A postpartum care breast unblocking treatment course tracking and management method

Abstract

The present application relates to the technical field of postpartum care monitoring, in particular to a breast dredging treatment course tracking and management method for postpartum care. The clogging level and duration of the puerpera are continuously recorded to form a dynamic disease course tracking framework, and historical samples are screened to establish a data benchmark library corresponding to each level to quantify the group evolution law. Further, the duration dimension is subdivided under each level, and a "level-duration-risk trend" model is constructed by combining historical trends to depict the dynamic evolution trend. In view of individual differences, the change characteristics and numerical law of the last record are analyzed in the target sample, and the group trend index of the last record is dynamically corrected to generate an individualized state trend index. Finally, risk warning is implemented based on the corrected index, effectively improving the individualized prediction accuracy.

Description

Technical Field

[0001] This invention relates to the field of postpartum care monitoring technology, specifically to a method for tracking and managing postpartum breast duct unblocking treatment. Background Technology

[0002] Postpartum women are physically and mentally vulnerable for a period of time. Physiological breast engorgement typically occurs 3-4 days after delivery. Hospitals provide postpartum care and related services to protect the mother's physical and mental health and ensure an effective supply of breast milk for the fetus. Breast milk flow unblocking is fundamental to successful breastfeeding. Postpartum, due to increased prolactin levels, mammary cells secrete large amounts of milk, which can easily lead to breast duct blockage and lumps. This makes it difficult for milk to flow smoothly, causing breast engorgement, blocked milk ducts, mastitis, and even affecting lactation function. Therefore, it is necessary to promptly detect and unblock breast ducts.

[0003] In the follow-up management of postpartum breast care treatment, there are often various states and symptoms. Current technology usually uses the changes in the state of other mothers' breast care treatment as the basis for assessment, and then compares them with the current state of the target mother's breasts for monitoring and early warning. However, different mothers have different physical conditions and individual differences. Therefore, if the assessment is based solely on the changes in the state of other mothers without considering the differences between the individual mother and the whole, the final early warning results will be unsatisfactory. Summary of the Invention

[0004] To address the issue of individual differences among postpartum women due to varying physical conditions, relying solely on the condition changes of other postpartum women for assessment, without considering the individual differences between the postpartum woman and the overall population, can lead to unsatisfactory early warning results. The present invention aims to provide a method for tracking and managing postpartum breast massage treatment. The specific technical solution adopted is as follows:

[0005] Nursing samples were obtained, which were divided into historical samples from multiple mothers and target samples from the target mother. Each nursing sample contained multiple records of the postpartum breast care treatment course of the mother in chronological order, and each record included the level of breast blockage and the duration of the level.

[0006] In all historical samples, all records are filtered to obtain the dataset corresponding to each level of breast blockage; in the dataset of each level of breast blockage, the changes in the duration of the level between records are analyzed, and combined with the changing trends of the records in the historical samples, multiple trend indicators of each level of breast blockage under each level duration are determined.

[0007] In the target sample, we analyze the changes between records and the numerical characteristics of the breast blockage level, and adjust the trend index of the last record in the historical sample to obtain the state trend index of the target sample at the current moment.

[0008] The state trend index is used to issue an early warning for the state of the target sample.

[0009] Furthermore, the trend indicators include positive trend indicators, negative trend indicators, and stable trend indicators.

[0010] Furthermore, the method for obtaining the positive trend indicator includes:

[0011] Choose any level of breast blockage as the level to be tested;

[0012] In the dataset of the level to be tested, the duration range is determined based on the maximum and minimum values ​​of the level duration in all records;

[0013] Within the specified duration range, any one of the specified duration levels can be selected as the time to be measured;

[0014] In the dataset, the number of records whose level duration is greater than or equal to the test time is used as the base value of the state transition corresponding to the test time under the test level. Records whose level duration is equal to the test time are used as records to be analyzed, and the number of records to be analyzed is used as the number of state transitions.

[0015] In the historical sample to which each record to be analyzed belongs, the level of breast blockage of each record to be analyzed is compared with the next adjacent record, and the number of records to be analyzed whose breast blockage level is lower than the level of the next record is counted as the number of positive changes.

[0016] The ratio of the positive change quantity to the state transition base value is used as the positive change trend indicator of the level to be tested during the test time.

