Information management system based on circadian rhythm refined nursing

Abstract

The invention relates to the technical field of health care, in particular to an information management system based on circadian rhythm fine care, which comprises an acquisition module used for acquiring circadian rhythm data; the feature analysis module is used for determining a circadian rhythm feature index measured each time and obtaining an initial user group according to feature fluctuation grouping; the nursing effect analysis module is used for determining the nursing effect coefficient of the user under the corresponding measurement according to the numerical value change of the circadian rhythm characteristic indexes of the adjacent measurement; and the adjusting module is used for obtaining a nursing intervention value by combining the nursing effect coefficients measured at the same time in the same initial user group, and determining whether to adjust the nursing scheme or not according to the nursing intervention value. According to the method, the nursing reliability can be effectively improved, the accuracy and pertinence of nursing scheme updating are improved, and the nursing effect is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of health care, and in particular to an information management system based on circadian rhythm refined care. Background Art

[0002] Circadian rhythm is an important reflection of the user's physiological state, including the periodic characteristics of physiological activities such as sleep, body temperature, and heart rate. By analyzing circadian rhythm, the user's normal or abnormal state can be identified. Due to individual differences and specific needs of users, in order to achieve refined care, personalized and accurate care plans can be formulated and implemented based on the user's circadian rhythm to optimize their health management and care effects.

[0003] In the related art, an information management system is used to organize the circadian rhythm information of all users, and the care plan is adjusted directly based on whether the value of each circadian information meets the preset standard. In this way, since the circadian rhythm will change over time and the individual differences between different users vary greatly, the use of preset standards cannot accurately reflect this feature, and the accuracy and specificity of the care plan update are poor. Summary of the invention

[0004] In order to solve the technical problem in the related art that the circadian rhythm will change over time and the individual differences of different users have large variations, the preset standard cannot accurately reflect this feature, and the accuracy and pertinence of the nursing plan update are poor, the present invention provides an information management system based on circadian rhythm refined nursing, and the technical solution adopted is as follows: The present invention proposes an information management system based on circadian rhythm refined nursing, the system comprising: An acquisition module, used to acquire circadian rhythm data measured at different times by different users, wherein the circadian rhythm data at least includes sleep, body temperature, and heart rate; The feature analysis module is used to determine the circadian rhythm feature index of each measurement based on the difference between the value of each circadian rhythm data under the same measurement and the ideal value, and to form a feature sequence according to the time sequence; according to the fluctuation of the feature sequence of different users in the same time period, determine the initial user group with similar rhythm feature fluctuations; The nursing effect analysis module is used to cluster the circadian rhythm characteristic indicators of all users measured at the same time, and determine the target user group with similar circadian rhythm characteristic indicators; determine the rhythm change value of each measurement based on the numerical changes of the circadian rhythm characteristic indicators of the same user in adjacent measurements; in the same target user group, determine the nursing effect coefficient of the user under the corresponding measurement according to the distribution of the rhythm change values ​​of adjacent measurements between users; The adjustment module is used to determine the initial intervention value of each user based on the nursing effect coefficient of the same measurement in the same initial user group, and to update the intervention based on the changes in the initial intervention value of each user in all measurements to obtain the nursing intervention value, and to determine whether to adjust the nursing plan based on the nursing intervention value.

[0005] Furthermore, the ideal value is a known preset value, and different circadian rhythm data types correspond to different ideal values; according to the difference between the value of each circadian rhythm data under the same measurement and the ideal value, the circadian rhythm characteristic index of each measurement is determined, including: The absolute value of the difference between each circadian rhythm data value and the corresponding ideal value was calculated and normalized as the ideal difference coefficient; The mean of the ideal difference coefficients of all circadian rhythm data was used as the circadian rhythm characteristic index for each measurement.

[0006] Furthermore, according to the fluctuation of the characteristic sequences of different users in the same time period, an initial user group with similar rhythm characteristic fluctuation is determined, including: Based on the dynamic time warping algorithm, the DTW values ​​of the feature sequences of any two users in the same time period are calculated, and the opposite number of the DTW value is normalized as the sequence similarity; Clustering is performed based on sequence similarity to obtain different clusters as initial user groups.

[0007] Furthermore, the circadian rhythm characteristic indicators of all users measured at the same time are clustered to determine the target user group with similar circadian rhythm characteristic indicators, including: The circadian rhythm characteristic indicators of all users measured at the same time are clustered by DBSCAN density to obtain the target user group.

