressure measured multiple times by a user under symptom-free and normal activity conditions, reflecting the user's baseline blood pressure level. The blood pressure stability index under stable conditions is calculated based on the deviation of the stable blood pressure data from the blood pressure fluctuation range. The smaller the deviation, the lower the blood pressure stability index, indicating more stable baseline blood pressure control and lower risk; conversely, the larger the deviation, the higher the blood pressure stability index, indicating drastic baseline blood pressure fluctuations and higher potential risk. In other embodiments, stable blood pressure data can also be a dynamic blood pressure data sequence over a period of time. By analyzing the distribution density and trend of this sequence within the blood pressure fluctuation range, blood pressure stability can be further quantified; the more concentrated the distribution and the smoother the fluctuations, the lower the stability index, indicating good baseline blood pressure control, but this is not limited here.

[0049] In this embodiment, the blood pressure fluctuation range of an individual user is determined by two physiologically consistent characteristic data: blood pressure during deep sleep and blood pressure upon waking in the morning. This lays an accurate foundation for subsequent quantitative analysis that aligns with individual physiological characteristics. An abnormal blood pressure fluctuation index is calculated based on the extent to which blood pressure exceeds this fluctuation range when symptoms are triggered. This accurately assesses the correlation between symptoms and abnormal blood pressure, helping to identify high-risk symptoms requiring increased attention. A blood pressure stability index is calculated by the degree of deviation between stable blood pressure and the fluctuation range. This effectively reflects the stability of the user's baseline blood pressure control (the smaller the deviation, the lower the stability index, indicating more stable baseline blood pressure control and lower risk), highlighting potential risks. Finally, a blood pressure score is obtained by combining the abnormal blood pressure fluctuation index (correlated with symptoms and abnormal blood pressure) and the blood pressure stability index (reflecting baseline blood pressure stability). This achieves a comprehensive quantification of the user's blood pressure status, providing an accurate and comprehensive decision-making basis for personalized blood pressure management interventions.

[0050] Furthermore, the blood pressure comorbidity data includes the user's hypertension-related comorbidities and other systemic comorbidities; the blood pressure comorbidity data is quantified and converted into a score to obtain a blood pressure comorbidity data score, including: determining the first comorbidity score based on the type and severity of the hypertension-related comorbidities; determining the second comorbidity score based on the correlation and influence of other systemic comorbidities on blood pressure; and weighted summing the first and second comorbidity scores to obtain the blood pressure comorbidity data score.

[0051] It should be noted that the data on hypertension-related comorbidities include diseases that are closely related to and influence blood pressure, such as diabetes, chronic kidney disease, and coronary heart disease. The more types of diseases there are and the more severe the condition, the higher the score for the first comorbidity.

[0052] Other systemic comorbidities, such as chronic respiratory diseases and thyroid dysfunction, are not directly classified as hypertension-related diseases, but they can indirectly interfere with blood pressure control by affecting metabolism, circulation, or autonomic nervous system function. Their corresponding scores are determined based on the degree of their potential impact on blood pressure regulation; the higher the degree of impact, the higher the score of the second comorbidity.

[0053] The blood pressure comorbidity score reflects the overall stress on the blood pressure regulation system caused by the burden of comorbidities. The higher the score, the greater the impact of comorbidities on the user's blood pressure and the higher the risk. Correspondingly, more precise protective measures are needed.

[0054] Finally, by weighted summing of the first and second comorbidity scores, a blood pressure comorbidity score is obtained. This not only allows for adaptive adjustment of the weighting coefficients of the first and second comorbidity scores according to actual needs to meet the needs of different users, but also enables precise quantification of the overall impact of comorbidities on blood pressure, thereby improving the accuracy of blood pressure risk assessment.

[0055] In this embodiment, by incorporating all blood pressure-related comorbidities into a unified scoring system, a comprehensive quantitative assessment of the user's blood pressure comorbidity data is achieved, thereby more accurately quantifying the risk of comorbidities to the user's blood pressure management.

[0056] Furthermore, the surgical risk triangle data includes surgical type, surgical duration, and potential blood loss. The surgical risk triangle data is quantified and converted into a score, which includes: determining the surgical risk score based on the risk level of the surgical type; determining the surgical duration score based on the length of the surgical duration; determining the surgical bleeding score based on the potential blood loss; and then weighting the surgical risk score, surgical duration score, and surgical bleeding score to obtain the surgical risk triangle data score.