[0017] Furthermore, the method for obtaining the negative trend indicator includes:

[0018] The difference between the number of state transitions and the number of positive changes is taken as the number of negative changes, and the ratio of the number of negative changes to the base value of state transitions is taken as the negative change trend index of the level to be tested under the test time.

[0019] Furthermore, the method for obtaining the steady-state trend indicator includes:

[0020] The difference between the base value of the state transition and the number of state transitions is taken as the quantity of steady change, and the ratio of the quantity of steady change to the base value of the state transition is taken as the steady change trend index of the level to be tested under the test time.

[0021] Furthermore, the method for obtaining the state trend indicator includes:

[0022] The state trend indicators include positive state trend indicators, negative state trend indicators, and stable state trend indicators;

[0023] In the target sample, analyze the discrete changes between records to determine the positive state quantity factor;

[0024] In the target sample, analyze the continuous changes between records to determine the positive state trend factor of the target sample;

[0025] The product of the positive state trend factor and the positive state quantity factor of the target sample is used as the positive adjustment factor. The normalized value of the ratio of the positive adjustment factor to the breast blockage level in the first record of the target sample is used as the positive adjustment parameter.

[0026] The product of the positive adjustment parameter of the target sample and the positive change trend index corresponding to the last record in the target sample is used as the positive state trend parameter of the target sample at the current moment.

[0027] The negative trend indicator, the stable trend indicator, and the positive trend parameter of the target sample at the current moment are normalized to obtain the negative trend indicator, the stable trend indicator, and the positive trend indicator of the target sample at the current moment.

[0028] Furthermore, the method for obtaining the positive state quantity factor includes:

[0029] In the target sample, the number of records with the same breast blockage level as the last record is used as the positive state quantity factor.

[0030] Furthermore, the method for obtaining the positive state trend factor includes:

[0031] In the target sample, backtrack from the last record to obtain the records where the level of breast blockage does not decrease continuously and monotonically, and use them as the target records.

[0032] The sum of the duration of the grade in all target records is used as the time factor. The absolute value of the difference between the breast grade of the first record and the last record in all target records is used as the grade change factor. The normalized value of the ratio of the grade change factor to the time factor is used as the positive state trend factor of the target sample.

[0033] If no target record exists, the positive state trend factor of the target sample is the preset value.

[0034] Furthermore, the step of issuing an early warning about the state of the target sample based on the state trend indicator includes:

[0035] If the maximum value of the positive trend indicator, negative trend indicator, and stable trend indicator is the negative trend indicator, then an early warning will be issued.

[0036] Furthermore, the method for obtaining the data set includes:

[0037] Among all records in all historical samples, records with the same level of breast blockage are extracted to obtain the dataset corresponding to each level of breast blockage.

[0038] The present invention has the following beneficial effects:

[0039] By recording the grade and duration of breast engorgement in postpartum women over time, dynamic disease progression tracking was achieved, establishing a basic framework for dynamic nursing assessment. All records were filtered from all historical samples to obtain datasets corresponding to each grade of breast engorgement, enabling the construction of a population-based data benchmark library, which helps to quantify the evolution patterns of different engorgement grades. Furthermore, within each dataset corresponding to a breast engorgement grade, the transitions in breast engorgement grades between records were analyzed for each grade duration. Combined with the changing trends recorded in historical samples, multiple trend indicators for each breast engorgement grade under each grade duration were determined. This led to the establishment of a three-dimensional model of "breast engorgement grade - grade duration - risk trend," accurately depicting the dynamic evolution path of engorgement grades and providing a basis for subsequent risk prediction. Furthermore, due to individual differences among mothers, the changes between records and the numerical characteristics of breast blockage levels are analyzed in the target sample of the target mothers. The trend indicators corresponding to the last record in the historical samples are adjusted to obtain the state trend indicators of the target sample at the current moment. The general model is corrected through individualized features. Finally, the state trend indicators are used to issue early warnings for the state of the target sample of the target mothers, which can effectively improve the accuracy of individualized prediction. Attached Figure Description

[0040] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A flowchart illustrating a method for tracking and managing postpartum breast duct unblocking treatment, as provided in an embodiment of the present invention;

[0042] Figure 2 A reference diagram illustrating a grade of breast engorgement and an example of a treatment course provided in an embodiment of the present invention;

[0043] Figure 3 This is a flowchart illustrating a method for obtaining positive state trend parameters according to an embodiment of the present invention. Detailed Implementation

[0044] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a postpartum breast unblocking treatment tracking and management method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0046] The following description, in conjunction with the accompanying drawings, details a specific scheme for tracking and managing postpartum breast duct unblocking treatment provided by this invention.