[0008] Further, based on the numerical changes of the circadian rhythm characteristic index of the same user in adjacent measurements, the rhythm change value of each measurement is determined, including: Any measurement of any user is regarded as the target measurement; the other two measurements that are closest to the target measurement in time sequence are regarded as adjacent measurements; Based on the least squares method, a straight line fitting is performed on the values ​​of the circadian rhythm characteristic indexes corresponding to the target measurement and the adjacent measurements, and the slope of the fitting line is used as the fluctuation trend coefficient of the target measurement; The mean of the absolute values ​​of the differences between the target measurement and each adjacent measurement in the circadian rhythm characteristic index was taken as the target characteristic difference coefficient; The product of the fluctuation trend coefficient and the standard feature difference coefficient was calculated and normalized as the rhythm change value of the target measurement.

[0009] Furthermore, according to the distribution of the rhythm change values ​​of the adjacent measurements between users, the nursing effect coefficient of the user under the corresponding measurements is determined, and the corresponding calculation formula is: ; In the formula, Indicates User No. The nursing effect coefficient of the secondary measurement; Indicates User No. The rhythm change value of the measurement; Indicates User No. The average of the rhythm change values ​​of all users in the same target user group is measured; Indicates User No. The mean of the rhythm change values ​​of all users in the same target user group is measured for the second time. exp represents an exponential function with a natural constant as the base, and sigmoid represents a sigmoid function.

[0010] Furthermore, according to the nursing effect coefficients measured at the same time in the same initial user group, the initial intervention value of each user is determined, including: The mean of the nursing effect coefficients of the same measurements in the same initial user group is taken as the nursing mean coefficient of the corresponding measurements; The absolute value of the difference between the nursing effect coefficient measured by any user at any time and the corresponding nursing mean coefficient is normalized and used as the mean difference factor; Calculate the normalized value of the inverse of the nursing effect coefficient measured by any user at any time as the effect intervention factor; The product of the effect intervention factor and the mean difference factor was calculated and normalized as the initial intervention value.

[0011] Furthermore, the intervention update is performed based on the changes in the initial intervention value of each user in all measurements to obtain the nursing intervention value, including: The absolute value of the difference between the initial intervention values ​​of each user in two consecutive measurements is taken as the intervention difference coefficient; Calculate the mean of all intervention differences from the initial moment to the current moment, and linearly map them to the preset adjustment range as the intervention adjustment coefficient, where the initial moment is the moment of the last nursing intervention; The product of the intervention adjustment coefficient and the initial intervention value is taken as the nursing intervention value.

[0012] Furthermore, the preset adjustment range is [0.5, 1.5].

[0013] Furthermore, whether to adjust the nursing plan is determined according to the nursing intervention value, including: When the nursing intervention value is greater than the preset intervention threshold, it is determined to adjust the nursing plan, otherwise, the nursing plan is not adjusted.

[0014] The present invention has the following beneficial effects: The embodiment of the present invention determines the circadian rhythm characteristic index by the numerical fluctuation of the circadian rhythm data of different users, and then groups them to obtain the initial user group, which characterizes the user group with similar fluctuations, and realizes the characteristic analysis of multiple types of circadian rhythm data; then, according to the changes in the circadian rhythm characteristic index of different users in the time series, the analysis of the nursing effect is realized, and the nursing effect coefficient is obtained; then, combined with the numerical value of the nursing effect coefficient in the initial user group, the intervention analysis is carried out to obtain the nursing intervention value, and the nursing plan is adjusted according to the nursing intervention value. This application mainly conducts nursing intervention analysis through the single-user numerical feature dimension, the multi-user numerical feature dimension, the single-user numerical change dimension and the multi-user numerical change dimension, so that the obtained nursing intervention value can be combined with the individual state change and the overall characteristic difference, and dynamically updated as time goes by, so that the individual differences of users can be effectively analyzed, and intervention adjustments are carried out according to the individual differences and collective changes, and the grouping standards and the updating rules of the nursing plan are continuously optimized, so as to realize the refined nursing of users. In summary, this application can effectively improve the reliability of nursing, improve the accuracy and pertinence of the nursing plan update, and enhance the nursing effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A structural diagram of an information management system based on circadian rhythm refined care provided by an embodiment of the present invention; Figure 2 A schematic diagram of user body temperature fluctuation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of an information management system based on circadian rhythm refined care proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, 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 belongs.

