A peritoneal dialysis patient physiological parameter analysis and complication early warning system

Abstract

The present application relates to the technical field of peritoneal dialysis patient physiological data analysis, and particularly relates to a peritoneal dialysis patient physiological parameter analysis and complication early warning system. Multi-time and multi-dimensional physiological data of peritoneal dialysis patients are collected, reference dimensions are selected, and a stability coefficient is calculated based on the difference between the data extreme value and the standard range and the data fluctuation of adjacent time. By comparing the abnormal dimensions of other patients, the number of patients with the same abnormal dimensions and having complications is counted. Combined with the difference in the stability coefficient of each abnormal dimension of the target patient and the number of related abnormal dimensions, a complication risk index is calculated to realize accurate early warning. The present application can correlate multi-dimensional patient physiological data, thereby improving the identification accuracy of complications, and can early identify and intervene the patient.

Description

Technical Field

[0001] This invention relates to the technical field of physiological data analysis for peritoneal dialysis patients, specifically to a system for analyzing physiological parameters and providing early warning of complications in peritoneal dialysis patients. Background Technology

[0002] Peritoneal dialysis is one of the main treatment methods for patients with end-stage renal disease. Because this treatment usually needs to be performed by patients at home, the process lacks real-time supervision by professional medical staff, and patients generate a large amount of physiological parameters and dialysis operation data every day. These data are closely related to the occurrence of serious complications such as peritonitis, volume overload, and heart failure.

[0003] Currently, these data records are fragmented and lack systematic analysis. Relevant personnel can only discover problems when patients have regular check-ups or develop severe symptoms, often missing the best opportunity for early intervention and failing to achieve early identification and intervention of complications. Furthermore, existing technologies cannot analyze the correlation between physiological data from multiple dimensions, thus reducing the accuracy of complication identification. Summary of the Invention

[0004] To address the issues of fragmented and unsystematic recording of physiological data, which often leads to problems being discovered only during regular patient checkups or when severe symptoms appear, thus missing the optimal window for early intervention and hindering the early identification and intervention of complications, and because existing technologies cannot analyze the correlation between multiple dimensions of physiological data, thereby reducing the accuracy of complication identification, this invention aims to provide a physiological parameter analysis and complication early warning system for peritoneal dialysis patients. The specific technical solution adopted is as follows:

[0005] A system for analyzing physiological parameters and predicting complications in peritoneal dialysis patients, the system comprising:

[0006] The data acquisition module is used to collect physiological data of the target peritoneal dialysis patient in all preset dimensions at different preset times;

[0007] The data analysis module is used to select any preset dimension of physiological data as the reference dimension; based on the differences between the maximum data range and the standard healthy data range of the physiological data in the reference dimension across all preset time points, and the changes in the reference dimension data between each two adjacent preset time points, the stability coefficient of the target peritoneal dialysis patient in the reference dimension is obtained; based on the differences in the stability coefficients of the target peritoneal dialysis patient and all other patients in each preset dimension, abnormal preset dimensions of the peritoneal dialysis patient are screened out; based on the number of patients with the same abnormal preset dimension and concurrent complications among all peritoneal dialysis patients, the number of correlated abnormal preset dimensions is obtained; based on the differences in the stability coefficients of each abnormal preset dimension of the target peritoneal dialysis patient and the number of correlated abnormal preset dimensions, the risk index of complications in the target peritoneal dialysis patient is obtained;

[0008] The complication warning module is used to provide early warnings of complications for target peritoneal dialysis patients based on the risk index.

[0009] Furthermore, the method for obtaining the stability coefficient includes:

[0010] Choose any preset time as a reference time. Calculate the difference between the physiological data of the reference dimension at the reference time and the physiological data of the reference dimension at the previous preset time as the first difference. Calculate the difference between the physiological data of the reference dimension at the next preset time and the physiological data of the reference dimension at the previous preset time as the second difference. Calculate the ratio between the first difference and the second difference as the degree of physiological data change of the physiological data of the reference dimension at the reference time. Iterate through the degree of physiological data change of the physiological data of the reference dimension at all preset times, and count the number of preset times with negative degree of physiological data change as the number of physiological data changes of the reference dimension at all preset times.