[0047] Please see Figure 1 The diagram illustrates a flowchart of a postpartum breast massage treatment tracking and management method according to an embodiment of the present invention, which includes the following steps:

[0048] Step S1: Obtain nursing samples. Nursing samples are divided into historical samples from multiple postpartum women and target samples from the target postpartum woman. Each nursing sample records multiple records of the postpartum breast care treatment course in chronological order, and each record includes the level of breast blockage and the duration of the level.

[0049] Currently, the changes in the condition of other postpartum women during breast massage treatment are often compared with the current condition of the target postpartum woman to predict potential trends and provide early warnings. However, this approach does not consider the individual differences in physical condition between the target postpartum woman and other postpartum women as a whole, which may lead to unsatisfactory prediction results. Therefore, in this embodiment of the invention, the changes in the target postpartum woman's breast massage treatment are used to correct the changes in the historical data, resulting in a more accurate description of the target postpartum woman's current condition and effectively improving the accuracy of early warnings.

[0050] First, nursing samples need to be obtained. Nursing samples are divided into historical samples from multiple mothers and target samples from the target mother. Nursing samples can be obtained through the hospital's electronic medical record system and nursing record system. The required data can be automatically exported. In addition, each nursing sample records multiple records of the mother in the postpartum breast care course in chronological order. Each record includes the level of breast blockage and the duration of the level.

[0051] Specifically, in this embodiment of the invention, a reference diagram is provided showing the level of breast blockage and an example of treatment course. Please refer to [link / reference]. Figure 2 The level of breast blockage can be assessed by a doctor. Here's an example of a nursing sample: For instance, during a postpartum breast blockage unblocking treatment, the record for a mother is as follows (observed every three hours): Level 5, recommended to stop treatment on the affected side and perform surgical drainage; Level 5, recommended to stop treatment on the affected side and perform surgical drainage; Level 3, breastfeeding can continue, but antibiotics and topical anti-swelling treatment are needed; Level 3, breastfeeding can continue, but antibiotics and topical anti-swelling treatment are needed; Level 2, breastfeeding can continue, but antibiotics and cold compresses are needed. The corresponding nursing sample would then be {(5,2), (3,2), (2,1)}, where (5,2) represents the level of breast blockage and 2 represents the duration of that level (in this embodiment, since observations are performed every three hours, the unit length of the duration of the level is 3 hours).

[0052] It should be noted that in this embodiment of the invention, the number of historical samples is set to 100, and the specific number can be adjusted according to the implementation scenario, which is not limited here; the time interval between two adjacent observations can also be adjusted according to the implementation scenario, which is not limited here; in this embodiment of the invention, the higher the level of breast blockage, the more severe it is considered.

[0053] In this embodiment of the invention, the collection and acquisition of maternal personal information data are all authorized by the relevant users, and the process does not violate relevant laws and regulations, nor does it violate public order and good morals.

[0054] Step S2: Filter all records in all historical samples to obtain the data set corresponding to each level of breast blockage; in the data set of each level of breast blockage, analyze the changes in the duration of the level between records, and combine the changing trends of the records in the historical samples to determine multiple trend indicators of each level of breast blockage under each level duration.

[0055] A patient's breast engorgement may improve under a breast unblocking treatment plan corresponding to the severity of the engorgement. However, during the unblocking process, a suitable plan cannot be determined for every postpartum woman. Some poorly matched plans may lead to further deterioration of the patient's condition. Therefore, the state of breast engorgement may change in various ways during breast unblocking treatment. It is necessary to predict the changing trend of the severity of the postpartum woman's breast engorgement to help doctors make further judgments, such as adjusting the treatment plan.

[0056] Analysis of all historical samples, based on the overall logic described above, reveals that the degree of breast engorgement in postpartum women may improve or worsen, and may also remain unchanged. When the physical conditions of most postpartum women are relatively consistent, the trend of their changes can represent the general change in the degree of breast engorgement during treatment. Therefore, based on the historical changes in the degree of breast engorgement in postpartum women, the potential future trend of the degree of engorgement in target postpartum women can be predicted.

[0057] First, all records in all historical samples were filtered to obtain the dataset corresponding to each level of breast blockage, thereby quantifying the changes in maternal care status under the same level of breast blockage.

