A method and related apparatus for managing risks of pregnant women
By calculating the risk score of pregnant women and the relationship between the regional grid and the hospital area, the shortest transfer route is planned, which solves the problem of inaccurate risk assessment of pregnant women in existing technologies and improves the efficiency and safety of treatment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN MATERNITY & CHILD HEALTHCARE HOSPITAL
- Filing Date
- 2026-04-22
- Publication Date
- 2026-07-03
AI Technical Summary
Current maternal risk assessment relies on the practical experience of medical staff, which is highly subjective and leads to inaccurate assessments. This affects the objectivity of medical resource allocation and treatment analysis, and reduces treatment efficiency and maternal safety.
By acquiring maternal and infant data, the target risk score is calculated based on prenatal check-up data, expected delivery data, address data, and regional data. Combined with the pre-defined risk level relationship, the target risk level is determined. By utilizing the regional grid and the relationship between the hospital area, the shortest transfer route is planned to achieve objective transfer decision-making.
It has improved the accuracy of maternal risk assessment and treatment efficiency, reduced the probability of maternal risks, and reduced reliance on the subjective experience of medical staff.
Smart Images
Figure CN122337529A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of smart healthcare, and more particularly to a method and related apparatus for managing maternal and infant risks. Background Technology
[0002] Maternal risk assessment is an important management tool. Assessing the risk level of pregnant women allows for the identification of high-risk factors early in pregnancy, enabling tiered management, precise intervention, and timely transfer, effectively reducing the risk of pregnancy complications, dystocia, and adverse pregnancy outcomes. By scientifically classifying risk levels, limited medical resources can be prioritized for high-risk pregnant women, improving treatment efficiency and success rates, enhancing maternal and infant safety, and ultimately improving the overall quality and standardization of maternal and child health services. In existing practices, healthcare professionals assess the risk level of pregnant women based on practical experience, and then allocate medical resources and conduct treatment analyses (such as transfer route analysis) based on these assessments.
[0003] However, the existing scheme relies on manual assessment, which depends on practical experience and is highly subjective. This may result in inaccurate assessment of maternal and child health risks, leading to a lack of objectivity in the subsequent allocation of medical resources and treatment analysis. Consequently, the efficiency of medical staff in providing treatment is low, and the probability of maternal and child health risks is high. Summary of the Invention
[0004] To address the technical problem of low treatment efficiency in the prior art, this application provides a method and related apparatus for managing maternal and infant risks, which can improve treatment efficiency.
[0005] The first aspect of this application provides a method for managing maternal risks, including: Obtain pregnancy and childbirth data of the target pregnant women, including prenatal check-up data, expected delivery data, address data, monitoring data, and regional data; The corresponding target risk score is obtained based on the prenatal checkup data, the expected delivery data, the address data, and the region data; Based on the preset relationship between risk score and risk level, the corresponding target risk level is obtained according to the target risk score; If the preset risk conditions are met based on the target risk level, the monitoring data, or the pre-delivery data, then the corresponding target hospital is determined based on the relationship between the preset regional grid and the hospital area, according to the target regional grid in the regional data. Based on the address data, the location of the target hospital, and the area data, determine the shortest transport route that the current emergency vehicle can take to transport the target pregnant woman.
[0006] Optionally, before obtaining the pregnancy and childbirth data of the target pregnant woman, the method further includes: Multiple regional grids are obtained based on the geographical extent of all regional units within the target area, where each regional unit is a street or community. For each of the aforementioned regional grids, the regional grid is associated with the nearest hospital area in the target region to the regional grid to establish the relationship between the regional grid and the hospital area.
[0007] Optionally, obtaining multiple regional grids based on the geographical extent of all regional units within the target area includes: Obtain the geographical extent of all regional units within the target area, and obtain multiple corresponding regional grids based on the geographical extent and preset merging conditions.
[0008] Optionally, obtaining the corresponding multiple regional grids based on the geographical range and preset merging conditions includes: Based on the geographical range, the regional units that meet the preset merging conditions are merged to obtain multiple corresponding regional grids to be adjusted. The grid of the region to be adjusted is dynamically adjusted to obtain multiple corresponding region grids.
[0009] Optionally, the preset merging conditions include: The regional units have the same and are adjacent in terms of functional attributes, which are core business districts, old urban areas, high-end residences, government centers, transportation hubs, or cross-border ports. And / or, The population densities of the regional units are within the same preset density range and are adjacent.