[0011] The stability coefficient is obtained according to the stability coefficient calculation formula, which is shown below:

[0012]

[0013] In the formula, Indicates the index of the reference dimension; This represents the stability coefficient of the target peritoneal dialysis patient in the reference dimension; This indicates the number of times the physiological data of the reference dimension changed across all preset time points; Indicates the number of preset times; Indicates the patient was in the first Physiological data for reference dimensions at a preset time point; Indicates the patient was in the first Physiological data for reference dimensions at a preset time point; This represents the maximum value among the physiological data of the reference dimension at all preset time points; This represents the minimum value among the physiological data of the reference dimension at all preset time points; The maximum value of the standard healthy range for physiological data representing the reference dimension for peritoneal dialysis patients; The minimum standard healthy range of physiological data representing the reference dimension for peritoneal dialysis patients; Represents the hyperbolic tangent function; Represents an exponential function with the natural constant as the base; Represents the absolute value function; This represents the logarithmic function with base 2.

[0014] Furthermore, the method for obtaining the preset dimension of the anomaly includes:

[0015] Calculate the mean stability coefficient of all other patients in the reference dimension as the first mean; if the stability coefficient of the target peritoneal dialysis patient in the reference dimension is less than the first mean, then the reference dimension is considered to be an abnormal preset dimension of the target peritoneal dialysis patient; traverse all preset dimensions to obtain all abnormal preset dimensions of the target peritoneal dialysis patient.

[0016] Furthermore, the method for obtaining the number of pre-defined dimensions of correlated anomalies includes:

[0017] Among all peritoneal dialysis patients, those with complications and the most abnormal preset dimensions were selected as reference patients; the number of abnormal preset dimensions possessed by the reference patients was used as the number of relevant abnormal preset dimensions.

[0018] Furthermore, the method for obtaining the risk index for having complications includes:

[0019] The ratio of the number of abnormal preset dimensions in the target peritoneal dialysis patient to the total number of preset dimensions is calculated as the degree of physiological abnormality in the target peritoneal dialysis patient.

[0020] The risk index is obtained according to the risk index calculation formula, which is shown below:

[0021]

[0022] In the formula, This indicates the risk index of complications in the target peritoneal dialysis patient; This indicates the number of relevant predefined abnormal dimensions in all peritoneal dialysis patients; This indicates the number of predefined abnormal dimensions in the target peritoneal dialysis patient; Indicates the degree of physiological abnormalities in the target peritoneal dialysis patient; This represents the mean stability coefficient of physiological data for all peritoneal dialysis patients in the reference dimension. This represents the stability coefficient of the physiological data of the target peritoneal dialysis patient in the reference dimension.

[0023] Furthermore, the risk index is used to provide early warning of complications in target peritoneal dialysis patients, including:

[0024] When the risk index exceeds the preset first threshold, the target peritoneal dialysis patient is considered to have complications, and an early warning is issued.

[0025] A method for analyzing physiological parameters and predicting complications in peritoneal dialysis patients, the method comprising:

[0026] Collect physiological data of the target peritoneal dialysis patient across all preset dimensions at different preset times;

[0027] Select any preset dimension of patient physiological data as the physiological data under the reference dimension; based on the difference between the maximum data range of physiological data under the reference dimension and the standard healthy data range of physiological data under the reference dimension at all preset time points, and the change characteristics of the reference dimension data at each two adjacent preset time points, obtain the stability coefficient of the target peritoneal dialysis patient in the reference dimension; based on the difference in stability coefficients between the target peritoneal dialysis patient and all other patients in each preset dimension, screen out the abnormal preset dimensions of the target peritoneal dialysis patient; based on the number of all other patients who have the same abnormal preset dimension and also have complications, obtain the number of correlated abnormal preset dimensions; based on the difference in stability coefficients of each abnormal preset dimension of the target peritoneal dialysis patient and the number of correlated abnormal preset dimensions, obtain the risk index of complications for the target peritoneal dialysis patient;

[0028] The risk index is used to provide early warning of complications in target peritoneal dialysis patients.