[0019] The following is a detailed description of a specific scheme of an information management system based on circadian rhythm refined care provided by the present invention in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a structural diagram of an information management system based on circadian rhythm refined nursing provided by an embodiment of the present invention, including: an acquisition module 101, a feature analysis module 102, a nursing effect analysis module 103 and an adjustment module 104. The following is a specific description of each module: The acquisition module 101 is used to acquire circadian rhythm data measured at different times by different users, wherein the circadian rhythm data at least includes sleep, body temperature, and heart rate.

[0021] Among them, circadian rhythm refers to the natural change pattern of physiological and behavioral activities of an organism within a day and night cycle (generally 24 hours), which is regulated by the external environment and the internal biological clock. The circadian rhythm data corresponding to each user will be different due to individual differences. In the embodiment of the present invention, wearable devices (such as smart bracelets, watches) or professional monitoring equipment can be used to collect data related to circadian rhythm. Specifically, the relevant data information of sleep, body temperature, and heart rate can be measured every half an hour, and integrated into daily circadian rhythm data, including sleep data, body temperature fluctuations, and heart rate changes.

[0022] In other embodiments of the present invention, the collected circadian rhythm data can also be preprocessed to clean noise, eliminate data deviations caused by equipment errors, and perform standardization to unify data from different sources and scales into comparable standardized indicators, without limitation.

[0023] The feature analysis module 102 is used to determine the circadian rhythm feature index of each measurement based on the difference between the numerical value of each circadian rhythm data under the same measurement and the ideal value, and to form a feature sequence in time sequence; based on the fluctuation of the feature sequence of different users in the same time period, determine the initial user group with similar rhythm feature fluctuations.

[0024] It is understandable that when a user who needs care has health problems, it may interfere with the user's circadian rhythm, which is manifested as abnormal circadian rhythm data and various data indicators may deviate from normal reference values ​​or ideal ranges. Figure 2 , Figure 2 This is a schematic diagram of user body temperature fluctuations provided in one embodiment of the present invention. The difference between the body temperature value obtained in each measurement and the preset standard body temperature can be used to analyze the user's body temperature characteristics. The same is true for other types of circadian rhythm data.

[0025] In the embodiment of the present invention, the ideal value is a known preset value, and different circadian rhythm data types correspond to different ideal values, for example, the ideal value of body temperature data is 36.5 degrees Celsius. Among them, due to differences between different individuals, the ideal value of each individual will also vary. For example, the standard sleep time of a 70-year-old user is somewhat different from that of a 30-year-old user. Therefore, each user can have a corresponding ideal value for each circadian rhythm data. This solution is only used for standardized difference analysis, and no further limitation or elaboration is made on this.

[0026] Further, in some embodiments of the present invention, the circadian rhythm characteristic index of each measurement is determined according to the difference between the numerical value of each circadian rhythm data under the same measurement and the ideal value, including: calculating the absolute value of the difference between the numerical value of each circadian rhythm data and the corresponding ideal value, and normalizing it as the ideal difference coefficient; taking the average of the ideal difference coefficients of all circadian rhythm data as the circadian rhythm characteristic index of each measurement, and the corresponding calculation formula can be specifically, for example, as follows: ; In the formula, Indicates User No. Circadian rhythm characteristic indicators in the sub-measurements; Indicates the number of types of circadian rhythm data; Indicates User No. In the measurement The numerical value of the circadian rhythm data; Indicates The ideal value of the circadian rhythm data (known standard data), Indicates the normalization of maximum and minimum values. Indicates taking the absolute value.

[0027] Indicates User No. The difference between the actual measured value and the ideal value of all circadian rhythm data in the measurement represents the characteristics of the abnormality of the indicator value reflected in the circadian rhythm.

[0028] From the above formula, we can see that It represents the ideal difference coefficient. The larger the value of the ideal difference coefficient is, the greater the difference between the measured value of the current circadian rhythm data and the standard situation, that is, the more obvious the abnormal characteristics are. Therefore, the average of the ideal difference coefficients of all types of circadian rhythm data is used as the circadian rhythm characteristic indicator of the corresponding measurement.