[0010] Optionally, the step of dynamically adjusting the grid of the region to be adjusted to obtain a plurality of corresponding region grids includes: Obtain the grid population of each of the grids in the area to be adjusted; For each grid in the area to be adjusted whose population exceeds a preset maximum number of grid services, the area to be adjusted is divided into n grids whose population does not exceed the maximum number of grid services, where n is the smallest positive integer less than or equal to the quotient, and the quotient is the ratio of the grid population to the maximum number of grid services. The grids to be adjusted, whose population does not exceed the maximum number of grid services, and all the grids to be determined are identified as the multiple regional grids; or, Obtain the grid area of each of the grids in the region to be adjusted; For each of the grid regions to be adjusted whose grid area exceeds the preset maximum service area, the grid region to be adjusted is divided into m grids whose grid area does not exceed the maximum service area, where m is the smallest positive integer less than or equal to the area quotient, and the area quotient is the ratio of the grid area to the maximum service area. The grids to be adjusted, whose grid area does not exceed the maximum area of the grid service, and all the grids to be determined are identified as the multiple regional grids.
[0011] Optionally, the step of dynamically adjusting the grid of the region to be adjusted to obtain a plurality of corresponding region grids includes: Based on the user's input adjustment command for the area to be adjusted, the area to be adjusted is divided and / or merged to obtain the multiple area grids.
[0012] A second aspect of this application provides a maternal risk management device, comprising: The acquisition unit is used to acquire the pregnancy and childbirth data of the target pregnant woman, including prenatal check-up data, expected delivery data, address data, monitoring data and regional data; The processing unit is used to obtain a corresponding target risk score based on the prenatal checkup data, the expected delivery data, the address data, and the region data; The processing unit is also used to obtain the corresponding target risk level based on the target risk score according to the preset relationship between risk score and risk level; The determining unit is used to determine the corresponding target hospital based on the target area grid in the area data if the preset risk conditions are met based on the target risk level, the monitoring data, or the pre-delivery data; The determining unit is used to determine the shortest transport route that the current emergency vehicle can travel based on the address data, the location of the target hospital area, and the area data, so as to transport the target pregnant woman.
[0013] A third aspect of this application provides a maternal risk management device, comprising: Central processing unit, memory, and input / output interfaces; The memory is either a short-term storage memory or a persistent storage memory; The central processing unit is configured to communicate with the memory and execute instructions in the memory to perform the aforementioned method.
[0014] A fourth aspect of this application provides a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the aforementioned method.
[0015] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: First, the pregnancy and childbirth data of the target pregnant woman are acquired. Then, based on prenatal checkup data, expected delivery data, address data, and regional data, a corresponding target risk score is obtained. Next, based on the preset relationship between the risk score and risk level, the corresponding target risk level is obtained. If the preset risk conditions are met based on the target risk level, monitoring data, or expected delivery data, the corresponding target hospital is determined based on the preset relationship between the regional grid and the hospital area, according to the target regional grid in the regional data. Finally, based on the address data, the location of the target hospital, and the regional data, the shortest transport route that the current emergency vehicle can take is determined to transport the target pregnant woman. The method of this application is performed by a device that objectively obtains the corresponding target risk level based on the pregnancy and childbirth data of the target pregnant woman, and then objectively analyzes and obtains the corresponding transport route. Compared with manual assessment, the accuracy is greatly improved, and it does not rely on the subjective experience of medical staff, thus making the treatment more efficient and greatly reducing the probability of risks to pregnant women.
[0016] Additional aspects and advantages of embodiments of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of this application. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.
[0018] Figure 1 This is a schematic diagram of an embodiment of a maternal risk management method disclosed in this application; Figure 2 This is a schematic diagram of another embodiment of a maternal risk management method disclosed in this application; Figure 3 This is a schematic diagram of an embodiment of a maternal risk management device disclosed in this application; Figure 4 This is a schematic diagram of another embodiment of a maternal risk management device disclosed in this application.
[0019] The realization of the objectives, functional features and advantages of the embodiments of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0020] Pregnancy risk assessment is crucial for the treatment of high-risk pregnant women. It can identify high-risk factors early in pregnancy, enabling tiered management, precise intervention, and timely transfer, effectively reducing the risk of pregnancy complications, dystocia, and adverse pregnancy outcomes. In existing methods, healthcare professionals assess the risk level of pregnant women based on practical experience, allocating medical resources and conducting treatment analyses accordingly. However, current methods rely on manual assessment, which is subjective and dependent on experience. This can lead to inaccurate risk assessments, resulting in less objective allocation of medical resources and treatment analyses, lower treatment efficiency, and a higher probability of maternal risks. To address these technical problems, this application provides a pregnancy risk management method and related apparatus. Based on the pregnancy data of the target pregnant woman, the method objectively obtains the corresponding target risk level, enabling objective analysis of the appropriate transfer route. Compared to manual assessment, this method significantly improves accuracy, eliminates reliance on the subjective experience of healthcare professionals, and thus improves treatment efficiency, greatly reducing the probability of maternal risks.