[0029] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the peritoneal dialysis patient physiological parameter analysis and complication early warning system described above.

[0030] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the peritoneal dialysis patient physiological parameter analysis and complication early warning system described above.

[0031] The present invention has the following beneficial effects:

[0032] This invention addresses the issue that during peritoneal dialysis, a patient's physiological data across multiple dimensions can change, potentially accompanied by complications. Therefore, it collects physiological data from a target peritoneal dialysis patient across all preset dimensions at different preset time points. For peritoneal dialysis patients, under normal conditions, these physiological data are in a dynamic equilibrium. However, when complications occur, these physiological data can fluctuate drastically. Therefore, based on the difference between the maximum range of physiological data in a reference dimension and the standard healthy range of physiological data in that reference dimension across all preset time points, as well as the change characteristics of the reference dimension data between each two adjacent preset time points, a stability coefficient is obtained for the target peritoneal dialysis patient in the reference dimension. This stability coefficient is used to determine whether any abnormalities occur in the physiological data of each dimension of the target peritoneal dialysis patient. Furthermore, physiological data from other peritoneal dialysis patients within different preset dimensions are also analyzed. This invention compares the changing trends of physiological data of target peritoneal dialysis patients across different preset dimensions to identify abnormal dimensions and reduce the number of dimensions required for subsequent calculations of correlations between different preset dimensions. By analyzing cases where all peritoneal dialysis patients share the same abnormal preset dimension and also suffer from complications, the number of correlated abnormal preset dimensions is estimated. Furthermore, based on the number of peritoneal dialysis patients with the same abnormal preset dimension and concurrent complications, the number of correlated abnormal preset dimensions is obtained. Based on the stability coefficient differences of each abnormal preset dimension in the target peritoneal dialysis patient and the number of correlated abnormal preset dimensions, a risk index for complications in the target peritoneal dialysis patient is obtained. The risk index is used to provide early warning of complications in the target peritoneal dialysis patient. This invention can correlate multi-dimensional patient physiological data, thereby improving the accuracy of complication identification and enabling early identification and intervention of patients. Attached Figure Description

[0033] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a block diagram of a peritoneal dialysis patient physiological parameter analysis and complication early warning system provided in one embodiment of the present invention. Detailed Implementation

[0035] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a peritoneal dialysis patient physiological parameter analysis and complication early warning system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0037] The following description, in conjunction with the accompanying drawings, details the specific scheme of the peritoneal dialysis patient physiological parameter analysis and complication early warning system provided by the present invention.

[0038] Please see Figure 1 This invention illustrates a system for analyzing physiological parameters and predicting complications in peritoneal dialysis patients, provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a data analysis module 102, and a complication prediction module 103. Specific steps include:

[0039] Data acquisition module 101: Collects physiological data of the target peritoneal dialysis patient in all preset dimensions at different preset times.

[0040] This invention is primarily applied to the early identification and intervention of complications in peritoneal dialysis patients. Since peritoneal dialysis uses the body's own peritoneum as a semipermeable membrane to remove metabolic waste and excess water, various physiological data of the patient will change during this process, potentially leading to complications. For example, peritoneal dialysis removes excess water through ultrafiltration based on the osmotic pressure of the dialysate. However, if peritoneal ultrafiltration function declines (ultrafiltration failure), water cannot be effectively removed, potentially leading to edema, hypertension, and excessive heart failure.

