Online management platform for nursing data of patients with cardiovascular diseases

Abstract

The invention relates to the technical field of medical data processing, in particular to a cardiovascular disease patient nursing data online management platform. The platform comprises a heart rate data preprocessing module used for acquiring normal data and abnormal data in a current sequence; the abnormal time standard acquisition module is used for acquiring an abnormal time standard according to the current sequence and the historical sequence; the numerical credibility calculation module is used for analyzing the abnormal data to obtain the numerical credibility of the abnormal data; the interference coefficient acquisition module is used for dividing the abnormal data into an electromagnetic interference type and a to-be-analyzed type; obtaining an interference coefficient of the abnormal data according to the quartile spacing of the data in the abnormal time period to which one abnormal data in the to-be-analyzed class belongs and the fitting curve; and the abnormal condition score acquisition module is used for acquiring an abnormal condition score of the abnormal data based on the interference coefficient and performing initial judgment on the condition of the patient. The condition that heart rate abnormity is interfered by other factors can be eliminated, and the accuracy of subsequent analysis is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and in particular to an online management platform for nursing data of patients with cardiovascular diseases. Background Art

[0002] With the continuous advancement of medical technology, the treatment and management of cardiovascular diseases have evolved from simple drug treatment to comprehensive and personalized integrated care. The situation of each cardiovascular patient is different, and patients often need continuous health monitoring. Therefore, the online management platform for cardiovascular patient care data came into being. The platform can monitor key indicators such as heart rate and blood pressure in real time, allowing doctors to remotely monitor and promptly detect and deal with potential risks.

[0003] The online nursing data management platform monitors the key indicators of cardiovascular patients, uses AI algorithms to analyze the physical condition of each cardiovascular patient, continuously optimizes nursing recommendations, and improves nursing effects. Therefore, the accuracy of the key indicator data used to analyze the physical condition of cardiovascular patients in the platform is particularly important. The most commonly used key indicator is heart rate data. To facilitate daily monitoring, patients generally wear special smart bracelets for 24-hour heart rate monitoring. When identifying abnormal heart rate data, traditional methods only use the threshold method to distinguish whether the data is abnormal. It does not consider data abnormalities caused by interference from other factors, such as the patient's mood swings and interference caused by exercise, and items such as mobile phones may cause electromagnetic interference, etc., which can easily lead to the analysis of the patient's physical condition not being consistent with the actual situation, increasing the uselessness of patient examinations. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide an online management platform for nursing data of patients with cardiovascular diseases, to identify interference anomalies of the commonly used and important heart rate data in the online management platform for nursing data of patients with cardiovascular diseases, to reduce the inaccuracy of the heart rate data used, and to improve the reliability and accuracy of the platform's analysis results of the patient's physical condition. The technical solutions adopted are as follows: An embodiment of the present invention provides an online management platform for nursing data of patients with cardiovascular diseases, the platform comprising: The heart rate data preprocessing module is used to obtain the patient's heart rate data through the monitoring device and record it as the current sequence; make an initial judgment on the current sequence according to the patient's disease type to obtain normal data and abnormal data; The abnormal time standard acquisition module is used to obtain historical heart rate data to form a historical sequence; record the time period in which normal data appears continuously in the current sequence and the historical sequence as a normal period; and obtain the abnormal time standard according to the normal period in the current sequence and the historical sequence; A numerical credibility calculation module is used to segment the abnormal data in the current sequence to obtain abnormal time periods; and obtain the numerical credibility of the abnormal data based on the time length of the two adjacent data of the abnormal data and the abnormal time period to which the abnormal data belongs; The interference coefficient acquisition module is used to classify abnormal data into electromagnetic interference class and to-be-analyzed class according to the numerical credibility; and to obtain the interference coefficient of an abnormal data in the to-be-analyzed class according to the interquartile range of the data in the abnormal period to which the abnormal data belongs and the fitting curve; The abnormal situation score acquisition module is used to obtain the interference coefficient of abnormal data in the electromagnetic interference class; obtain the abnormal situation score of the abnormal data according to the interference coefficient of the abnormal data; and make an initial judgment on the patient's condition according to the abnormal situation score.

[0005] Preferably, the current sequence is initially judged according to the patient's disease type to obtain normal data and abnormal data, including: The current sequence is initially judged using the normal range of the heart rate data of the disease type of the patient corresponding to the current sequence. The heart rate data within the normal range of the heart rate data is normal data, and the heart rate data outside the normal range of the heart rate data is abnormal data.