[0058] Preferably, in one embodiment of the present invention, the method for obtaining the data set includes:

[0059] In all historical samples, records with the same level of breast engorgement are extracted to obtain the dataset corresponding to each level of breast engorgement. For example, if there are three historical samples, namely historical sample a{(5,2),(3,2),(2,1)}, historical sample b{(5,2),(4,2),(3,1)}, and historical sample c{(5,3),(2,2)}, then the dataset corresponding to breast engorgement level 5 is {(5,2),(5,2),(5,3)}. Although there are two (5,2) records, they come from different historical samples and therefore cannot be omitted.

[0060] After filtering through all historical samples, a dataset corresponding to each level of breast engorgement can be obtained. Since the duration of breast engorgement at a given level varies among different mothers, the changes before and after may also differ, resulting in varying trends. Therefore, the dataset can be further subdivided according to the duration of each level, quantifying multiple trend indicators for each level of breast engorgement within each level's duration. Specifically, the changes in the duration of each level of breast blockage can be analyzed within the dataset of each level. Combined with the trends recorded in historical samples, multiple trend indicators for each level of breast blockage under each level's duration can be determined. Based on the aforementioned analysis, the postpartum woman's condition may exhibit various changes, such as improvement, deterioration, or no change. Therefore, in this embodiment of the invention, the trend indicators include positive trend indicators, negative trend indicators, and stable trend indicators. Positive trend indicators represent that the postpartum woman's condition, as recorded during postpartum care, tends to improve; negative trend indicators represent that the postpartum woman's condition, as recorded during postpartum care, tends to deteriorate; similarly, stable trend indicators represent that the postpartum woman's condition, as recorded during postpartum care, tends to remain unchanged.

[0061] Preferably, in one embodiment of the present invention, the method for obtaining the positive trend indicator includes:

[0062] In the aforementioned process, each level of breast engorgement corresponds to a dataset. For ease of explanation, we will randomly select one level of breast engorgement as the test level, and determine the duration range within the dataset of the test level based on the maximum and minimum duration values ​​of the level across all records. For example, if the test level is 3, then within the duration range, changes in the degree of breast engorgement are possible.

[0063] Similarly, for ease of subsequent explanation and clarification, any one of the duration levels will be selected as the test time within the duration range.

[0064] In the dataset of the level to be tested, if the level duration is less than the testing time, it indicates that a level change has already occurred between the duration of the level to be tested and the testing time. Therefore, the impact of this type of data on the testing time is not considered. Thus, only the number of records with a level duration greater than or equal to the testing time is used as the base value for state transitions corresponding to the testing time under the level to be tested. Records with a level duration equal to the testing time are used as records to be analyzed, and the number of records to be analyzed is used as the number of state transitions. The base value for state transitions represents the number of records that have lasted for the testing time or longer than the testing time, considered as the number of records where the level to be tested may change its state now or in the future. Records to be analyzed represent records where the level to be tested has lasted exactly for the testing time. Therefore, the number of state transitions represents the number of records that have lasted for the testing time, considered as the number of records where the level to be tested will immediately change its state.

[0065] Next, in the records to be analyzed, the specific state changes are analyzed: in the historical samples to which each record belongs, the level of breast blockage of each record to be analyzed is compared with the next adjacent record, and the number of records in which the level of breast blockage of the record to be analyzed is lower than the level of breast blockage of the next record is counted as the number of positive changes. The larger the number of positive changes, the more records in the analysis have a lower level of breast blockage, reflecting an improvement in the state.

[0066] Finally, the ratio of the number of positive changes to the base value of state transition is used as the positive change trend index of the test level under the test time. The larger the positive change trend index, the more records of reduced breast blockage in the data set corresponding to the test level under the test time, and the more obvious the improvement trend represented by the data.

[0067] After obtaining the positive trend indicator, you can continue to obtain the negative trend indicator and the stable trend indicator.

[0068] Preferably, in one embodiment of the present invention, the method for obtaining the negative trend indicator and the stable trend indicator includes:

[0069] In the dataset corresponding to the test level, the difference between the number of state transitions and the number of positive changes at the test time is taken as the number of negative changes, and the ratio of the number of negative changes to the base value of state transitions is taken as the negative trend index of the test level at the test time. The larger the negative trend index, the more records of increased breast blockage at the test time are in the dataset corresponding to the test level, and the more obvious the trend that the data represents that contradicts expectations.