[0041] In order to utilize the correlation of physiological data of previous peritoneal dialysis patients in various dimensions to analyze the changes in physiological data of the target peritoneal dialysis patient, in one embodiment of the present invention, physiological data of previous peritoneal dialysis patients and the target peritoneal dialysis patient in all preset dimensions at different preset times are collected from the hospital information system (HIS), laboratory information system (LIS) and peritoneal dialysis system.

[0042] In one embodiment of the present invention, the preset time period is set to 1 day, and the preset dimensions are set to blood pressure, weight, ultrafiltration rate, serum creatinine, blood urea nitrogen, serum potassium, serum sodium, serum phosphorus, serum calcium, serum albumin, hemoglobin, and PTH. It should be noted that the preset time period and preset dimensions can be set independently and are not limited here.

[0043] Data Analysis Module 102: Select any preset dimension of physiological data as the physiological data under the reference dimension; based on the difference characteristics between the maximum data range of physiological data under the reference dimension and the standard healthy data range of physiological data under the reference dimension in all preset time periods, and the change characteristics of the reference dimension data in each two adjacent preset time periods, obtain the stability coefficient of the target peritoneal dialysis patient in the reference dimension; based on the difference in stability coefficients between the target peritoneal dialysis patient and all other patients in each preset dimension, screen out the abnormal preset dimensions of the peritoneal dialysis patient; based on the number of all other peritoneal dialysis patients who have the same abnormal preset dimension and also have complications, obtain the number of correlated abnormal preset dimensions; based on the difference in stability coefficients of each abnormal preset dimension of the target peritoneal dialysis patient and the number of correlated abnormal preset dimensions, obtain the risk index of complications for the target peritoneal dialysis patient.

[0044] For peritoneal dialysis patients, under normal conditions, multi-dimensional physiological data are in a state of dynamic equilibrium, meaning that the trend of physiological data changes is constantly rising and falling. However, when complications occur, the physiological data in multiple dimensions can change drastically. Therefore, in this embodiment of the invention, a preset dimension of physiological data is first selected as the physiological data under the reference dimension. Based on the difference between the maximum data range of the physiological data under the reference dimension and the standard healthy data range of the physiological data under the reference dimension in all preset times, as well as the change characteristics of the reference dimension data in every two adjacent preset times, the stability coefficient of the target peritoneal dialysis patient in the reference dimension is obtained. The stability coefficient is used to determine whether there are abnormalities in the physiological data of each dimension of the target peritoneal dialysis patient.

[0045] Preferably, in one embodiment of the present invention, the method for obtaining the stability coefficient includes:

[0046] Choose any preset time as the reference time. Calculate the difference between the physiological data of the reference dimension at the reference time and the physiological data of the reference dimension at the previous preset time as the first difference. Calculate the difference between the physiological data of the reference dimension at the next preset time and the physiological data of the reference dimension at the previous preset time as the second difference. Calculate the ratio between the first difference and the second difference as the degree of physiological data change in the reference dimension at the reference time. Iterate through the degree of physiological data change in the reference dimension at all preset times. If there are more positive values ​​among all the degree of physiological data change, it indicates that the trend of physiological data change in the reference dimension has changed more. If there are more negative values ​​among all the degree of physiological data change, it indicates that the physiological data of the reference dimension is always changing, and the number of changes is more frequent. Count the number of preset times corresponding to negative degree of physiological data change as the number of physiological data changes in the reference dimension at all preset times.