[0029] Usually circadian rhythm data is stored in medical information management systems, which need to implement classified storage, rapid retrieval and sharing of circadian rhythm data information, and improve the utilization of nursing resources by grouping and aggregating nursing plans for users with similar circadian rhythms. Circadian rhythm data changes dynamically, showing a regular or disordered dynamic change process over time. The consistency of the dynamic change trend of circadian rhythm data can help discover the common patterns of circadian rhythms (such as rhythm changes under specific abnormal conditions), classify users with similar dynamic changes, and facilitate the unified design of nursing plans. The consistency of circadian rhythm change patterns of different users can be obtained through the dynamic changes of circadian rhythm data.

[0030] In the embodiment of the present invention, the initial user group is used as the user group group characterizing similar circadian rhythm fluctuation characteristics. Therefore, it is necessary to determine the initial user group with similar rhythm characteristic fluctuations according to the fluctuations of characteristic sequences of different users in the same time period.

[0031] Furthermore, in some embodiments of the present invention, the sequence similarity of feature sequences of any two users in the same time period is calculated based on a dynamic time warping algorithm; clustering is performed based on the sequence similarity to obtain different clusters as initial user groups.

[0032] Among them, the dynamic time warping algorithm is an algorithm well known to relevant technical personnel in the field. Based on the dynamic time warping algorithm, the DTW value of two time series can be calculated. The smaller the DTW value, the higher the similarity of the two time series. Based on this, sequence similarity analysis is performed, and the opposite number of the DTW value is normalized as the sequence similarity.

[0033] In the embodiment of the present invention, the value of the sequence similarity can be limited to between [0, 1]. When the sequence similarity is greater than a preset similarity threshold (for example, 0.8), the corresponding two users can be regarded as a cluster, and all users are analyzed to achieve cluster division, with each cluster being regarded as an initial user group.

[0034] The change patterns of circadian rhythm data of users in the same initial user group are relatively similar, and they can be analyzed as a type. For example, they are all physically weak users, or they are all insomniac users, and they can be classified according to the actual situation.

[0035] After the initial user group is determined, the care effect of each user needs to be analyzed, as described in detail in the subsequent embodiments.

[0036] The nursing effect analysis module 103 is used to cluster the circadian rhythm characteristic indicators of all users measured at the same time, and determine the target user group with similar circadian rhythm characteristic indicators; determine the rhythm change value of each measurement based on the numerical changes of the circadian rhythm characteristic indicators of the same user in adjacent measurements; in the same target user group, determine the nursing effect coefficient of the user in the corresponding measurement according to the distribution of the rhythm change values ​​of adjacent measurements between users.

[0037] It should be noted that diseases (such as insomnia, diabetes, cardiovascular disease, etc.) can significantly interfere with the user's circadian rhythm. At the same time, treatment plans (such as drugs, rehabilitation training, etc.) may also regulate or interfere with the user's circadian rhythm. In order to achieve refined care for users and optimize care plans, it is necessary to clarify the impact of the disease on the circadian rhythm, analyze the changing trends of rhythm characteristics, and evaluate the impact of care on the user's physiological laws, so as to analyze the care effect.

[0038] In the process of caring for users, the changing rhythm of circadian rhythm reflects the effectiveness of current care. Since the changes in circadian rhythm are dynamic, the care plans for some users may not be applicable at different stages of care, and their circadian rhythm changes may stagnate slowly. In order to more accurately match care plans for users, it is necessary to consider the dynamic care effect of circadian rhythm changing with the care plan. Therefore, the circadian rhythm characteristic indicators of all users in each circadian rhythm measurement are clustered.

[0039] Furthermore, in some embodiments of the present invention, the circadian rhythm characteristic indicators of all users measured at the same time are clustered to determine a target user group with similar circadian rhythm characteristic indicators, including: performing DBSCAN density clustering on the circadian rhythm characteristic indicators of all users measured at the same time to obtain the target user group.

[0040] Among them, the target user group refers to the cluster group corresponding to the circadian rhythm characteristic indicators obtained based on the same measurement, which represents the similar characteristics under one measurement. DBSCAN density clustering is a clustering algorithm well known to relevant technical personnel in the field. The density of the values ​​can be increased based on this, and users with similar circadian rhythm characteristic indicators can be formed into a target user group.