[0070] Finally, the state transition baseline value represents the number of records that may change state now or in the future, while the state transition quantity represents the number of records whose state will change immediately at the level under test. Therefore, the difference between the state transition baseline value and the state transition quantity represents the number of records that may change to a blocked state in the future, which means that the level under test needs to continue for a longer period of time. Thus, this difference is used as the steady change quantity, and the ratio of the steady change quantity to the state transition baseline value is used as the steady change trend index of the level under test during the test time. The larger the steady change trend index, the higher the probability that the level under test will change steadily in the data set corresponding to the level under test, that is, remain unchanged.

[0071] Thus, by following the steps above, we can obtain three trend indicators for the duration of each breast blockage level based on all historical samples.

[0072] Step S3: In the target sample, analyze the changes between records and the numerical characteristics of the breast blockage level, adjust the trend index corresponding to the last record in the historical sample, and thus obtain the state trend index of the target sample at the current moment.

[0073] Using only the trend indicators obtained from analyzing historical samples in step S2 as a reference, combined with the last record in the target sample, to predict the subsequent trend of the target mother's condition is too one-sided and may lead to a low degree of matching between the target mother's data and historical sample data. However, the data changes in the target user's target sample are also of reference value. Therefore, in this embodiment of the invention, the changes between records and the numerical characteristics of the breast blockage level are analyzed in the target sample, and the trend indicators corresponding to the last record in the historical samples are adjusted to obtain the status trend indicators of the target sample at the current moment.

[0074] Preferably, in one embodiment of the present invention, the method for obtaining the state trend indicator includes:

[0075] Similar to the trend indicators in step S2, there are also three state trend indicators: positive state trend indicator, negative state trend indicator, and stable state trend indicator.

[0076] The trend indicators are mainly based on the analysis and calculation of historical sample data, reflecting the potential state change trends of the group data. In this step, by analyzing the individual characteristics of the target sample, the positive trend in the prediction results obtained from the historical samples is dynamically corrected to obtain positive state trend parameters. Finally, the positive state trend parameters, negative state trend indicators, and stationary state trend indicators are normalized to obtain three state trend indicators, making the final prediction direction more consistent with the individual characteristics of the target sample and improving the accuracy of subsequent predictions.

[0077] Please see Figure 3 The diagram illustrates a method flowchart for obtaining positive state trend parameters according to an embodiment of the present invention. The method includes the following steps:

[0078] Step S301: In the target sample, analyze the discrete changes between records and determine the positive state quantity factor.

[0079] When a postpartum woman is initially diagnosed with a certain level of breast engorgement, the limited data makes it difficult to accurately match with previous data, resulting in poor targeting and limited reference value for changes in the woman's condition. However, by generating data through successive tracking, the comprehensiveness of the postpartum woman's condition is increased. When the woman subsequently falls into the same level of breast engorgement, the estimation results will be more accurate compared to the limited data available previously. Furthermore, since the last record in the target sample is closest to the current moment, it better reflects the current state of the target postpartum woman. Therefore, in the target sample, the number of records with the same breast engorgement level as the last record is used as a positive state quantity factor. The larger the positive state quantity factor, the more targeted the treatment can be, and the more comprehensive the reference data, which will positively contribute to the recovery of the target postpartum woman's condition.

[0080] Step S302: In the target sample, analyze the continuous changes between records to determine the positive state trend factor of the target sample.

[0081] The continuous changes in the degree of blockage recorded in the target sample of the target mother can reflect the recent changes in the state of the target sample. Therefore, in the target sample, we trace back from the last record to obtain the records in which the level of breast blockage is continuously and monotonously reduced, and use them as target records. The target records are arranged in chronological order to reflect the trend of the target mother's state gradually improving.

[0082] The sum of the durations of all target records' grades is used as the time factor. The length of the time factor represents the time taken for the target mother's condition to approach the desired state. The absolute value of the difference between the breast grade in the first and last records of all target records is used as the grade change factor. The grade change factor reflects the change in the target mother's breast blockage grade, specifically the degree of relief. Finally, the normalized ratio of the grade change factor to the time factor is used as the positive state trend factor of the target sample. The positive state trend factor indicates the rate at which the target mother's physiological state approaches normal; the larger the value, the greater the rate of change towards normal. Normalization is a technique well-known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0083] It should be noted that if no target record exists, the positive state trend factor of the target sample is a preset value, which is 0 here.