[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the embodiments of this application, and should not be construed as limiting the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.
[0022] In the description of the embodiments of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "circumferential", "radial", etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.
[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "first," "second," "third," "fourth," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. In the description of embodiments of this application, "a plurality of" means two or more, unless otherwise expressly specified.
[0024] In the embodiments of this application, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.
[0025] In the embodiments of this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature being directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.
[0026] The following describes a method for managing maternal risks according to this application. Please refer to [link / reference]. Figure 1 An embodiment of a maternal risk management method of this application includes: 101. Obtain pregnancy and childbirth data of the target pregnant women; The system acquires pregnancy and childbirth data for the target pregnant woman, including prenatal checkup data, due date data, address data, monitoring data, and regional data. Prenatal checkup data comprises the results of the target pregnant woman's prenatal examinations; due date data includes information about her expected delivery date; address data includes her detailed residential address and building information; and regional data refers to relevant data within the grid area where her residential address is located. Specifically, the device has pre-established communication connections with various relevant institutions and can obtain pregnancy and childbirth data from preset databases or individual institutions, or it can receive user input in real time; the specifics are not limited here.
[0027] 102. Obtain the corresponding target risk score based on prenatal checkup data, expected delivery data, address data, and regional data; The target risk score is obtained based on prenatal checkup data, expected delivery data, address data, and regional data. Specifically, risk parameters are obtained based on address data and / or regional data. These risk parameters include at least one of five parameters: handling difficulty, distance, traffic congestion, building accessibility, and transportation convenience. The target risk score is then obtained by weighting and calculating these parameters, prenatal checkup data, expected delivery data, and multiple preset weights.
[0028] 103. Based on the preset relationship between risk scores and risk levels, the corresponding target risk level is obtained according to the target risk score; Based on the pre-defined relationship between risk scores and risk levels, the corresponding target risk level is obtained from the target risk score. There can be multiple risk levels, which can be set according to actual needs; no specific limit is set here. Generally speaking, the higher the risk score, the higher the risk level.
[0029] 104. If the preset risk conditions are met based on the target risk level, monitoring data, or pre-production data, then the corresponding target hospital is determined based on the relationship between the preset regional grid and the hospital area, according to the target regional grid in the regional data. If the preset risk conditions are determined based on the target risk level, monitoring data, or expected delivery data, then the corresponding target hospital is determined based on the relationship between the preset regional grid and the hospital, according to the target regional grid in the regional data. The preset risk conditions are for high-risk situations, in order to screen out high-risk pregnant women.
[0030] 105. Based on address data, the location and area data of the target hospital, determine the shortest transport route that the current emergency vehicle can take in order to transport the target pregnant woman.
[0031] Based on address data, the location of the target hospital, and regional data, determine the shortest transport route that the current ambulance can travel to transport the target pregnant woman. Multiple routes between the actual residential address and the target hospital location can be randomly generated first, and then filtered based on the current ambulance information to select the transport route that is both accessible and has the shortest travel time.
[0032] In this embodiment, the pregnancy and childbirth data of the target pregnant woman are first acquired. Then, a corresponding target risk score is obtained based on prenatal checkup data, expected delivery data, address data, and regional data. Next, based on a preset relationship between the risk score and risk level, a corresponding target risk level is obtained. If the preset risk conditions are met based on the target risk level, monitoring data, or expected delivery data, the corresponding target hospital is determined based on the preset relationship between the regional grid and the hospital area, according to the target regional grid in the regional data. Finally, the shortest transport route that the current emergency vehicle can travel is determined based on the address data, the location of the target hospital, and the regional data to transport the target pregnant woman. This method is executed using a device. The device objectively obtains the corresponding target risk level based on the pregnant woman's pregnancy and childbirth data, and then objectively analyzes and obtains the corresponding transport route. Compared to manual assessment, the accuracy is greatly improved, and it does not rely on the subjective experience of medical personnel, thus resulting in higher treatment efficiency and significantly reducing the probability of risks to the pregnant woman.