[0041] Based on the numerical changes of the circadian rhythm characteristic indicators of the same user in adjacent measurements, the rhythm change value of each measurement is determined, including: taking any measurement of any user as the target measurement; taking the other two measurements that are closest to the target measurement in time sequence as adjacent measurements; performing a straight line fit on the values ​​of the circadian rhythm characteristic indicators corresponding to the target measurement and the adjacent measurements based on the least squares method, and taking the slope of the fitting line as the fluctuation trend coefficient of the target measurement; taking the average of the absolute values ​​of the differences between the target measurement and each adjacent measurement in the circadian rhythm characteristic indicator as the target characteristic difference coefficient; calculating the product of the fluctuation trend coefficient and the standard characteristic difference coefficient and normalizing it as the rhythm change value of the target measurement. The calculation formula for the rhythm change value can be, for example: ; Indicates User No. The value of the circadian rhythm change during the measurement; Indicates User No. The fluctuation trend coefficient in this measurement, that is, the slope of the fitted straight line for the circadian rhythm characteristics in the neighborhood of this measurement, represents the changing trend of the circadian rhythm characteristics of this measurement; Indicates the neighborhood of each measurement (including two adjacent measurements, determined by the distance between the measurements); Indicates User No. Circadian rhythm characteristic indicators in the sub-measurements; Indicates User No. The first measurement in the neighborhood The circadian rhythm characteristic index measured by times; norm means the normalization of the maximum and minimum values, Indicates taking the absolute value.

[0042] In the formula, Indicates User No. The target characteristic difference coefficient of the measurement indicates the degree of circadian rhythm disorder of the measurement results in the neighborhood. The larger the target characteristic difference coefficient, the more disordered the circadian rhythm change. Similarly, the larger the value of the fluctuation trend coefficient, the larger the corresponding fluctuation trend slope value, that is, the more drastic the overall circadian rhythm characteristic change, and the more disordered the circadian rhythm change. Therefore, the product of the fluctuation trend coefficient and the target characteristic difference coefficient is directly calculated, and the product value is normalized as the rhythm change value of the target measurement.

[0043] Due to individual differences among users, for example, in the care process of the elderly, when the initial circadian rhythm characteristics are similar and the same care plan is adopted, different users may have different care effects on the same care plan (such as exercise time or food planning, etc.), resulting in different speeds of recovery of circadian rhythms such as blood sugar.

[0044] If a user belongs to different target user groups in two adjacent circadian rhythm feature clusters, it means that the user may have different nursing effects on the nursing plan. In the clusters of the same circadian rhythm feature, the greater the difference between the user's circadian rhythm change rhythm and the cluster average change rhythm, and the greater the difference between the average circadian rhythm change rhythm of two adjacent cluster results, it means that the nursing effect of the user is abnormal. Based on this, a specific nursing effect analysis is conducted.

[0045] According to the distribution of rhythm change values ​​of adjacent measurements between users, the nursing effect coefficient of the user under the corresponding measurements is determined. The corresponding calculation formula is: ; In the formula, Indicates User No. The nursing effect coefficient of the secondary measurement; Indicates User No. The rhythm change value of the measurement; Indicates User No. The average of the rhythm change values ​​of all users in the same target user group is measured; Indicates User No. The average of the rhythm change values ​​of all users in the same target user group is measured. exp represents an exponential function with a natural constant as the base. Represents the sigmoid function.

[0046] From the above formula, we can see that Indicates User No. The difference between the rhythm change value of the circadian rhythm change measured by the user and the rhythm change value of the circadian rhythm change of other users in the same cluster, The larger the value is, the greater the rhythm change is than that of other users in the same target user group, and the corresponding nursing effect is more likely to be abnormal. That is, when there is a large difference in the rhythm change values ​​of the circadian rhythm characteristics of the same target user group in this measurement, it indicates that the nursing effect may be abnormal.

[0047] Similarly, Indicates User No. The difference between the average rhythm change value of the target user group to which the previous measurement belongs and the adjacent previous measurement. If the formula is larger, it means that the rhythm change value has increased from the target user group with a lower value to the target user group with a higher value. The corresponding rhythm fluctuation between two adjacent measurements becomes more obvious, and the nursing effect is worse.

[0048] Therefore, the exponential function with natural constant as the base is used for negative correlation mapping and normalization to obtain the nursing effect coefficient. The larger the value of the nursing effect coefficient, the more stable the parameter value in the dimension of circadian rhythm change. Subsequent nursing intervention analysis can be carried out based on the nursing effect.

[0049] The adjustment module 104 is used to determine the initial intervention value of each user based on the nursing effect coefficient measured at the same time in the same initial user group, and to perform intervention updates based on the changes in the initial intervention value of each user in all measurements to obtain the nursing intervention value, and to determine whether to adjust the nursing plan based on the nursing intervention value.