[0084] Step S303: Based on the positive state quantity factor, the positive state trend factor, and the breast blockage level in the first record of the target sample, the positive change trend index corresponding to the last record is corrected to obtain the positive state trend parameter.

[0085] Based on the logic in the aforementioned steps, it can be seen that both the positive state quantity factor and the positive state trend factor are positively correlated with the degree of change in the physiological state of the target mother towards normalization. In the target sample, if the level of breast blockage in the first record is higher, it indicates a higher degree of severity, and the subsequent change process may be slower, thus the positive change trend will also be reduced.

[0086] Therefore, the product of the positive trend factor and the positive quantity factor of the target sample is used as the positive adjustment factor. The normalized value of the ratio of the positive adjustment factor to the breast blockage level in the first record of the target sample is then used as the positive adjustment parameter. The larger the positive adjustment factor, the smaller the breast blockage level in the first record of the target sample; therefore, the larger the positive adjustment parameter, the more pronounced the trend of the target sample towards normal change. Normalization is a technique well-known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0087] In step S2, the trend index of each breast blockage level over each level's duration can be obtained. Therefore, by comparing it with the last record in the target sample, the positive trend index corresponding to the last record in the target sample can be found. Finally, the product of the positive adjustment parameter of the target sample and the positive trend index corresponding to the last record in the target sample is used as the positive state trend parameter of the target sample at the current moment. The larger the positive state trend parameter, the higher the probability of normal changes in the physiological state area of ​​the target mother. This positive state trend parameter combines the overall change characteristics of historical samples and is also corrected using the personalized characteristics of the target sample. Therefore, it can more accurately match the state change trend of the target mother, and the results obtained from subsequent early warning and prediction using this indicator will be more accurate.

[0088] After obtaining the positive state trend parameters of the target sample at the current moment, the negative trend indicator, the stable trend indicator, and the positive state trend parameters of the target sample at the current moment corresponding to the last record in the target sample can be normalized to obtain the negative state trend indicator, the stable state trend indicator, and the positive state trend indicator of the target sample at the current moment. The purpose of normalization here is to make the sum of the negative state trend indicator, the stable state trend indicator, and the positive state trend indicator equal to 1. Normalization is a technique well known to those skilled in the art, and the specific normalization method is not limited here.

[0089] Step S4: Issue an early warning about the status of the target sample based on the status trend indicators.

[0090] Based on the aforementioned steps, the state change trend of the target sample of the target mother can be obtained and quantified using the state trend index. Therefore, the state trend index can be used to issue an early warning for the state of the target sample.

[0091] Preferably, in one embodiment of the present invention, providing an early warning of the state of a target sample based on a state trend indicator includes:

[0092] If the maximum value of the positive trend indicator, negative trend indicator, and stable trend indicator is the negative trend indicator, an early warning will be issued, notifying the doctor to conduct further analysis and treatment for the target pregnant woman in a timely manner, such as revising the treatment plan.

[0093] In summary, by recording the grade and duration of breast engorgement in postpartum women over time, a dynamic disease progression tracking mechanism was achieved, establishing a basic framework for dynamic nursing assessment. By filtering all records from all historical samples, a dataset corresponding to each grade of breast engorgement was obtained, enabling the construction of a population-based data benchmark library, which helps in quantifying the evolution patterns of different engorgement grades. Furthermore, within each dataset corresponding to a breast engorgement grade, further subdivision was performed. The transitions in breast engorgement grades between records were analyzed for each grade duration. Combined with the changing trends recorded in historical samples, multiple trend indicators for each breast engorgement grade under each grade duration were determined. This led to the establishment of a three-dimensional model of "breast engorgement grade - grade duration - risk trend," accurately depicting the dynamic evolution path of engorgement grades and providing a basis for subsequent risk prediction. Furthermore, due to individual differences among mothers, the changes between records and the numerical characteristics of breast blockage levels are analyzed in the target sample of the target mothers. The trend indicators corresponding to the last record in the historical samples are adjusted to obtain the state trend indicators of the target sample at the current moment. The general model is corrected through individualized features. Finally, the state trend indicators are used to issue early warnings for the state of the target sample of the target mothers, which can effectively improve the accuracy of individualized prediction.