[0006] Preferably, the normal period includes: The amount of heart rate data in the normal period is greater than or equal to a preset amount.

[0007] Preferably, obtaining the abnormal time standard according to the normal time periods in the current sequence and the historical sequence includes: The heart rate data of each normal time period in the current sequence are combined into a time period sequence, where an element in the time period sequence is the heart rate data within a normal time period. Similarly, the time period sequence corresponding to each historical sequence is obtained; the intersection of the time points contained in each element in the time period sequence corresponding to the current sequence and the time points contained in each element in the time period sequence corresponding to each historical sequence are obtained respectively to obtain different intersection time periods; the time length of the shortest intersection time period is the abnormal time standard; when obtaining the intersection of the time points contained in each element, the element in the sequence is used as the unit for acquisition.

[0008] Preferably, obtaining the numerical credibility of the abnormal data based on the time length of two data adjacent to the abnormal data and the abnormal period to which the abnormal data belongs includes: Calculate the slopes between an abnormal data and the data adjacent to the left and the right respectively, record them as the first slope and the second slope; obtain the inverse of the absolute value of the difference between the first slope and the second slope, record them as the change characteristic value; obtain the difference between the time length of the abnormal time standard and the abnormal period where the abnormal data is located, record them as the time difference; perform the maximum value operation on the time difference and the first preset value to obtain the maximum value; the inverse of the sum of the maximum value and the preset value is the time duration characteristic value; perform weighted summation on the change characteristic value and the time characteristic value to obtain the numerical credibility of the abnormal data.

[0009] Preferably, the abnormal data is divided into electromagnetic interference category and to-be-analyzed category according to the numerical credibility, including: Abnormal data with numerical credibility less than or equal to the credibility threshold are classified as electromagnetic interference class, and abnormal data with numerical credibility greater than the credibility threshold are classified as to-be-analyzed class.

[0010] Preferably, obtaining the interference coefficient of an abnormal data in the class to be analyzed according to the interquartile range of data in the abnormal period and the fitting curve includes: Arrange all data in an abnormal time period where an abnormal data in the class to be analyzed is located from small to large to obtain a sequential sequence, and the reciprocal of the interquartile range of the sequential sequence is the data change difference characteristic value; use all data in the abnormal time period where the abnormal data is located to perform curve fitting to obtain a fitting curve; obtain the number of abnormal data with derivatives greater than zero and the number of abnormal data with derivatives less than zero on the fitting curve, and record them as the first number and the second number respectively; compare the second number with the sum of the first number and the second number to obtain a ratio, and the difference between the preset value and the ratio is the derivative change characteristic value; calculate the average value of the derivative change characteristic value and the reciprocal of the time length of the abnormal time period where the abnormal data is located, and record it as the data time characteristic value; the mean of the data change difference characteristic value and the data time characteristic value is the interference coefficient of the abnormal data.

[0011] Preferably, obtaining the interference coefficient of abnormal data in the electromagnetic interference category includes: The interference coefficient of abnormal data in the electromagnetic interference category is the difference between the preset value and the numerical credibility.

[0012] Preferably, obtaining an abnormality score of abnormal data according to an interference coefficient of the abnormal data includes: The preset value is subtracted from the interference coefficient of the abnormal data and the initial abnormal value score to obtain the difference result. The product of the difference result and the abnormal score base value is the abnormal situation score of the abnormal data.

[0013] The embodiments of the present invention have at least the following beneficial effects: the present invention performs an initial judgment on the nursing data (heart rate data) of the patient obtained on the online management platform, identifies normal data and abnormal data, preliminarily judges the abnormal situation of the heart rate data, and improves the efficiency of subsequent calculations; further, by combining the time characteristics of normal data in the historical heart rate data of a patient and normal data in the current sequence, an abnormal time standard is obtained, and the numerical credibility of the abnormal data is obtained by combining the abnormal time standard and the change of the abnormal data in the current sequence, and the abnormal data is divided into an electromagnetic interference class and a class to be analyzed, and a preliminarily judgment is made whether the abnormal data is interfered by factors other than pathological factors, so as to facilitate the subsequent analysis of the interference of other factors; then, the interference coefficient of the abnormal data in the class to be analyzed is obtained, and the interference coefficient of the abnormal data in the electromagnetic interference class is obtained, and the abnormal situation score of the abnormal data is calculated based on the interference coefficient, and the interference of factors other than pathological factors on the abnormal heart rate data is excluded, so as to improve the accuracy of the abnormal data, reduce the influence of factors other than pathological factors on the accuracy of the patient's physical condition analysis, analyze the patient's physical condition based on more realistic heart rate data, and obtain more accurate physical condition analysis results of cardiovascular patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] 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.