[0033] In addition, to achieve precise, balanced, and efficient maternal risk management, the target area (the following example uses Futian District of Shenzhen as an example) can be divided into multiple regional grids. For details, please refer to [link / reference needed]. Figure 2 Another embodiment of a maternal risk management method of this application includes: 201. Based on the geographical extent of all regional units within the target area, obtain the corresponding multiple regional grids; Multiple regional grids are obtained based on the geographical extent of all regional units within the target area. Regional units can be streets or communities. Specifically, the geographical extent of all regional units within the target area is acquired, and multiple regional grids are obtained based on the geographical extent and preset merging conditions. First, regional units that meet the preset merging conditions are merged based on their geographical extent to obtain multiple regional grids to be adjusted. Then, these regional grids are dynamically adjusted to obtain the final multiple regional grids. Within Futian District of Shenzhen, regional grids can include Futian-Huaqiangbei grid (core business district + old urban area), Shatou-Meilin grid (transportation hub), Xiangmihu-Lianhua grid (high-end residential + government core area), Yuanling-Huafu grid (old urban area), Nanyuan-Futian Street grid (old urban area), Fubao-Hetao grid (cross-border port), etc.
[0034] There are various preset merging conditions, which are not limited here. The following describes two implementation methods.
[0035] In one implementation, the regional units have the same and are adjacent in terms of functional attributes, such as core business district, old urban area, high-end residential area, government center, transportation hub, or cross-border port. As shown in the example above, Yuanling Community and its surrounding area and Huafu Community and its surrounding area are both old urban areas and are adjacent to each other, so they are classified into the same regional grid.
[0036] In another implementation, the population densities of the regional units are within the same preset density range and are adjacent. The preset density range, also known as the population density range, can be set according to actual needs and is not limited here.
[0037] The step "dynamically adjust the grid of the area to be adjusted to obtain multiple corresponding area grids" has at least three implementation methods. It is understood that there are other implementation methods as well, which are not limited here. The following uses three implementation methods as examples.
[0038] In one implementation, the population of each grid to be adjusted is first obtained. Then, for each grid to be adjusted whose population exceeds a preset maximum service capacity, the grid to be adjusted is divided into n undetermined grids whose population does not exceed the maximum service capacity. Here, n is the smallest positive integer less than or equal to the quotient, which is the ratio of the grid population to the maximum service capacity. Finally, the grids to be adjusted whose population does not exceed the maximum service capacity and all undetermined grids are determined as multiple grid areas. For example, if the population of a grid to be adjusted is 200,000, and the preset maximum service capacity is 150,000, then the quotient is 200,000 divided by 150,000, which is approximately 1.33. Therefore, n is 2, and the grid to be adjusted can be divided into two undetermined grids with a population not exceeding 150,000. It is understood that there are other similar methods of dividing based on population, which are not limited here.
[0039] In another implementation, the grid area of each region to be adjusted is first obtained. Then, for each region whose area exceeds a preset maximum service area, the region to be adjusted is divided into m undetermined grids whose area does not exceed the maximum service area. Here, m is the smallest positive integer less than or equal to the area quotient, which is the ratio of the grid area to the maximum service area. Finally, the region to be adjusted with areas not exceeding the maximum service area and all undetermined grids are identified as multiple region grids. For example, if a region to be adjusted has an area of 300,000 square meters and a maximum service area of 140,000 square meters, the area quotient is 30 divided by 14, which is approximately 2.14. Therefore, m is 3, and the region to be adjusted can be divided into 3 undetermined grids with areas not exceeding 140,000 square meters. It is understood that there are other similar methods of dividing based on area, which are not limited here.
[0040] In another implementation, based on the user's input adjustment command for the area to be adjusted, the area grid is divided and / or merged to obtain multiple area grids. Specifically, the user can freely adjust the area grid based on the adjustment command, for example, by re-dividing and / or merging it according to special circumstances such as geographical features. For example, the waterfront area near the Shenzhen River needs to be freely adjusted based on characteristics such as rainy season flooding and geographical conditions.
[0041] 202. For each regional grid, associate the regional grid with the nearest hospital area in the target region to establish the relationship between the regional grid and the hospital area; For each regional grid, the nearest hospital within the target area is associated with that regional grid to establish the relationship between the grid and the hospital. Specifically, the nearest hospital to each regional grid is first identified, and then the association is performed. For example, Shenzhen Maternal and Child Health Hospital in Futian District, Shenzhen, involves two campuses: Hongli Campus and Fuqiang Campus. Hongli Campus mainly covers the Futian-Huaqiangbei grid, Yuanling-Huafu grid, and Xiangmihu-Lianhua grid, while Fuqiang Campus mainly covers the Shatou-Meilin grid, Nanyuan-Futian Street grid, and Fubao-Hetao grid.