[0057] It should be noted that surgical types include high-risk surgeries (such as cardiac surgery, major vascular surgery, etc.), medium-risk surgeries (such as abdominal surgery, major orthopedic surgery, etc.), and low-risk surgeries (such as superficial surgery, endoscopic examination, etc.). Different risk levels correspond to different surgical risk scores, and the higher the risk level, the higher the surgical risk score.

[0058] Surgical duration refers to the total time taken from start to finish of a surgery. The longer the surgery, the greater the corresponding risk and the higher the level of attention required. Surgical duration score is a quantitative value of surgical risk determined based on the surgical duration. Specifically, it is divided into intervals according to the duration of the surgery. The longer the surgery, the higher the surgical duration score, reflecting a greater risk of intraoperative blood pressure fluctuations.

[0059] Potential bleeding volume refers to the volume of blood expected to be lost during surgery. Generally, the greater the bleeding volume, the more significant the impact on the circulatory system and the greater the impact on blood pressure stability. Therefore, the greater the potential bleeding volume and the higher the surgical bleeding score, the more significant the impact on the user's blood pressure and the greater the risk.

[0060] The surgical risk triangle score is a comprehensive score obtained by systematically quantifying all blood pressure-related risk factors during surgery. It comprehensively reflects the overall stress level of surgery on blood pressure regulation. Clearly, a higher surgical risk triangle score indicates a greater impact of the surgery on the user's blood pressure, requiring greater attention.

[0061] Finally, in the process of weighting the surgical risk score, surgical duration score, and surgical bleeding score to obtain the surgical risk triangle data score, the weight coefficients of each scoring dimension can be flexibly adjusted according to actual needs to adapt to different surgical scenarios and personalized requirements.

[0062] In this embodiment, by quantifying and scoring the type of surgery, duration of surgery, and potential blood loss, the risk factors related to surgery and blood pressure are covered as comprehensively as possible. Furthermore, because the quantification process is not only objective but also closely integrated with the user's own data, the surgical risk triangle data score can truly reflect the user's risk status, thereby providing a more reliable basis for assessing blood pressure risk levels.

[0063] Please see Figure 4 , Figure 4 This diagram illustrates the results of an embodiment of data integration provided by the present invention. Specifically, a nomogram is used to evaluate various user indicators (including gender, age, normal blood pressure, presence of comorbidities, surgical grade, surgical duration, and blood loss; obviously, other parameters may be used in other embodiments, which are not limited here) based on a nomogram. Each indicator has a corresponding scoring range, and based on the user's specific data, the specific score can be determined within the corresponding scoring range. The scores of each indicator are then summed to obtain a comprehensive score. The nomogram visually presents the contribution of each indicator to the overall risk, facilitating doctors to quickly identify key influencing factors.

[0064] In one specific example, a 65-year-old female patient with a baseline blood pressure of 120 / 65 mmHg and preoperative comorbidities is scheduled for an ASA grade 3 surgery. The estimated surgery time is 60 minutes, and the estimated blood loss is 300 ml. Based on the Nomo chart, her total score is 160 points, indicating a 68% probability of perioperative hypotension / adverse reactions. Blood pressure control should be maintained within a 20-30% fluctuation range from the baseline blood pressure of 120 / 60 mmHg. Clearly, this approach can effectively guide the patient's perioperative blood pressure management strategy, ensuring that intraoperative blood pressure is maintained within a safe range.

[0065] After obtaining the blood pressure data score, blood pressure comorbidity data score, and surgical risk triangle data score, the three are weighted and integrated to obtain a comprehensive blood pressure risk score, thereby accurately determining the user's blood pressure risk level for subsequent blood pressure management.

[0066] It's important to note that blood pressure risk is categorized into multiple levels. These levels can be established based on historical data, or roughly based on expert experience. The categorization criteria are then continuously refined using subsequent real-world data feedback to ensure the rationality of the blood pressure risk classification. A positive correlation exists between the overall blood pressure risk score and the level of blood pressure risk. That is, a higher overall blood pressure risk score indicates a higher blood pressure risk level, meaning a higher level of blood pressure management is required for the user.