[0015] Figure 1 A platform block diagram of an online management platform for cardiovascular disease patient nursing data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] 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 online management platform for nursing data of cardiovascular patients proposed by the present invention, its specific implementation, structure, features and effects, 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.

[0017] 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.

[0018] The specific scheme of the online management platform for nursing data of cardiovascular disease patients provided by the present invention is described in detail below with reference to the accompanying drawings.

[0019] Example: The main application scenario of the present invention is: This application is used to analyze the patient's heart rate monitoring data in the online management platform of cardiovascular patient nursing data, and identify data anomalies caused by interference from factors other than pathological factors.

[0020] See also Figure 1 , which shows a platform block diagram of an online management platform for cardiovascular disease patient care data provided by an embodiment of the present invention, the platform includes the following modules: The heart rate data preprocessing module is used to obtain the patient's heart rate data through the monitoring equipment and record it as the current sequence; make an initial judgment on the current sequence according to the patient's disease type to obtain normal data and abnormal data.

[0021] The main purpose of this application is to identify interference anomalies in the more commonly used and important heart rate data in the online management platform for cardiovascular disease patient care data.

[0022] First, it is necessary to use monitoring equipment to collect the patient's heart rate data and obtain the monitoring data of any patient's daily heart rate for analysis in the management platform. The heart rate data is collected by wearing a dedicated heart rate measuring smart bracelet (monitoring equipment). The bracelet has a networking function and can transmit data to the management platform system in real time. The heart rate smart bracelet monitoring principle is the photoelectric projection measurement method, that is, through the LED light source (usually red or green light) inside the bracelet, regularly emit light to the wrist skin. When the light penetrates the skin and encounters blood, the hemoglobin in the blood will absorb part of the light, and the unabsorbed light will be reflected back. According to the conversion of the reflected light into an electrical signal, the strength of the electrical signal is related to the rhythm of the heartbeat. The signal is processed and converted into a heart rate value display, thereby obtaining the patient's heart rate data. It should be noted that the collection frequency of the heart rate data in the embodiment of the present application is once every 1 minute, and the implementer can adjust it according to the actual situation. Each collection cycle is one day. The heart rate data collected in the current collection cycle are formed into a sequence, which is recorded as the current sequence.

[0023] Further, the abnormal data in the current sequence is screened out, and interference analysis is subsequently performed on the abnormal data to exclude interference from other factors on the heart rate data. The heart rate data range of the normal population is generally 60-100 times / minute. Since the management platform is mainly for cardiovascular patients, there are many different types of cardiovascular diseases. The management platform can set different normal ranges of heart rate data according to the type of cardiovascular disease to which the patient user belongs. For example, for users with hypertension, the heart rate data should be controlled at 60-80 times / minute, for users with heart failure, the heart rate data should be controlled at 60-70 times / minute, for users with chronic persistent atrial fibrillation, the heart rate data should be controlled at 80-100 times / minute, and for users with stable angina pectoris, the heart rate data should be controlled at 55-60 times / minute. Therefore, different types of illness have different normal ranges of heart rate data. The normal range of heart rate data of the type of illness corresponding to the patient in the current sequence is used to make an initial judgment on the current sequence. The heart rate data within the normal range of heart rate data is normal data, and the heart rate data not within the normal range of heart rate data is abnormal data.

[0024] For example, taking patients with hypertension as an example, the normal range of their heart rate data is 60-80 beats / minute. For patients with hypertension, heart rate data less than 60 beats / minute and greater than 80 beats / minute are marked as abnormal data, and heart rate data between [60,80] is normal data. Furthermore, in order to facilitate subsequent analysis, the initial abnormal value of abnormal data is recorded as 1, and the initial abnormal value of normal data is recorded as 0, and the initial abnormal value is represented as α.

[0025] The abnormal time standard acquisition module is used to obtain historical heart rate data to form a historical sequence; record the time period in which normal data appears continuously in the current sequence and the historical sequence as a normal period; and obtain the abnormal time standard based on the normal period in the current sequence and the historical sequence.