[0042] 203. Obtain pregnancy and childbirth data of the target pregnant women; Obtain pregnancy and childbirth data for the target pregnant women. The pregnancy and childbirth data includes prenatal examination data, expected delivery data, address data, monitoring data, and regional data. Among them, prenatal examination data is the result data obtained after the target pregnant women have undergone prenatal examinations, expected delivery data is the data about the expected delivery date of the target pregnant women, address data is the detailed residential address and building information of the target pregnant women, and regional data is the relevant data within the area where the grid of the target pregnant women's residential address is located. Specifically, prenatal checkup data includes prenatal blood pressure, prenatal blood glucose, prenatal fetal heart rate, ultrasound results, high-risk scores, and complication results. Expectant delivery data includes the current last menstrual period date and current gestational age (both can be used to calculate the expected delivery date). Address data includes the community name, building number, floor, house number, mode of transport (stairs only / stairs and elevator), community exit width, building narrowness, and accessibility information (whether accessible pathways are available). Monitoring data (real-time data from monitoring devices on the target pregnant woman) includes blood pressure, blood glucose, and fetal heart rate monitoring. Regional data includes traffic congestion in designated areas (e.g., weekday morning rush hour in the Huaqiangbei commercial district), grid location characteristics (e.g., location characteristics of the Yuanling-Huafu grid), and area accessibility characteristics (e.g., road characteristics in the waterfront area). Specifically, the device has pre-established communication connections with various relevant institutions and can obtain data from preset databases or these institutions, or receive real-time user input to obtain pregnancy and childbirth data; specific details are not limited here.
[0043] 204. Obtain the corresponding target risk score based on prenatal checkup data, expected delivery data, address data, and regional data; The target risk score is obtained based on prenatal checkup data, expected delivery data, address data, and regional data. Specifically, risk parameters are obtained based on address data and / or regional data. These risk parameters include at least one of five parameters: transportation difficulty, distance, traffic congestion, building accessibility, and transportation convenience. These parameters are then weighted and calculated based on the risk parameters, prenatal checkup data, expected delivery data, and multiple preset weights to arrive at the target risk score. The transportation difficulty parameter indicates the difficulty of transporting the target pregnant woman; the distance parameter indicates the distance between the target pregnant woman's community and various hospital campuses; the traffic congestion parameter indicates the traffic congestion situation in various parts of the target area; the building accessibility parameter indicates the accessibility of the building in the target pregnant woman's community; and the transportation convenience parameter indicates the road conditions in various parts of the target area. The distance parameter includes the straight-line distance to the hospital campus and the actual path distance to the hospital campus; the traffic congestion parameter includes congested areas and the corresponding congestion time periods. It is understood that the specific number of risk parameters can be selected according to actual needs and is not limited to just one of the five parameters mentioned above; no specific limitation is made here. The preset weights can be set according to the actual emphasis requirements, and there is no specific limitation here. Preferably, in one implementation, the total weight of risk parameters is 0.6, the total weight of prenatal checkup data is 0.2, and the total weight of expected delivery data is also 0.2.
[0044] 205. Based on the preset relationship between risk scores and risk levels, the corresponding target risk level is obtained according to the target risk score; Based on the preset relationship between risk scores and risk levels, the corresponding target risk level is obtained according to the target risk score. The risk level can be low, medium, or high. The preset relationship can be set according to actual needs and is not limited here. Preferably, in one implementation, a risk score less than 30 points indicates a low risk level; a risk score greater than or equal to 30 points and less than or equal to 60 points indicates a medium risk level; and a risk score greater than 60 points indicates a high risk level.
[0045] 206. If the preset risk conditions are met based on the target risk level, monitoring data, or pre-production data, then the corresponding target hospital is determined based on the relationship between the preset regional grid and the hospital area, according to the target regional grid in the regional data. If the preset risk conditions are met based on the target risk level, monitoring data, or expected delivery data, then the corresponding target hospital is determined according to the target area grid in the regional data, based on the relationship between the preset regional grid and the hospital. Meeting the preset risk conditions indicates that the target pregnant woman is in a high-risk state and requires activation of the green channel for treatment. Specifically, the preset risk conditions can be set according to actual needs; no specific limitations are specified here, but examples are provided below.
[0046] In one implementation, the target risk level is a high-risk level.
[0047] In one implementation, the target risk level is upgraded to a high-risk level.
[0048] In one implementation, the monitoring data detects blood pressure changes exceeding a preset high-risk blood pressure rate threshold.
[0049] In one implementation, the monitoring data detects changes in blood glucose levels exceeding a preset high-risk blood glucose rate threshold.