[0067] In one specific embodiment, in S303, determining the user's dynamic blood pressure threshold range based on the blood pressure risk level includes: determining the user's maximum fluctuation value according to the blood pressure risk level; and determining the user's dynamic blood pressure threshold range according to the maximum fluctuation value and the user's optimal blood pressure value; wherein the optimal blood pressure value includes at least one of the user's stable blood pressure value and historical average blood pressure value.

[0068] It should be noted that the blood pressure risk level is related to the maximum blood pressure fluctuation value. Generally speaking, the higher the blood pressure risk level, the more critical the user's condition is, and the more stable the blood pressure needs to be. Therefore, the corresponding blood pressure fluctuation range should be smaller, that is, the maximum fluctuation value should be smaller, to ensure more precise blood pressure control.

[0069] Stable blood pressure refers to the blood pressure value measured when a user is in a stable physical condition without external interference, and is usually obtained through long-term monitoring. It can be a baseline value derived from a comprehensive analysis of blood pressure data from various normal states, such as during exercise, rest, or daily activities, or it can be blood pressure data from other relatively stable states of the user; there are no restrictions on this.

[0070] Historical average blood pressure refers to the average level obtained through long-term statistical analysis of users' blood pressure data, which can reflect the trend of blood pressure changes over a longer period of time.

[0071] Specifically, after determining the user's blood pressure risk level, the range within which their blood pressure can fluctuate, i.e., the maximum fluctuation value, is determined. However, each user's physiological state and blood pressure level are different. Therefore, different baseline blood pressure values ​​need to be set as a reference baseline for determining the dynamic blood pressure threshold range. Moreover, the baseline blood pressure value should be dynamically adjusted according to the user's different state. That is, the optimal blood pressure value can be adaptively adjusted according to the user's needs to obtain the dynamic blood pressure threshold range.

[0072] Furthermore, the process of managing a user's blood pressure based on the ambulatory blood pressure threshold range also includes: acquiring new symptom data of the user; adjusting the ambulatory blood pressure threshold range based on the new symptom data; and managing the user's blood pressure based on the adjusted ambulatory blood pressure threshold range.

[0073] It should be noted that a user's physical condition is constantly changing, therefore, dynamic monitoring and adjustments are necessary during blood pressure management.

[0074] New symptom data refers to newly acquired clinical indicators or abnormal signs related to a user's cardiovascular health during blood pressure management, such as signs of acute kidney injury, heart failure, or electrolyte imbalances. This data may directly affect blood pressure control targets. Therefore, when new symptom data is detected, it needs to be incorporated into the dynamic assessment system to recalibrate the user's blood pressure risk level. Based on this recalibrated risk level, the current ambulatory blood pressure threshold range is adjusted, resulting in an updated ambulatory blood pressure threshold range. Finally, blood pressure management based on this adjusted threshold range allows for real-time responses to user changes, improving the accuracy of blood pressure management.

[0075] Specifically, blood pressure management for users is based on the dynamic blood pressure threshold range, including: issuing an alarm signal and / or uploading alarm information when the user's real-time blood pressure value exceeds the dynamic blood pressure threshold range.

[0076] Alarm signals may include audible and visual alerts, vibration reminders, or push notifications to ensure that medical staff or users are promptly notified of any abnormal situations.

[0077] The alarm information includes real-time blood pressure value, measurement time, user identification, and current dynamic blood pressure threshold range, which helps medical staff quickly assess the user's current blood pressure status and thus effectively assist clinical decision-making.

[0078] In this embodiment, the maximum fluctuation value is determined based on the blood pressure risk level, and then the dynamic blood pressure threshold range is determined by combining the user's individual optimal blood pressure value, thus realizing personalized and accurate blood pressure threshold setting. At the same time, by adding symptom data feedback to adjust the dynamic threshold, it can respond to changes in the user's physical condition in real time and continuously maintain the accuracy of the threshold. In addition, an alarm with detailed information is issued when there is an abnormality, which helps to detect abnormalities in a timely manner and assist in rapid assessment and decision-making, thereby improving the overall accuracy, real-time adaptability and clinical decision support capabilities of blood pressure management.

[0079] To facilitate better implementation of the blood pressure detection-based medical assistance method provided by this invention, embodiments of this invention also provide an apparatus based on the aforementioned blood pressure detection-based medical assistance method. The meanings of the terms used are the same as in the aforementioned blood pressure detection-based medical assistance method, and specific implementation details can be found in the descriptions within the method embodiments.