[0050] Among them, nursing intervention mainly involves adjusting the nursing plan, such as exercise adjustment, diet adjustment and other adjustment methods. Individual differences among users may lead to differences in the therapeutic effects of similar nursing plans. In this case, the nursing plan of the user can be intervened according to the nursing effect coefficient.

[0051] Further, in some embodiments of the present invention, the initial intervention value of each user is determined according to the nursing effect coefficient measured the same number of times in the same initial user group, including: taking the mean of the nursing effect coefficient measured the same number of times in the same initial user group as the nursing mean coefficient of the corresponding measurement; normalizing the absolute value of the difference between the nursing effect coefficient measured at any time by any user and the corresponding nursing mean coefficient as the mean difference factor; calculating the normalized value of the opposite number of the nursing effect coefficient measured at any time by any user as the effect intervention factor; calculating the product of the effect intervention factor and the mean difference factor, and normalizing it as the initial intervention value. The corresponding calculation formula can be specifically, for example, as follows: ; In the formula, Indicates User No. The initial intervention value of the circadian rhythm was measured; Indicates User No. The nursing effect coefficient of circadian rhythm in the sub-measurement; Indicates User No. The average value of the nursing effect coefficient of the circadian rhythm in the initial clustering result corresponding to the measurement. Indicates taking the absolute value, Represents the sigmoid function, and norm represents maximum and minimum value normalization.

[0052] From the above formula, we can know that the average nursing effect in the initial user group obtained according to the change trend of circadian rhythm characteristics is compared with the nursing effect of the current user to obtain the mean difference factor The larger the value of the mean difference factor, the greater the difference between the nursing effect of the current user and the average nursing effect in the same initial user group. The average nursing effect in the same initial user group is regarded as the normal effect. At this time, the larger the value of the mean difference factor, the greater the difference from the normal nursing effect. Regardless of whether the user's condition after nursing gradually improves or deteriorates, the large difference requires nursing intervention. Therefore, the analysis of the mean difference factor is realized by the absolute value of the difference.

[0053] Among them, the nursing effect coefficient represents the quality of nursing effect. The larger its value, the better the nursing effect. Therefore, it can be used as a variable for analysis, and the normalized value of the opposite number of the nursing effect coefficient measured by any user at any time is calculated as the effect intervention factor. The larger the value of the effect intervention factor, the smaller the value of the nursing effect coefficient itself, that is, the worse the nursing effect. The product of the effect intervention factor and the mean difference factor is calculated and normalized as the initial intervention value.

[0054] It should be noted that the overall nursing process also has superposition characteristics. If the circadian rhythm characteristics after a single nursing care are not sufficient to reflect the effect of the nursing plan, the obtained nursing intervention value will be small. However, as the nursing progresses, due to the individual differences of users, the nursing effect of the nursing plan will become worse and worse, which is the superposition of the corresponding effects. It is necessary to analyze the superposition state.

[0055] Furthermore, in some embodiments of the present invention, intervention updates are performed in combination with changes in the initial intervention values ​​of each user in all measurements to obtain a nursing intervention value, including: taking the absolute value of the difference between the initial intervention values ​​of each user in two adjacent measurements as the intervention difference coefficient; calculating the mean of all intervention differences from the initial moment to the current moment, and linearly mapping it to a preset adjustment range as the intervention adjustment coefficient; and taking the product of the intervention adjustment coefficient and the initial intervention value as the nursing intervention value.

[0056] In the embodiment of the present invention, the preset adjustment range represents the predetermined intervention adjustment range indicator, and the value can be set according to individual differences and the sensitivity of individual nursing adjustment. Optionally, the preset adjustment range in the embodiment of the present invention can be specifically [0.5, 1.5]. Of course, when the user needs a more sensitive nursing adjustment, it can also be set to [0.8, 1.8] and adjusted according to individual conditions.

[0057] Among them, the intervention difference coefficient represents both the numerical change of the initial intervention value of two adjacent measurements, that is, the intervention superposition effect produced by two adjacent measurements. Therefore, the mean of all intervention differences from the initial moment to the current moment is calculated and linearly mapped to the preset adjustment range as the intervention adjustment coefficient. The intervention adjustment coefficient represents the intervention superposition from the initial moment to the current moment. It should be noted that the initial moment in the embodiment of the present invention is the moment of the last nursing intervention, that is, after the nursing intervention, the superposition analysis is re-performed to avoid the influence of previous nursing on the current nursing.