[0050] In one implementation, the duration for which the fetal heart rate is monitored within a preset high-risk heart rate range exceeds the preset high-risk duration.
[0051] In one implementation, the current day is a pre-defined high-risk number of days beyond the due date, and the due date is a date calculated based on the current last menstrual period and current gestational week in the due date data.
[0052] In one implementation, a user-triggered emergency rescue request is received. Specifically, the user can trigger emergency rescue with a single click to deal with an emergency.
[0053] 207. Based on address data, the location and area data of the target hospital, determine the shortest transport route that the current emergency vehicle can take in order to transport the target pregnant woman.
[0054] Based on address data, the location of the target hospital, and regional data, the shortest transport route accessible to emergency vehicles is determined to transport the target pregnant women. Route planning follows three principles tailored to the characteristics of Futian District: 1. Convenience Principle: Prioritizing main roads and expressways, avoiding narrow sections, construction zones, and areas with dense pedestrian crossings, with a focus on adapting to the traffic conditions of older urban areas and waterfront areas; 2. Timeliness Principle: Avoiding areas and times of high congestion, such as choosing Shennan Middle Road during peak hours in the Huaqiangbei commercial district, choosing the Meiguan Expressway during peak hours at Meilin Pass, and avoiding congested sections around the port during peak hours; 3. Adaptability Principle: Considering the address characteristics of the pregnant woman, such as choosing a route where the ambulance can park at the nearest building entrance for pregnant women in walk-up buildings; planning routes accessible to small emergency vehicles in narrow sections of older urban areas; and planning the optimal route including port clearance channels for pregnant women crossing borders. Meanwhile, key nodes along the route are marked, such as the optimal parking location for ambulances, building transport entrances, number of traffic lights, estimated travel time, and border crossing points, providing precise guidance for emergency personnel. Specifically, planning is based on the above three principles, and routes that do not conform to these principles are deleted, leaving only the initial route with the shortest travel time.
[0055] In addition, the transfer route can be updated in real time according to the actual situation. For example, if a section of the original planned transfer route is temporarily impassable, a new transfer route will be automatically obtained by reanalyzing the address data, the location of the target hospital, and the regional data.
[0056] In this embodiment, the method is executed using a device. The device objectively determines the target risk level based on the pregnant woman's pregnancy and childbirth data, and then objectively analyzes and determines the corresponding transfer route. Compared with manual assessment, the accuracy is greatly improved, and it does not rely on the subjective experience of medical staff, thus resulting in higher treatment efficiency and significantly reducing the probability of risks to pregnant women. Furthermore, the regional grid division can further improve treatment efficiency.
[0057] The above describes a method for managing maternal risk in embodiments of this application. The following describes a device for managing maternal risk in embodiments of this application. Please refer to... Figure 3 One embodiment of a maternal risk management device in this application includes: The acquisition unit 301 is used to acquire the pregnancy and childbirth data of the target pregnant woman, including prenatal check-up data, expected delivery data, address data, monitoring data and regional data; Processing unit 302 is used to obtain the corresponding target risk score based on prenatal checkup data, expected delivery data, address data and regional data; The processing unit 302 is also used to obtain the corresponding target risk level based on the target risk score according to the preset relationship between risk score and risk level; The determination unit 303 is used to determine the corresponding target hospital based on the target area grid in the area data if the preset risk conditions are met based on the target risk level, monitoring data or pre-production data. Unit 303 is used to determine the shortest transport route that the current emergency vehicle can take based on address data, the location of the target hospital, and regional data, so as to transport the target pregnant woman.
[0058] In this embodiment, the acquisition unit 301 first acquires the pregnancy and childbirth data of the target pregnant woman. Then, the processing unit 302 obtains the corresponding target risk score based on prenatal checkup data, expected delivery data, address data, and regional data. Then, based on a preset relationship between the risk score and risk level, the corresponding target risk level is obtained. If the preset risk conditions are met based on the target risk level, monitoring data, or expected delivery data, the determination unit 303 then determines the corresponding target hospital based on the preset relationship between the regional grid and the hospital area, according to the target regional grid in the regional data. Finally, based on the address data, the location of the target hospital, and the regional data, the shortest transport route that the current emergency vehicle can travel is determined to transport the target pregnant woman. This method is executed using a device. The device objectively obtains the corresponding target risk level based on the pregnancy and childbirth data of the target pregnant woman, and can then objectively analyze and obtain the corresponding transport route. Compared to manual assessment, the accuracy is greatly improved, and it does not rely on the subjective experience of medical personnel, thus resulting in higher treatment efficiency and significantly reducing the probability of risks to the pregnant woman.