[0026] In the previous module, a preliminary judgment was made on the heart rate data collected in the current sequence, and the changing time and pattern of the patient's normal heart rate data were further analyzed to analyze whether the abnormal data was interfered with.

[0027] The patient user heart rate monitoring set up in the management platform is 24-hour continuous monitoring. Unless there are special circumstances, the patient user's heart rate data in adjacent days should have similar changes. Therefore, normal data can be analyzed in combination with data from multiple days.

[0028] Since the patient user heart rate monitoring set in the management platform is 24-hour continuous monitoring, if there are no special circumstances, the patient user's heart rate data in adjacent days should have similar changes. Therefore, normal data can be analyzed in combination with historical data of multiple days. First, due to external interference, such as electromagnetic interference, skin sweating and oiling, etc., which affect the sensor reading, this type of interference generally lasts for a short time and can be recovered, so it can be viewed according to the stage of the heart rate data. Since the patient may exercise or be emotionally excited, this situation will cause the heart rate data to rise rapidly and maintain for a period of time before falling according to the length of exercise time or the length of emotional calmness. Therefore, the normal heart rate in the patient user's heart rate data may appear intermittently in the form of time periods, and the length of time periods varies. Therefore, the appropriate time length can be obtained based on multiple days of historical data and the characteristics of data changes around the data can be used to obtain whether the abnormal data is interfered with for analysis.

[0029] First, historical heart rate data of the same period as the current sequence is obtained from the data stored in the management platform to form a historical sequence. It should be noted that since one cycle in this application is one day, the historical sequence obtained is also composed of the patient's historical heart rate data in one day. If in a specific implementation, the collection cycle is not one day, for example, the collection cycle is from 0 o'clock to 20 o'clock, then the data in the historical sequence is also the historical heart rate data from 0 o'clock to 20 o'clock in one day. The number of historical sequences obtained needs to be determined based on the actual computing resources of the implementer.

[0030] Furthermore, the time period in which normal data appears continuously in the current sequence and the historical sequence is recorded as a normal period. It should be noted that the number of heart rate data or historical heart rate data that appears continuously in a normal period needs to be greater than or equal to a preset number. Preferably, the preset number in the present invention is 3.

[0031] For example, the sequence of heart rate data of patients with hypertension is {60, 59, 60, 61, 63, 66, 64, 67, 80, 81, 79, 78, 82}, among which the data in the normal period is {60, 61, 63, 66, 64, 67, 80}.

[0032] Then, the normal time periods in the current sequence and the historical sequence are comprehensively analyzed, and the abnormal time standard is obtained according to the normal time periods in the current sequence and the historical sequences. Specifically, the heart rate data of each normal time period in the current sequence are combined into a time period sequence, and an element in the time period sequence is the heart rate data in a normal time period. Similarly, the time period sequence corresponding to each historical sequence is obtained; the intersection of the time points contained in each element in the time period sequence corresponding to the current sequence and the time points contained in each element in the time period sequence corresponding to each historical sequence are obtained respectively, and different intersection time periods are obtained; the time length of the shortest intersection time period is the abnormal time standard; when obtaining the intersection of the time points contained in each element, it is obtained in units of elements in the sequence.

[0033] For example, the time period sequence corresponding to the current sequence is {5,6,7,8,9; 12,13,14,15,16,17; 20,21,22,23,24,25,26,27} (5,6,7,8,9, 12,13,14,15,16,17 and 20,21,22,23,24,25,26,27 represent time points in three normal time periods respectively), the time period sequence corresponding to the first historical sequence is {6,7,8,9; 12,13,14}, and the time period sequence corresponding to the second historical sequence is {6,7,8,9; 12,13,14}. The time period sequence corresponding to the sequence is {15,16,17; 20,21,22,23,24,25}, then the intersection time periods of the time period sequence corresponding to the current sequence and the time period sequence corresponding to the first historical sequence are 6,7,8,9 and 12,13,14; the intersection time periods of the time period sequence corresponding to the current sequence and the time period sequence corresponding to the second historical sequence are 15,16,17 and 20,21,22,23,24,25. In this example, the abnormal time standard is 3 minutes.