[0080] Please see Figure 5 , Figure 5 This is a schematic diagram of a medical assistive device based on blood pressure detection according to an embodiment of the present invention, wherein the medical assistive device 500 based on blood pressure detection may include: The data acquisition module 501 is used to acquire the user's blood pressure data, blood pressure comorbidity data, and surgical risk triangle data; The blood pressure risk level generation module 502 is used to integrate blood pressure data, blood pressure comorbidity data and surgical risk triangle data to generate the user's blood pressure risk level. The blood pressure management module 503 is used to determine the user's dynamic blood pressure threshold range based on the blood pressure risk level, and to manage the user's blood pressure according to the dynamic blood pressure threshold range.

[0081] This invention also provides an electronic device, such as... Figure 6 As shown, Figure 6 This is a schematic diagram of an embodiment of the electronic device provided by the present invention, specifically: The electronic device may include components such as a processor 601 with one or more processing cores, a memory 602 with one or more computer-readable storage media, a power supply 603, and an input unit 604. Those skilled in the art will understand that... Figure 6 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 601 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 602, and by calling data stored in the memory 602, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 601 may include one or more processing cores; preferably, the processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operation of the storage medium, user interface, and application programs, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 601.

[0082] The memory 602 can be used to store software programs and modules. The processor 601 executes various functional applications and data processing by running the software programs and modules stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area. The program storage area may store applications required for operating the storage medium and at least one function (such as sound playback function, image playback function, etc.); the data storage area may store data created according to the use of the electronic device. In addition, the memory 602 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 602 may also include a memory controller to provide the processor 601 with access to the memory 602.

[0083] The electronic device also includes a power supply 603 that supplies power to various components. Preferably, the power supply 603 can be logically connected to the processor 601 via a power management storage medium, thereby enabling functions such as charging, discharging, and power consumption management through the power management storage medium. The power supply 603 may also include one or more DC or AC power supplies, recharge storage media, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0084] The electronic device may also include an input unit 604, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0085] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 601 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 602 according to the following instructions, and the processor 601 runs the applications stored in the memory 602 to realize various functions, as follows: Acquire users' blood pressure data, blood pressure comorbidity data, and surgical risk triangle data; Integrate blood pressure data, blood pressure comorbidity data, and surgical risk triangle data to generate a user's blood pressure risk level; The user's dynamic blood pressure threshold range is determined based on the blood pressure risk level, and blood pressure management is carried out for the user according to the dynamic blood pressure threshold range.

[0086] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0087] Therefore, embodiments of the present invention provide a computer-readable storage medium storing a computer program thereon, the computer program being loaded by a processor to execute the steps of any of the blood pressure detection-based medical assistance methods provided by the present invention. For example, the computer program, when loaded by a processor, can execute the following steps: Acquire users' blood pressure data, blood pressure comorbidity data, and surgical risk triangle data; Integrate blood pressure data, blood pressure comorbidity data, and surgical risk triangle data to generate a user's blood pressure risk level; The user's dynamic blood pressure threshold range is determined based on the blood pressure risk level, and blood pressure management is carried out for the user according to the dynamic blood pressure threshold range.

[0088] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0089] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0090] Since the computer program stored in the computer-readable storage medium can execute the steps in any of the blood pressure detection-based medical assistance methods provided by the present invention, the beneficial effects that any of the blood pressure detection-based medical assistance methods provided by the present invention can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0091] The above provides a detailed description of a medical auxiliary method, device, and electronic device based on blood pressure detection provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A medical auxiliary method based on blood pressure detection, characterized in that, include: Acquire users' blood pressure data, blood pressure comorbidity data, and surgical risk triangle data; The blood pressure data, the blood pressure comorbidity data, and the surgical risk triangle data are integrated to generate the user's blood pressure risk level. Based on the blood pressure risk level, the dynamic blood pressure threshold range of the user is determined, and the user's blood pressure is managed according to the dynamic blood pressure threshold range.

2. The medical auxiliary method based on blood pressure detection according to claim 1, characterized in that, The process of integrating the blood pressure data, the blood pressure comorbidity data, and the surgical risk triangle data to generate the user's blood pressure risk level includes: The blood pressure data, the blood pressure comorbidity data, and the surgical risk triangle data are respectively quantified and converted into scores to obtain blood pressure data scores, blood pressure comorbidity data scores, and surgical risk triangle data scores. The blood pressure data score, the blood pressure comorbidity data score, and the surgical risk triangle data score are weighted and fused to determine the user's blood pressure risk level.