[0058] Among them, the nursing intervention value is the indicator data for nursing intervention, the intervention adjustment coefficient is the weight value for intervention adjustment, and the product of the intervention adjustment coefficient and the initial intervention value is used as the nursing intervention value. That is to say, the larger the value of the intervention adjustment coefficient, the larger the corresponding nursing intervention value will be, and the greater the need for nursing intervention.

[0059] Therefore, in the embodiment of the present invention, determining whether to adjust the nursing plan according to the nursing intervention value includes: when the nursing intervention value is greater than a preset intervention threshold, determining to adjust the nursing plan, otherwise, not adjusting the nursing plan.

[0060] Among them, the preset intervention threshold is the threshold value of the nursing intervention value. Since the larger the nursing intervention value, the more nursing intervention is needed, at this time, a universal preset intervention threshold can be set for specific analysis of nursing plan intervention. Optionally, the preset intervention threshold can be specifically 0.75, that is, when the nursing intervention value is greater than 0.75, it is determined to adjust the nursing plan, otherwise, the nursing plan is not adjusted.

[0061] Circadian rhythm is dynamic, and an individual's physiological state may change over time. As the circadian rhythm data changes during the user's care process, the nursing intervention value is dynamically updated. If the current user's circadian rhythm nursing intervention value exceeds the threshold of 0.75, it means that the matching nursing plan at this time is not effective for the user, and the nursing plan needs to be adjusted separately to ensure that the nursing plan can adapt to the changes in the user's physiological state in a timely manner.

[0062] The embodiment of the present invention determines the circadian rhythm characteristic index by the numerical fluctuation of the circadian rhythm data of different users, and then groups them to obtain the initial user group, which represents the user group with similar fluctuations, and realizes the characteristic analysis of multiple types of circadian rhythm data; then, according to the changes in the circadian rhythm characteristic index of different users in the time series, the analysis of the nursing effect is realized, and the nursing effect coefficient is obtained; then, combined with the numerical value of the nursing effect coefficient in the initial user group, the intervention analysis is carried out to obtain the nursing intervention value, and the nursing plan is adjusted according to the nursing intervention value. This application mainly conducts nursing intervention analysis through the numerical feature dimension of a single user, the numerical feature dimension of multiple users, the numerical change dimension of a single user, and the numerical change dimension of multiple users, so that the obtained nursing intervention value can be combined with the state change of the individual and the overall characteristic difference, and dynamically updated as time goes by, so that the individual differences of the users can be effectively analyzed, and intervention adjustments are carried out according to the individual differences and collective changes, and the updating rules of the grouping standards and nursing plans are continuously optimized, so as to realize the refined nursing of users. In summary, this application can effectively improve the reliability of nursing, improve the accuracy and pertinence of the nursing plan update, and enhance the nursing effect.

[0063] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0064] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. An information management system based on circadian rhythm refined care, characterized in that: include: An acquisition module, used to acquire circadian rhythm data measured at different times by different users, wherein the circadian rhythm data at least includes sleep, body temperature, and heart rate; The feature analysis module is used to determine the circadian rhythm feature index of each measurement based on the difference between the value of each circadian rhythm data under the same measurement and the ideal value, and to form a feature sequence according to the time sequence; according to the fluctuation of the feature sequence of different users in the same time period, determine the initial user group with similar rhythm feature fluctuations; The nursing effect analysis module is used to cluster the circadian rhythm characteristic indicators of all users measured at the same time, and determine the target user group with similar circadian rhythm characteristic indicators; determine the rhythm change value of each measurement based on the numerical changes of the circadian rhythm characteristic indicators of the same user in adjacent measurements; in the same target user group, determine the nursing effect coefficient of the user under the corresponding measurement according to the distribution of the rhythm change values ​​of adjacent measurements between users; The adjustment module is used to determine the initial intervention value of each user based on the nursing effect coefficient of the same measurement in the same initial user group, and to update the intervention based on the changes in the initial intervention value of each user in all measurements to obtain the nursing intervention value, and to determine whether to adjust the nursing plan based on the nursing intervention value.