[0034] Due to individual and behavioral differences among each patient, it is impossible to use a unified length of time as the standard length of time for analysis. However, for a single patient, the heart rate data within adjacent time periods has certain historical regularities, that is, the historical data has certain repeatability, so there will be multiple segments of repetitive time data, and these time data have certain behavioral regularities. Therefore, the data that intersects the historical heart rate data and the current heart rate data within the normal time period is representative and represents the time range in which the patient is most likely to maintain a normal heart rate. Therefore, the minimum length of the time period among all the intersection time periods is used as the abnormal time standard for subsequent analysis of abnormal data.

[0035] The numerical credibility calculation module is used to segment the abnormal data in the current sequence to obtain abnormal time periods; and obtain the numerical credibility of the abnormal data based on the time length of two adjacent data of the abnormal data and the abnormal time period to which the abnormal data belongs.

[0036] After obtaining the abnormal time standard, the characteristics of the abnormal data in the current sequence are combined to analyze whether the abnormal data is disturbed. The abnormal heart rate data caused by pathology and the abnormal heart rate data caused by interference are different in duration, so it is necessary to segment the abnormal data in the current sequence to obtain the abnormal period. The time period when the abnormal data appears continuously is the abnormal period, and the time length corresponding to the abnormal data that appears discontinuously is also an abnormal period.

[0037] Furthermore, the numerical credibility of the abnormal data is obtained based on the two data adjacent to the abnormal data and the time length of the abnormal period to which the abnormal data belongs. The slopes between an abnormal data and the data adjacent to the left and the right are calculated respectively, recorded as the first slope and the second slope; the reciprocal of the absolute value of the difference between the first slope and the second slope is obtained, recorded as the change characteristic value; the difference between the abnormal time standard and the time length of the abnormal period to which the abnormal data belongs is obtained, recorded as the time difference; the maximum value operation is performed on the time difference and the first preset value to obtain the maximum value; the reciprocal of the sum of the maximum value and the preset value is the time duration characteristic value; the change characteristic value and the time characteristic value are weighted and summed to obtain the numerical credibility of the abnormal data.

[0038] The specific calculation formula is: , Among them, β represents the numerical credibility of an abnormal data; Indicates the abnormal time standard; t indicates the length of the abnormal period where the abnormal data is located; 1 indicates a preset value, and 0 indicates the first preset value; max indicates the maximum value operation; and Respectively represent the slopes between the abnormal data and the adjacent data on the left and right; and They represent weight coefficients respectively.

[0039] The abnormal period is the period of time formed by the adjacent abnormal data after the normal data segment is divided (the time length corresponding to a single abnormal data without adjacent adjacent data is an abnormal period). If the abnormal period is short, it means that the abnormal data may be caused by external conditions such as electromagnetic interference, resulting in abnormal heart rate data, low numerical credibility, and time duration characteristic value. The larger it is, the more credible the abnormal data is and the less likely it is to be interfered with. Indicates the absolute value of the difference between the first slope and the second slope. Due to external conditions such as electromagnetic interference, abnormal data may have mutation characteristics. The mutation characteristics will cause the two slopes to be in opposite directions. Therefore, the absolute value of the difference between the slopes is large, and the change characteristic value is The larger the value, the more reliable the abnormal data is and the lower the possibility of interference. Since heart rate data is also affected by other factors, heart rate data may not necessarily show mutation characteristics when disturbed by external conditions such as electromagnetic interference. Therefore, , .

[0040] The interference coefficient acquisition module is used to classify abnormal data into electromagnetic interference class and to-be-analyzed class according to numerical credibility; and to obtain the interference coefficient of an abnormal data in the to-be-analyzed class according to the interquartile range of data in the abnormal period to which the abnormal data belongs and the fitting curve.

[0041] When selecting some historical abnormal heart rates for analysis, since external interference has a certain degree of randomness, statistical analysis can be performed by analyzing the numerical credibility results of the abnormal heart rate data. It can be clearly seen from the histogram that there are obvious differences in some frequencies, that is, a small number of numerical credibility is between 0 and 0.4, so the credibility threshold is set to 0.4, and the credibility threshold is used to divide the abnormal data into two categories based on the numerical credibility of the abnormal data. The abnormal data with numerical credibility less than or equal to the credibility threshold is divided into the electromagnetic interference category, and the abnormal data with numerical credibility greater than the credibility threshold is divided into the category to be analyzed.