[0094] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for tracking and managing postpartum breast duct unblocking treatment, characterized in that, The method comprises: acquiring nursing samples, the nursing samples being divided into historical samples of a plurality of puerperae and a target sample of a target puerpera, wherein each nursing sample records a plurality of records in a time sequence in a postpartum breast care course of the puerpera, and each record comprises a breast blockage level and a level duration; in all historical samples, all records are screened to obtain a data set corresponding to each breast blockage level; in the data set of each breast blockage level, the change of the level duration between records is analyzed, and combined with the change trend of the records in the historical samples, a plurality of change trend indexes of each breast blockage level under each level duration are determined; in the target sample, the change between records and the numerical characteristics of the breast blockage level are analyzed, the change trend index corresponding to the last record in the historical samples is adjusted, and thus a state trend index of the target sample at the current time is obtained; the state of the target sample is warned according to the state trend index; the change trend index comprises a positive change trend index, a negative change trend index and a stable change trend index; the acquisition method of the positive change trend index comprises: optionally selecting a breast blockage level as a to-be-tested level; in the data set of the to-be-tested level, the maximum value and the minimum value of the level duration in all records are determined to determine a duration range; in the duration range, a level duration is selected as a to-be-tested time; in the data set, the number of records with a level duration greater than or equal to the to-be-tested time is taken as a state transition base value corresponding to the to-be-tested time under the to-be-tested level, the record corresponding to the to-be-tested time is taken as an analyzed record, and the number of analyzed records is taken as a state transition number; in the historical sample to which each analyzed record belongs, the breast blockage levels between each analyzed record and the next adjacent record are compared, and the number of analyzed records with a breast blockage level smaller than that of the next record is taken as a positive change number; the ratio of the positive change number to the state transition base value is taken as the positive change trend index of the to-be-tested level under the to-be-tested time; the acquisition method of the negative change trend index comprises: the difference between the state transition number and the positive change number is taken as a negative change number, and the ratio of the negative change number to the state transition base value is taken as the negative change trend index of the to-be-tested level under the to-be-tested time; the acquisition method of the state trend index comprises: the state trend index comprises a positive state trend index, a negative state trend index and a stable state trend index; in the target sample, the discrete change between records is analyzed to determine a positive state number factor; in the target sample, the continuous change between records is analyzed to determine a positive state trend factor of the target sample; the product of the positive state trend factor and the positive state number factor of the target sample is taken as a positive adjustment factor, and the ratio of the positive adjustment factor to the breast blockage level in the first record in the target sample is taken as a normalized value of a positive adjustment parameter. The product of the positive adjustment parameter of the target sample and the positive change trend index corresponding to the last record in the target sample is taken as the positive state trend parameter of the target sample at the current time; The negative change trend index corresponding to the last record in the target sample, the stable change trend index and the positive state trend parameter of the target sample at the current time are normalized to obtain the negative state trend index, the stable state trend index and the positive state trend index of the target sample at the current time respectively.

2. The method of claim 1, wherein the method is a postpartum care breast unblock therapy follow-up and management method. The method for obtaining the stable change trend index comprises: The difference between the state transition base value and the state transition number is taken as the stable change number, and the ratio of the stable change number to the state transition base value is taken as the stable change trend index of the to-be-tested level at the to-be-tested time.

3. The method for tracking and managing postpartum breast duct unblocking treatment according to claim 1, characterized in that, The method for obtaining the positive state number factor comprises: In the target sample, the number of records with the same breast blockage level as that in the last record is taken as the positive state number factor.

4. The method of claim 1, wherein the method further comprises: providing a postpartum care breast unblock therapy program to the user; and tracking and managing the postpartum care breast unblock therapy program. The method for obtaining the positive state trend factor comprises: In the target sample, the records with continuously monotone non-decreasing breast blockage levels are obtained by backtracking from the last record, as target records; The sum of the level duration in all target records is taken as the time factor, the absolute value of the difference between the breast levels of the first record and the last record in all target records is taken as the level change factor, and the ratio of the level change factor to the time factor is normalized to obtain the positive state trend factor of the target sample. If there is no target record, the positive state trend factor of the target sample is a preset value.

5. The method of claim 1, wherein the method further comprises: providing a postpartum care breast unblock therapy program to the user; and tracking and managing the postpartum care breast unblock therapy program. The method for obtaining the data set comprises: In all records in all historical samples, the records with the same breast blockage level are extracted to obtain the data set corresponding to each breast blockage level.

6. The method of claim 1, wherein the method further comprises: providing a postpartum care breast unblock therapy program to the user. ​ ​

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