[0047] The stability coefficient is obtained using the formula shown below:

[0048]

[0049] In the formula, Indicates the index of the reference dimension; This represents the stability coefficient of the target peritoneal dialysis patient in the reference dimension; This indicates the number of times the physiological data of the reference dimension changed across all preset time points; Indicates the number of preset times; Indicates the patient was in the first Physiological data for reference dimensions at a preset time point; Indicates the patient was in the first Physiological data for reference dimensions at a preset time point; This represents the maximum value among the physiological data of the reference dimension at all preset time points; This represents the minimum value among the physiological data of the reference dimension at all preset time points; The maximum value of the standard healthy range for physiological data representing the reference dimension for peritoneal dialysis patients; The minimum standard healthy range of physiological data representing the reference dimension for peritoneal dialysis patients; Represents the hyperbolic tangent function; Represents an exponential function with the natural constant as the base; Represents the absolute value function; This represents the logarithmic function with base 2.

[0050] In the stability coefficient calculation formula, the more times the physiological data of the reference dimension changes across all preset time points, the more likely the physiological data of the reference dimension is to be in a dynamic equilibrium state. In this case, the stability coefficient of the target peritoneal dialysis patient in the reference dimension is greater. Since physiological data from very early time series have little reference value, greater weight needs to be given to physiological data from later time series. In this embodiment of the invention, the time range for the weight reduction is set to one week. As an index, the weight of physiological data decreases every 7 preset time points, or 7 days. The differences between each pair of adjacent physiological data in the reference dimension are weighted and averaged to obtain... Among these, the smaller the weighted mean, the smaller the trend of physiological data change, and the greater the stability coefficient of the target peritoneal dialysis patient in the reference dimension. If the maximum variation of the target peritoneal dialysis patient's physiological data in the reference dimension is less than the standard healthy range of the peritoneal dialysis patient's physiological data in the reference dimension, that is... The smaller the value, the more stable the target peritoneal dialysis patient's physiological data in the reference dimension. In this case, the stability coefficient of the target peritoneal dialysis patient in the reference dimension is larger. Furthermore, when the maximum variation in the target peritoneal dialysis patient's physiological data in the reference dimension exceeds the standard healthy range of the peritoneal dialysis patient's physiological data in the reference dimension, then... The cube of the value in parentheses is amplified, which increases the magnitude of the reduction in the stability coefficient and indicates that the physiological data of the target peritoneal dialysis patient are very unstable.

[0051] By comparing the physiological data trends of other peritoneal dialysis patients across different preset dimensions, the physiological data trends of the target peritoneal dialysis patient across different preset dimensions are compared to identify the abnormal dimensions in the target peritoneal dialysis patient. This reduces the number of dimensions required to calculate the correlation between different preset dimensions. Therefore, based on the difference in stability coefficients between the target peritoneal dialysis patient and all other patients in each preset dimension, the abnormal preset dimensions of the target peritoneal dialysis patient are screened out.

[0052] Preferably, in one embodiment of the present invention, the method for obtaining the abnormal preset dimension includes:

[0053] Calculate the mean stability coefficient of all other patients in the reference dimension as the first mean; if the stability coefficient of the target peritoneal dialysis patient in the reference dimension is less than the first mean, the reference dimension is considered an abnormal preset dimension of the target peritoneal dialysis patient; iterate through all preset dimensions to obtain all initial abnormal preset dimensions of the target peritoneal dialysis patient. Iterate through all peritoneal dialysis patients to obtain all initial abnormal preset dimensions of each peritoneal dialysis patient; count the types of initial abnormal preset dimensions of all peritoneal dialysis patients to obtain the abnormal preset dimensions of the peritoneal dialysis patient.

[0054] By analyzing cases where all peritoneal dialysis patients share the same abnormal pre-defined dimension and also suffer from complications, the number of correlated abnormal pre-defined dimensions is estimated.

[0055] Preferably, in one embodiment of the present invention, the method for obtaining the number of pre-defined dimensions of relevance anomalies includes:

[0056] Among all peritoneal dialysis patients, those with complications and the most abnormal preset dimensions were selected as reference patients; the number of abnormal preset dimensions possessed by the reference patients was used as the number of relevant abnormal preset dimensions.