[0042] The abnormal data in the class to be analyzed needs to be further analyzed to analyze whether it is disturbed by behaviors such as movement. Arrange all the data in the abnormal period where an abnormal data in the class to be analyzed is located from small to large to obtain a sequence, and the reciprocal of the interquartile range of the sequence is the data change difference characteristic value; use all the data in the abnormal period where the abnormal data is located to perform curve fitting to obtain a fitting curve; obtain the number of abnormal data with derivatives greater than zero and the number of abnormal data with derivatives less than zero on the fitting curve, and record them as the first number and the second number respectively; compare the second number with the sum of the first number and the second number to obtain a ratio, and the difference between the preset value and the ratio is the derivative change characteristic value; calculate the average value of the derivative change characteristic value and the reciprocal of the time length of the abnormal period where the abnormal data is located, and record it as the data time characteristic value; the mean of the data change difference characteristic value and the data time characteristic value is the interference coefficient of the abnormal data.

[0043] The specific calculation formula is: , Among them, δ represents the interference coefficient of an abnormal data in the class to be analyzed; Indicates the interquartile range of all data in the abnormal period where the abnormal data is located; All data in the abnormal period where the abnormal data is located are subjected to curve fitting, and the number of abnormal data whose derivatives on the fitting curve are greater than zero is the first number; It indicates that curve fitting is performed on all data in the abnormal time period where the abnormal data is located, and the number of abnormal data whose derivatives on the fitting curve are less than zero is the second number; T indicates the time length of the abnormal time period where the abnormal data is located.

[0044] It represents the characteristic value of data change difference. If the interquartile range is large, it means that there is a large difference in the data change of the abnormal period of the abnormal heart rate data. There may be obvious changes in the heart rate data. However, the obvious changes in the heart rate data may be caused by behaviors such as exercise and emotional changes, or it may be caused by its own pathological reasons.

[0045] Therefore, further analysis is needed, and the derivative of abnormal data after curve fitting is introduced. A derivative greater than 0 indicates that the heart rate data at that location is on an upward trend, and a derivative less than 0 indicates that the heart rate data at that location is on a downward trend. When the heart rate data is abnormal due to exercise, emotional excitement, etc., it tends to recover to normal levels faster, while the heart rate abnormality caused by pathological reasons often takes more time to recover. It indicates the relationship and proportion of the falling heart rate data in the entire abnormal time period. If there are more falling heart rate data, it means that most of the data in the abnormal time period is in a downward trend, and the decline takes a long time. A larger value indicates that the data abnormality is more likely to be caused by pathological reasons. Represents the changing characteristic value of the inverse. The larger the value, the greater the possibility of interference.

[0046] T represents the total duration of the abnormal period in which the selected abnormal data is located. When the value of is large, it means that there are relatively more heart rate data in the falling period, but because the total duration is short, the probability that the abnormal heart rate in the abnormal period is caused by exercise, emotional excitement, etc. is still relatively high.

[0047] Comprehensive analysis shows that if the interquartile range difference is too large, the data in this section may show a process of first rising and then falling. The heart rate data caused by exercise can return to normal in a shorter time, while the heart rate data caused by pathology requires more time to return to normal or is difficult to return to normal. At the same time, the abnormal heart rate data caused by pathology will remain for a longer time.

[0048] In this way, the interference coefficient of each abnormal data in the class to be analyzed is obtained, and the analysis of each data is stored in the data storage module of the management platform. Each time the heart rate data of the patient user is collected, the above process is recalculated according to the new data, and the analysis of the heart rate data in the data storage module is updated. Subsequent scoring is based on the data at the time of analysis.

[0049] The abnormal situation score acquisition module is used to obtain the interference coefficient of abnormal data in the electromagnetic interference class; obtain the abnormal situation score of the abnormal data according to the interference coefficient of the abnormal data; and make an initial judgment on the patient's condition according to the abnormal situation score.

[0050] For abnormal data in the electromagnetic interference category, its interference coefficient is obtained using its numerical credibility, and the interference coefficient of the abnormal data in the electromagnetic interference category is the difference between the preset value and the numerical credibility.

[0051] After obtaining the initial abnormal value of each heart rate data and the interference coefficient of the abnormal data, the abnormal situation score of the heart rate data obtained by the management platform can be calculated. The doctor can view the heart rate data score of each patient user and obtain the physical condition of the patient user through the management platform at any time. The doctor can also understand the effect of the nursing plan based on the changes in the patient's physical data in the management platform, and can adjust the patient's nursing plan in a targeted manner to improve the nursing effect.