3. The medical auxiliary method based on blood pressure detection according to claim 2, characterized in that, The blood pressure data includes the user's stable blood pressure data, deep sleep blood pressure data, morning wake-up blood pressure data, and blood pressure data at symptom triggers; the blood pressure data is quantified and converted into a score to obtain a blood pressure data score, including: The user's blood pressure fluctuation range is determined based on the deep sleep blood pressure data and the morning wake-up blood pressure data; Based on the blood pressure data at the time the symptoms were triggered and the range of blood pressure fluctuations, the abnormal blood pressure fluctuation index of the user at the time the symptoms were triggered was determined; Based on the stable blood pressure data and the blood pressure fluctuation range, determine the user's blood pressure stability index under stable conditions; The blood pressure data score is determined based on the abnormal blood pressure fluctuation index and the blood pressure stability index.

4. The medical auxiliary method based on blood pressure detection according to claim 2, characterized in that, The blood pressure comorbidity data includes the user's hypertension-related comorbidities and other systemic comorbidities; the blood pressure comorbidity data is quantified and converted into a score to obtain a blood pressure comorbidity data score, including: The first comorbidity score is determined based on the type and severity of the hypertension-related comorbidities data. The second comorbidity score is determined based on the correlation and degree of influence between the other systemic comorbidities and blood pressure; The blood pressure comorbidity score is obtained by weighted summing of the first comorbidity score and the second comorbidity score.

5. The medical auxiliary method based on blood pressure detection according to claim 2, characterized in that, The surgical risk triangle data includes surgical type, surgical duration, and potential blood loss. The surgical risk triangle data is quantified and converted into scores to obtain surgical risk triangle data scores, including: A surgical risk score is determined based on the risk level of the surgical type. A surgical duration score is determined based on the length of the surgical procedure. A surgical bleeding score is determined based on the magnitude of the potential bleeding. The surgical risk score, the surgical duration score, and the surgical bleeding score are weighted and calculated to obtain the surgical risk triangular data score.

6. The medical auxiliary method based on blood pressure detection according to claim 1, characterized in that, Determining the user's dynamic blood pressure threshold range based on the blood pressure risk level includes: The maximum fluctuation value of the user is determined based on the blood pressure risk level; Based on the maximum fluctuation value and the user's optimal blood pressure value, determine the user's dynamic blood pressure threshold range; The optimal blood pressure value includes at least one of the user's stable blood pressure value and historical average blood pressure value.

7. The medical auxiliary method based on blood pressure detection according to claim 1, characterized in that, The process of managing the user's blood pressure based on the dynamic blood pressure threshold range also includes: Obtain the user's new symptom data; The ambulatory blood pressure threshold range is adjusted based on the newly added symptom data, and blood pressure management is performed on the user based on the adjusted ambulatory blood pressure threshold range.

8. The medical auxiliary method based on blood pressure detection according to claim 1, characterized in that, The step of managing the user's blood pressure based on the dynamic blood pressure threshold range includes: When the user's real-time blood pressure value exceeds the dynamic blood pressure threshold range, an alarm signal is issued and / or alarm information is uploaded.

9. A medical auxiliary device based on blood pressure detection, characterized in that, include: The data acquisition module is used to acquire users' blood pressure data, blood pressure comorbidity data, and surgical risk triangle data; The blood pressure risk level generation module is used to integrate the blood pressure data, the blood pressure comorbidity data, and the surgical risk triangle data to generate the user's blood pressure risk level. The blood pressure management module is used to determine the dynamic blood pressure threshold range of the user based on the blood pressure risk level, and to manage the user's blood pressure according to the dynamic blood pressure threshold range.

10. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the following steps: Acquire users' blood pressure data, blood pressure comorbidity data, and surgical risk triangle data; The blood pressure data, the blood pressure comorbidity data, and the surgical risk triangle data are integrated to generate the user's blood pressure risk level. Based on the blood pressure risk level, the dynamic blood pressure threshold range of the user is determined, and the user's blood pressure is managed according to the dynamic blood pressure threshold range.

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