[0057] Furthermore, based on the number of peritoneal dialysis patients who share the same abnormal preset dimension and also suffer from complications, the number of relevant abnormal preset dimensions is obtained; based on the stability coefficient difference of each abnormal preset dimension of the target peritoneal dialysis patient and the number of relevant abnormal preset dimensions, the risk index of complications in the target peritoneal dialysis patient is obtained.

[0058] Preferably, in one embodiment of the present invention, the method for obtaining the risk index of having complications includes:

[0059] The ratio of the number of abnormal preset dimensions in the target peritoneal dialysis patient to the total number of preset dimensions is calculated as the degree of physiological abnormality in the target peritoneal dialysis patient. The higher the proportion of abnormal preset dimensions, the more abnormal the physiological data of the target peritoneal dialysis patient is at this time, and the higher the risk of complications for the target peritoneal dialysis patient.

[0060] The risk index is obtained according to the risk index calculation formula, which is shown below:

[0061]

[0062] In the formula, This indicates the risk index of complications in the target peritoneal dialysis patient; This indicates the number of relevant predefined abnormal dimensions in all peritoneal dialysis patients; This indicates the number of predefined abnormal dimensions in the target peritoneal dialysis patient; Indicates the degree of physiological abnormalities in the target peritoneal dialysis patient; This represents the mean stability coefficient of physiological data for all peritoneal dialysis patients in the reference dimension. This represents the stability coefficient of the physiological data of the target peritoneal dialysis patient in the reference dimension.

[0063] In the risk index calculation formula, since the larger the base of the logarithmic function, the slower the function grows, the more likely the number of abnormal pre-defined dimensions will be in the target peritoneal dialysis patient. The smaller the number of predefined abnormal dimensions that are relevant across all peritoneal dialysis patients, the better. For a fixed value, at this time The larger the value, the lower the risk index for complications in the target peritoneal dialysis patient. It should be noted that the molecular... This is because logarithmic functions cannot use 1 as the base; the larger the proportion of abnormal preset dimensions, the more abnormal the physiological data of the target peritoneal dialysis patient at this time, i.e. The larger the value, the higher the risk of complications for the target peritoneal dialysis patient; the stability coefficient of the target peritoneal dialysis patient's physiological data in the reference dimension. Mean stability coefficient of physiological data in the reference dimension compared with all peritoneal dialysis patients The greater the difference between the two values, the more likely the target peritoneal dialysis patient is to develop complications, and the higher the risk index of complications for the target peritoneal dialysis patient.

[0064] Complication warning module 103: Provides early warning of complications for target peritoneal dialysis patients based on risk index.

[0065] Preferably, in one embodiment of the present invention, the method of providing early warning of complications for target peritoneal dialysis patients based on a risk index includes:

[0066] When the risk index exceeds a preset first threshold, the target peritoneal dialysis patient is considered to have complications, and an early warning is issued. In one embodiment of the present invention, the preset first threshold is set to 0.6. It should be noted that the preset first threshold can be set arbitrarily and is not limited here.

[0067] In summary, physiological data of the target peritoneal dialysis patient across all preset dimensions were collected at different preset time points. Physiological data from one preset dimension were randomly selected as the reference dimension. Based on the differences between the maximum range of physiological data in the reference dimension and the standard healthy range of physiological data in the reference dimension across all preset time points, as well as the changes in reference dimension data between adjacent preset time points, the stability coefficient of the target peritoneal dialysis patient in the reference dimension was obtained. Abnormal preset dimensions of the target peritoneal dialysis patient were identified based on the differences in stability coefficients between the target peritoneal dialysis patient and all other patients in each preset dimension. The number of correlated abnormal preset dimensions was obtained based on the number of all other patients who shared the same abnormal preset dimension and also had complications. A risk index for complications in the target peritoneal dialysis patient was obtained based on the differences in stability coefficients of each abnormal preset dimension and the number of correlated abnormal preset dimensions. The risk index was used to provide early warnings for complications in the target peritoneal dialysis patient.