[0052] For normal data, that is, heart rate data with an initial abnormal value of 0, no score calculation is required, and its abnormal score is set to 0. Only the abnormal data needs to be scored.

[0053] For abnormal data, an abnormal score base value is set. Preferably, in the embodiment of the present invention, the abnormal score base value is set to 10, and the implementer can set it according to the actual situation. The preset value is subtracted from the interference coefficient of the abnormal data and the initial abnormal value score result to obtain the difference result. The product of the difference result and the abnormal score base value is the abnormal situation score of the abnormal data. The specific calculation formula is: , Among them, ε represents the abnormality score of an abnormal data; 10 represents the basic value of the abnormality score; δ and α represent the interference coefficient and initial abnormality value of the abnormal data respectively. The larger the normality score, the more abnormal the heart rate data of the patient user is. The management platform should focus on the user's physical data and issue heart rate warnings to the patient user and the nursing doctor in time.

[0054] After obtaining the abnormality score of each abnormal data, a detailed physical examination was conducted on several patients with different scores. According to the statistical analysis of the examination results, the distribution of patients' different health status in each score can be obtained, and the score classification intervals are obtained, which are 0, (0,7] and (7,10]. For normal data, that is, when the abnormality score is 0, it means that the current heart rate data is normal and does not need to be paid too much attention; when the abnormality score of the abnormal data is within (0,7], it means that the abnormal heart rate data may be disturbed and requires continuous attention from medical staff. It is decided whether to issue a heart rate warning based on the subsequent physical condition of the patient. If the subsequent heart rate data continues to be abnormal, a heart rate warning will be issued to remind the patient to conduct further examinations; when the abnormality score of the abnormal data is within (7,10], it means that the heart rate data is abnormal and needs to be paid special attention to, and a heart rate warning will be issued to remind the patient to conduct further examinations.

[0055] In addition, when using the built-in AI algorithm in the platform to analyze the patient's physical condition based on heart rate data, if the abnormality score of the abnormal data is within (0,7], and the patient is subsequently observed and no abnormal physical condition is found, then the abnormal data with an abnormality score within (0,7] needs to be processed. The mean replacement method can be used, such as selecting the heart rate data with adjacent scores on the left and right that are not within (0,7] to calculate the mean for replacement, so as to improve the accuracy of the data and thus improve the accuracy of subsequent analysis.

[0056] Patients can seek medical treatment on time and adjust their daily living habits in a timely manner based on information such as heart rate warnings on the platform. Doctors and other caregivers can modify care plans based on the physical data of each patient on the platform, improve treatment and care measures in a timely manner, and enhance care outcomes.

[0057] In summary, the technical solution provided by the present invention takes into account the situation where the heart rate data is interfered by factors other than pathology during the heart rate monitoring process, analyzes the data during the monitoring process, eliminates these interferences, and further improves the accuracy of monitoring.

[0058] 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 above is a description of a specific embodiment of this specification. In addition, 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.

[0059] 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.

[0060] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An online management platform for nursing data of patients with cardiovascular diseases, characterized in that: The platform includes: The heart rate data preprocessing module is used to obtain the patient's heart rate data through the monitoring device and record it as the current sequence; make an initial judgment on the current sequence according to the patient's disease type to obtain normal data and abnormal data; The abnormal time standard acquisition module is used to obtain historical heart rate data to form a historical sequence; record the time period in which normal data appears continuously in the current sequence and the historical sequence as a normal period; and obtain the abnormal time standard according to the normal period in the current sequence and the historical sequence; The numerical credibility calculation module is used to segment the abnormal data in the current sequence to obtain abnormal time periods; obtain the numerical credibility of the abnormal data based on the time length of the two adjacent data of the abnormal data and the abnormal time period to which the abnormal data belongs; The interference coefficient acquisition module is used to classify abnormal data into electromagnetic interference class and to-be-analyzed class according to the numerical credibility; and to obtain the interference coefficient of abnormal data according to the interquartile range of data in the abnormal period to which an abnormal data in the to-be-analyzed class belongs and the fitting curve; The abnormal situation score acquisition module is used to obtain the interference coefficient of abnormal data in the electromagnetic interference class; obtain the abnormal situation score of the abnormal data according to the interference coefficient of the abnormal data; and make an initial judgment on the patient's condition according to the abnormal situation score.