[0059] The following is a detailed description of a maternal risk management device according to an embodiment of this application. Another embodiment of the maternal risk management device according to an embodiment of this application includes: The acquisition unit is used to acquire the pregnancy and childbirth data of the target pregnant women. The pregnancy and childbirth data includes prenatal check-up data, expected delivery data, address data, monitoring data, and regional data. The first processing unit is used to obtain the corresponding target risk score based on prenatal checkup data, expected delivery data, address data, and regional data. The first processing unit is also used to obtain the corresponding target risk level based on the preset relationship between risk score and risk level. The determination unit is used to determine the corresponding target hospital based on the target area grid in the area data if the preset risk conditions are met based on the target risk level, monitoring data, or pre-production data. The determination unit is used to determine the shortest transport route that the current emergency vehicle can take based on address data, the location of the target hospital, and regional data, in order to transport the target pregnant woman.
[0060] The device also includes a second processing unit for: Multiple regional grids are obtained based on the geographical extent of all regional units within the target area, where the regional units are streets or communities; For each regional grid, the regional grid is associated with the nearest hospital area in the target region to establish the relationship between the regional grid and the hospital area.
[0061] The second processing unit is specifically used for: Obtain the geographic extent of all regional units within the target area, and generate multiple corresponding regional grids based on the geographic extent and preset merging conditions.
[0062] The second processing unit is specifically used for: Based on geographical scope, regional units that meet the preset merging conditions are merged to obtain multiple corresponding regional grids to be adjusted. The grid of the area to be adjusted is dynamically adjusted to obtain multiple corresponding area grids.
[0063] The second processing unit is specifically used for: The regional units have the same and adjacent functional attributes, namely, core business district, old urban area, high-end residential area, government center, transportation hub or cross-border port; And / or, The population density of the regional units is within the same preset density range and they are adjacent.
[0064] The second processing unit is specifically used for: Obtain the population of each grid cell in the region to be adjusted; For each grid area to be adjusted whose population exceeds the preset maximum number of grid services, the grid area to be adjusted is divided into n grids whose population does not exceed the maximum number of grid services. Here, n is the smallest positive integer less than or equal to the quotient, and the quotient is the ratio of the grid population to the maximum number of grid services. The grids to be adjusted, whose population does not exceed the maximum number of grid services, and all grids to be determined are identified as multiple regional grids; or, Obtain the grid area of each region to be adjusted; For each grid area to be adjusted that exceeds the preset maximum service area of the grid, the grid area to be adjusted is divided into m grids whose grid area does not exceed the maximum service area of the grid. Here, m is the smallest positive integer less than or equal to the area quotient, and the area quotient is the ratio of the grid area to the maximum service area of the grid. The grids to be adjusted, whose grid area does not exceed the maximum area of the grid service, and all grids to be determined are identified as multiple regional grids.
[0065] The second processing unit is specifically used for: Based on the user's input adjustment instructions for the area to be adjusted, the area to be adjusted is divided and / or merged to obtain multiple area grids.
[0066] The functions and processes performed by each unit in the maternal risk management device in this embodiment are the same as those described above. Figures 1 to 2The functions and processes performed by the maternal and infant risk management device are similar, and will not be described in detail here.
[0067] Figure 4 This is a schematic diagram of the structure of a maternal risk management device provided in an embodiment of this application. The maternal risk management device 400 may include one or more central processing units (CPUs) 401 and a memory 405, wherein the memory 405 stores one or more applications or data.
[0068] The memory 405 can be volatile or persistent storage. The program stored in the memory 405 can include one or more modules, each module including a series of instruction operations on the maternal risk management device 400. Furthermore, the central processing unit 401 can be configured to communicate with the memory 405 and execute the series of instruction operations stored in the memory 405 on the maternal risk management device 400.
[0069] The maternal risk management device 400 may also include one or more power supplies 402, one or more wired or wireless network interfaces 403, one or more input / output interfaces 404, and / or one or more operating systems, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.
[0070] The central processing unit 401 can perform the aforementioned... Figures 1 to 2 The specific operations performed by the maternal risk management device in the illustrated embodiment will not be described in detail here.
[0071] This application also provides a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the methods described in the foregoing embodiments.
[0072] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0073] It should be noted that although the steps in the flowcharts of the various embodiments are drawn sequentially according to the arrows, unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the various embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps.
[0074] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0075] If the integrated unit is implemented as 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 this application, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0076] The above are merely preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structural transformations made using the description and drawings of the present application under the inventive concept of the present application, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present application.