[0068] A second objective of one embodiment of the present invention is to provide a method for analyzing physiological parameters and predicting complications in peritoneal dialysis patients, the method comprising:

[0069] Collect physiological data of the target peritoneal dialysis patient across all preset dimensions at different preset times;

[0070] Select any preset dimension of patient physiological data as the physiological data under the reference dimension; based on the difference between the maximum data range of physiological data under the reference dimension and the standard healthy data range of physiological data under the reference dimension at all preset time points, and the change characteristics of the reference dimension data at each two adjacent preset time points, obtain the stability coefficient of the target peritoneal dialysis patient in the reference dimension; based on the difference in stability coefficients between the target peritoneal dialysis patient and all other patients in each preset dimension, screen out the abnormal preset dimensions of the target peritoneal dialysis patient; based on the number of all other patients who have the same abnormal preset dimension and also have complications, obtain the number of correlated abnormal preset dimensions; based on the difference in stability coefficients of each abnormal preset dimension of the target peritoneal dialysis patient and the number of correlated abnormal preset dimensions, obtain the risk index of complications for the target peritoneal dialysis patient;

[0071] Early warning of complications for target peritoneal dialysis patients is provided based on risk indices.

[0072] A third objective of this invention is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the system described in modules 101-103.

[0073] The fourth objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the system described in modules 101-103.

[0074] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A system for analyzing physiological parameters and predicting complications in peritoneal dialysis patients, characterized in that, The system includes: The data acquisition module is used to collect physiological data of the target peritoneal dialysis patient in all preset dimensions at different preset times; The data analysis module is used to select any preset dimension of physiological data as the reference dimension; based on the differences between the maximum data range and the standard healthy data range of the physiological data in the reference dimension across all preset time points, and the changes in the reference dimension data between each two adjacent preset time points, the stability coefficient of the target peritoneal dialysis patient in the reference dimension is obtained; based on the differences in the stability coefficients of the target peritoneal dialysis patient and all other patients in each preset dimension, abnormal preset dimensions of the peritoneal dialysis patient are screened out; based on the number of patients with the same abnormal preset dimension and concurrent complications among all peritoneal dialysis patients, the number of correlated abnormal preset dimensions is obtained; based on the differences in the stability coefficients of each abnormal preset dimension of the target peritoneal dialysis patient and the number of correlated abnormal preset dimensions, the risk index of complications in the target peritoneal dialysis patient is obtained; The complication warning module is used to provide early warnings of complications for target peritoneal dialysis patients based on the risk index.

2. The peritoneal dialysis patient physiological parameter analysis and complication early warning system according to claim 1, characterized in that, The method for obtaining the stability coefficient includes: Choose any preset time as a reference time. Calculate the difference between the physiological data of the reference dimension at the reference time and the physiological data of the reference dimension at the previous preset time as the first difference. Calculate the difference between the physiological data of the reference dimension at the next preset time and the physiological data of the reference dimension at the previous preset time as the second difference. Calculate the ratio between the first difference and the second difference as the degree of physiological data change of the physiological data of the reference dimension at the reference time. Iterate through the degree of physiological data change of the physiological data of the reference dimension at all preset times, and count the number of preset times with negative degree of physiological data change as the number of physiological data changes of the reference dimension at all preset times. The stability coefficient is obtained according to the stability coefficient calculation formula, which is shown below: In the formula, Indicates the index of the reference dimension; This represents the stability coefficient of the target peritoneal dialysis patient in the reference dimension; This indicates the number of times the physiological data of the reference dimension changed across all preset time points; Indicates the number of preset times; Indicates the patient was in the first Physiological data for reference dimensions at a preset time point; Indicates the patient was in the first Physiological data for reference dimensions at a preset time point; This represents the maximum value among the physiological data of the reference dimension at all preset time points; This represents the minimum value among the physiological data of the reference dimension at all preset time points; The maximum value of the standard healthy range for physiological data representing the reference dimension for peritoneal dialysis patients; The minimum standard healthy range of physiological data representing the reference dimension for peritoneal dialysis patients; Represents the hyperbolic tangent function; Represents an exponential function with the natural constant as the base; Represents the absolute value function; This represents the logarithmic function with base 2.