2. An information management system based on circadian rhythm refined care as claimed in claim 1, characterized in that: The ideal value is a known preset value, and the ideal value corresponding to different circadian rhythm data types is different; According to the difference between the value of each circadian rhythm data under the same measurement and the ideal value, the circadian rhythm characteristic index of each measurement is determined, including: The absolute value of the difference between each circadian rhythm data value and the corresponding ideal value was calculated and normalized as the ideal difference coefficient; The mean of the ideal difference coefficients of all circadian rhythm data was used as the circadian rhythm characteristic index for each measurement.

3. An information management system based on circadian rhythm refined care as claimed in claim 1, characterized in that: According to the fluctuation of feature sequences of different users in the same time period, the initial user group with similar rhythm feature fluctuations is determined, including: Based on the dynamic time warping algorithm, the DTW values ​​of the feature sequences of any two users in the same time period are calculated, and the opposite number of the DTW value is normalized as the sequence similarity; Clustering is performed based on sequence similarity to obtain different clusters as initial user groups.

4. An information management system based on circadian rhythm refined care as claimed in claim 1, characterized in that: Cluster the circadian rhythm characteristic indicators of all users measured at the same time to determine the target user group with similar circadian rhythm characteristic indicators, including: The circadian rhythm characteristic indicators of all users measured at the same time are clustered by DBSCAN density to obtain the target user group.

5. An information management system based on circadian rhythm refined care as claimed in claim 1, characterized in that: Based on the numerical changes of the circadian rhythm characteristic indicators of the same user in adjacent measurements, the rhythm change value of each measurement is determined, including: Any measurement of any user is regarded as the target measurement; the other two measurements that are closest to the target measurement in time sequence are regarded as adjacent measurements; Based on the least squares method, a straight line fitting is performed on the values ​​of the circadian rhythm characteristic indexes corresponding to the target measurement and the adjacent measurements, and the slope of the fitting line is used as the fluctuation trend coefficient of the target measurement; The mean of the absolute values ​​of the differences between the target measurement and each adjacent measurement in the circadian rhythm characteristic index was taken as the target characteristic difference coefficient; The product of the fluctuation trend coefficient and the standard feature difference coefficient was calculated and normalized as the rhythm change value of the target measurement.

6. An information management system based on circadian rhythm refined care as claimed in claim 1, characterized in that: According to the distribution of rhythm change values ​​of adjacent measurements between users, the nursing effect coefficient of the user under the corresponding measurements is determined. The corresponding calculation formula is: ； In the formula, Indicates User No. The nursing effect coefficient of the secondary measurement; Indicates User No. The rhythm change value of the measurement; Indicates User No. The average of the rhythm change values ​​of all users in the same target user group is measured; Indicates User No. The mean of the rhythm change values ​​of all users in the same target user group is measured. exp represents an exponential function with a natural constant as the base, and sigmoid represents a sigmoid function.

7. An information management system based on circadian rhythm refined care as claimed in claim 1, characterized in that: According to the nursing effect coefficient of the same measurement in the same initial user group, the initial intervention value of each user is determined, including: The mean of the nursing effect coefficients of the same measurements in the same initial user group is taken as the nursing mean coefficient of the corresponding measurements; The absolute value of the difference between the nursing effect coefficient measured by any user at any time and the corresponding nursing mean coefficient is normalized and used as the mean difference factor; Calculate the normalized value of the inverse of the nursing effect coefficient measured by any user at any time as the effect intervention factor; The product of the effect intervention factor and the mean difference factor was calculated and normalized to serve as the initial intervention value.

8. An information management system based on circadian rhythm refined care as claimed in claim 1, characterized in that: Combined with the changes in the initial intervention values ​​of each user in all measurements, the intervention is updated to obtain the nursing intervention value, including: The absolute value of the difference between the initial intervention values ​​of each user in two consecutive measurements is taken as the intervention difference coefficient; Calculate the mean of all intervention differences from the initial moment to the current moment, and linearly map them to the preset adjustment range as the intervention adjustment coefficient, where the initial moment is the moment of the last nursing intervention; The product of the intervention adjustment coefficient and the initial intervention value is taken as the nursing intervention value.

9. An information management system based on circadian rhythm refined care as claimed in claim 8, characterized in that: The default adjustment range is [0.5,1.5].

10. An information management system based on circadian rhythm refined care as claimed in claim 1, characterized in that: Determine whether to adjust the nursing plan based on the nursing intervention value, including: When the nursing intervention value is greater than the preset intervention threshold, it is determined to adjust the nursing plan, otherwise, the nursing plan is not adjusted.

Images