Claims
1. A method of maternal risk management, characterized in that, include: Obtain pregnancy and childbirth data of the target pregnant women, including prenatal check-up data, expected delivery data, address data, monitoring data, and regional data; The corresponding target risk score is obtained based on the prenatal checkup data, the expected delivery data, the address data, and the region data; Based on the preset relationship between risk score and risk level, the corresponding target risk level is obtained according to the target risk score; If the preset risk conditions are met based on the target risk level, the monitoring data, or the pre-delivery data, then the corresponding target hospital is determined based on the relationship between the preset regional grid and the hospital area, according to the target regional grid in the regional data. Based on the address data, the location of the target hospital, and the area data, determine the shortest transport route that the current emergency vehicle can take to transport the target pregnant woman.
2. The maternal risk management method of claim 1, wherein, Before obtaining the pregnancy and childbirth data of the target pregnant woman, the method further includes: Multiple regional grids are obtained based on the geographical extent of all regional units within the target area, where each regional unit is a street or community. For each of the aforementioned regional grids, the regional grid is associated with the nearest hospital area in the target region to the regional grid to establish the relationship between the regional grid and the hospital area.
3. The method of maternal risk management of claim 2, wherein, The process of obtaining multiple corresponding regional grids based on the geographical extent of all regional units within the target area includes: Obtain the geographical extent of all regional units within the target area, and obtain multiple corresponding regional grids based on the geographical extent and preset merging conditions.
4. The method of maternal risk management of claim 3, wherein, The process of obtaining multiple corresponding regional grids based on the geographical range and preset merging conditions includes: Based on the geographical range, the regional units that meet the preset merging conditions are merged to obtain multiple corresponding regional grids to be adjusted. The grid of the region to be adjusted is dynamically adjusted to obtain multiple corresponding region grids.
5. The method of claim 4, wherein, The preset merging conditions include: The regional units have the same and are adjacent in terms of functional attributes, which are core business districts, old urban areas, high-end residences, government centers, transportation hubs, or cross-border ports. And / or, The population densities of the regional units are within the same preset density range and are adjacent.
6. The method of claim 4, wherein, The step of dynamically adjusting the grid of the region to be adjusted to obtain multiple corresponding region grids includes: Obtain the grid population of each of the grids in the area to be adjusted; For each grid in the area to be adjusted whose population exceeds a preset maximum number of grid services, the area to be adjusted is divided into n grids whose population does not exceed the maximum number of grid services, where n is the smallest positive integer less than or equal to the quotient, and the quotient is the ratio of the grid population to the maximum number of grid services. The grids to be adjusted, whose population does not exceed the maximum number of grid services, and all the grids to be determined are identified as the multiple regional grids; or, Obtain the grid area of each of the grids in the region to be adjusted; For each of the grid regions to be adjusted whose grid area exceeds the preset maximum service area, the grid region to be adjusted is divided into m grids whose grid area does not exceed the maximum service area, where m is the smallest positive integer less than or equal to the area quotient, and the area quotient is the ratio of the grid area to the maximum service area. The grids to be adjusted, whose grid area does not exceed the maximum area of the grid service, and all the grids to be determined are identified as the multiple regional grids.
7. The method of maternal risk management of claim 4, wherein, The step of dynamically adjusting the grid of the region to be adjusted to obtain multiple corresponding region grids includes: Based on the user's input adjustment command for the area to be adjusted, the area to be adjusted is divided and / or merged to obtain the multiple area grids.
8. A maternal risk management device, characterized by, include: The acquisition unit is used to acquire the pregnancy and childbirth data of the target pregnant woman, including prenatal check-up data, expected delivery data, address data, monitoring data and regional data; The processing unit is used to obtain a corresponding target risk score based on the prenatal checkup data, the expected delivery data, the address data, and the region data; The processing unit is also used to obtain the corresponding target risk level based on the target risk score according to the preset relationship between risk score and risk level; The determining unit is used to determine the corresponding target hospital based on the target area grid in the area data if the preset risk conditions are met based on the target risk level, the monitoring data, or the pre-delivery data; The determining unit is used to determine the shortest transport route that the current emergency vehicle can travel based on the address data, the location of the target hospital area, and the area data, so as to transport the target pregnant woman.
9. A maternal risk management device, characterized in that, include: Central processing unit, memory, and input / output interfaces; The memory is either a short-term storage memory or a persistent storage memory; The central processing unit is configured to communicate with the memory and execute instructions in the memory to perform the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, Includes instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.