3. The peritoneal dialysis patient physiological parameter analysis and complication early warning system according to claim 1, characterized in that, The method for obtaining the preset dimension of the anomaly includes: Calculate the mean stability coefficient of all other patients in the reference dimension as the first mean; if the stability coefficient of the target peritoneal dialysis patient in the reference dimension is less than the first mean, then the reference dimension is considered to be an abnormal preset dimension of the target peritoneal dialysis patient; traverse all preset dimensions to obtain all abnormal preset dimensions of the target peritoneal dialysis patient.

4. The peritoneal dialysis patient physiological parameter analysis and complication early warning system according to claim 1, characterized in that, The method for obtaining the number of pre-defined dimensions of correlated anomalies includes: Among all peritoneal dialysis patients, those with complications and the most abnormal preset dimensions were selected as reference patients; the number of abnormal preset dimensions possessed by the reference patients was used as the number of relevant abnormal preset dimensions.

5. The peritoneal dialysis patient physiological parameter analysis and complication early warning system according to claim 1, characterized in that, The methods for obtaining the risk index for developing complications include: The ratio of the number of abnormal preset dimensions in the target peritoneal dialysis patient to the total number of preset dimensions is calculated as the degree of physiological abnormality in the target peritoneal dialysis patient. The risk index is obtained according to the risk index calculation formula, which is shown below: In the formula, This indicates the risk index of complications in the target peritoneal dialysis patient; This indicates the number of relevant predefined abnormal dimensions in all peritoneal dialysis patients; This indicates the number of predefined abnormal dimensions in the target peritoneal dialysis patient; Indicates the degree of physiological abnormalities in the target peritoneal dialysis patient; This represents the mean stability coefficient of physiological data for all peritoneal dialysis patients in the reference dimension. This represents the stability coefficient of the physiological data of the target peritoneal dialysis patient in the reference dimension.

6. The system for analyzing physiological parameters and predicting complications in peritoneal dialysis patients according to claim 1, characterized in that, The risk index is used to provide early warning of complications in target peritoneal dialysis patients, including: When the risk index exceeds the preset first threshold, the target peritoneal dialysis patient is considered to have complications, and an early warning is issued.

7. A method for analyzing physiological parameters and predicting complications in peritoneal dialysis patients, characterized in that, The method includes: Collect physiological data of the target peritoneal dialysis patient across all preset dimensions at different preset times; Select any preset dimension of patient physiological data as the physiological data under the reference dimension; based on the difference between the maximum data range of physiological data under the reference dimension and the standard healthy data range of physiological data under the reference dimension at all preset time points, and the change characteristics of the reference dimension data at each two adjacent preset time points, obtain the stability coefficient of the target peritoneal dialysis patient in the reference dimension; based on the difference in stability coefficients between the target peritoneal dialysis patient and all other patients in each preset dimension, screen out the abnormal preset dimensions of the target peritoneal dialysis patient; based on the number of all other patients who have the same abnormal preset dimension and also have complications, obtain the number of correlated abnormal preset dimensions; based on the difference in stability coefficients of each abnormal preset dimension of the target peritoneal dialysis patient and the number of correlated abnormal preset dimensions, obtain the risk index of complications for the target peritoneal dialysis patient; The risk index is used to provide early warning of complications in target peritoneal dialysis patients.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the peritoneal dialysis patient physiological parameter analysis and complication early warning system as described in any one of claims 1 to 6.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the peritoneal dialysis patient physiological parameter analysis and complication early warning system as described in any one of claims 1 to 6.

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