2. According to claim 1, an online management platform for nursing data of cardiovascular disease patients is characterized in that: The initial judgment of the current sequence according to the patient's disease type to obtain normal data and abnormal data includes: The current sequence is initially judged using the normal range of the heart rate data of the disease type of the patient corresponding to the current sequence. The heart rate data within the normal range of the heart rate data is normal data, and the heart rate data outside the normal range of the heart rate data is abnormal data.

3. The online management platform for nursing data of cardiovascular disease patients according to claim 1, characterized in that: The normal period includes: The amount of heart rate data in the normal period is greater than or equal to a preset amount.

4. The online management platform for nursing data of cardiovascular disease patients according to claim 1, characterized in that: The step of obtaining the abnormal time standard according to the normal time periods in the current sequence and the historical sequence includes: The heart rate data of each normal time period in the current sequence are combined into a time period sequence, where an element in the time period sequence is the heart rate data within a normal time period. Similarly, the time period sequence corresponding to each historical sequence is obtained; the intersection of the time points contained in each element in the time period sequence corresponding to the current sequence and the time points contained in each element in the time period sequence corresponding to each historical sequence are obtained respectively to obtain different intersection time periods; the time length of the shortest intersection time period is the abnormal time standard; when obtaining the intersection of the time points contained in each element, the element in the sequence is used as the unit for acquisition.

5. The online management platform for nursing data of cardiovascular disease patients according to claim 1, characterized in that: The obtaining of the numerical credibility of the abnormal data based on the time length of the abnormal period to which the abnormal data belongs and the two adjacent data of the abnormal data comprises: Calculate the slopes between an abnormal data and the data adjacent to the left and the right respectively, record them as the first slope and the second slope; obtain the inverse of the absolute value of the difference between the first slope and the second slope, record them as the change characteristic value; obtain the difference between the time length of the abnormal time standard and the abnormal period where the abnormal data is located, record them as the time difference; perform the maximum value operation on the time difference and the first preset value to obtain the maximum value; the inverse of the sum of the maximum value and the preset value is the time duration characteristic value; perform weighted summation on the change characteristic value and the time characteristic value to obtain the numerical credibility of the abnormal data.

6. The online management platform for nursing data of cardiovascular disease patients according to claim 1, characterized in that: The abnormal data is divided into electromagnetic interference type and to-be-analyzed type according to the numerical credibility, including: Abnormal data with numerical credibility less than or equal to the credibility threshold are classified as electromagnetic interference class, and abnormal data with numerical credibility greater than the credibility threshold are classified as to-be-analyzed class.

7. The online management platform for nursing data of cardiovascular disease patients according to claim 1, characterized in that: The step of obtaining the interference coefficient of an abnormal data according to the interquartile range of data in an abnormal period to which an abnormal data in the class to be analyzed belongs and the fitting curve comprises: Arrange all data in an abnormal time period where an abnormal data in the class to be analyzed is located from small to large to obtain a sequential sequence, and the reciprocal of the interquartile range of the sequential sequence is the data change difference characteristic value; use all data in the abnormal time period where the abnormal data is located to perform curve fitting to obtain a fitting curve; obtain the number of abnormal data with derivatives greater than zero and the number of abnormal data with derivatives less than zero on the fitting curve, and record them as the first number and the second number respectively; compare the second number with the sum of the first number and the second number to obtain a ratio, and the difference between the preset value and the ratio is the derivative change characteristic value; calculate the average value of the derivative change characteristic value and the reciprocal of the time length of the abnormal time period where the abnormal data is located, and record it as the data time characteristic value; the mean of the data change difference characteristic value and the data time characteristic value is the interference coefficient of the abnormal data.

8. The online management platform for nursing data of cardiovascular disease patients according to claim 1, characterized in that: The obtaining of the interference coefficient of abnormal data in the electromagnetic interference category includes: The interference coefficient of abnormal data in the electromagnetic interference category is the difference between the preset value and the numerical credibility.

9. The online management platform for nursing data of cardiovascular disease patients according to claim 1, characterized in that: The step of obtaining an abnormality score of the abnormal data according to an interference coefficient of the abnormal data includes: The preset value is subtracted from the interference coefficient of the abnormal data and the initial abnormal value score to obtain the difference result. The product of the difference result and the abnormal score base value is the abnormal situation score of the abnormal data.

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