Peritoneal dialysis patient follow-up visit system and patient information remote monitoring method

Abstract

The invention relates to the technical field of peritoneal dialysis, in particular to a peritoneal dialysis patient follow-up visit system and a patient information remote monitoring method. In the neighborhood range of each examination, for the examination sample space or any symptom sample space, according to the position distribution of different data points in the sample space, the overall health degree of the patient corresponding to the examination sample space in each examination and the local health degree corresponding to each symptom sample space are obtained; obtaining the self-control degree of the patient for each symptom in each examination; further obtaining the drug intervention degree of the patient for each symptom in each examination; combining the local health degree difference of the corresponding symptom sample space between each examination and the adjacent previous examination; and carrying out follow-up visit on the patient. According to the method, the effectiveness of follow-up visit of the patient is improved by obtaining the accurate drug intervention degree of the patient on each symptom in each examination.

Description

Technical Field

[0001] The present invention relates to the technical field of peritoneal dialysis, and in particular to a peritoneal dialysis patient follow-up system and a patient information remote monitoring method. Background Art

[0002] Long-term peritoneal dialysis can cause symptoms such as increased blood lipids, increased cholesterol, malnutrition, and high blood sugar in peritoneal dialysis patients. Therefore, peritoneal dialysis patients need to be examined to improve their physical condition. In the prior art, the physical sign data obtained from each examination of the patient is analyzed based on thresholds set by experience. However, since the physical condition and living habits of each patient are different, the traditional method lacks a relatively standard judgment on whether peritoneal dialysis patients should undergo drug injection intervention due to symptoms such as malnutrition and high blood sugar caused by long-term dialysis, resulting in inaccurate follow-up arrangements. Summary of the invention

[0003] In order to solve the technical problems of lack of standard judgment on drug intervention and inaccurate follow-up arrangement, the purpose of the present invention is to provide a peritoneal dialysis patient follow-up system and a patient information remote monitoring method. The technical solutions adopted are as follows: The present invention proposes a method for remote monitoring of peritoneal dialysis patient information, the method comprising: Acquire multiple physical sign data of peritoneal dialysis patients in each examination, and multiple physical sign and symptom data for each symptom, to form data points in the examination sample space and each symptom sample space, wherein the sample space includes data points of a healthy control population; Within the neighborhood of each examination, for the examination sample space or any symptom sample space, according to the position distribution of different data points in the sample space, the patient's overall health degree in the corresponding examination sample space at each examination and the local health degree in the corresponding symptom sample space are obtained, and the patient's self-control degree for each symptom at each examination is obtained; According to the overall health distribution of the patient's examination sample space corresponding to different examinations within the neighborhood of each examination, and the local health of each symptom sample space corresponding to each examination, the degree of drug intervention of the patient for each symptom in each examination is obtained; according to the patient's self-control degree for each symptom in each examination, the degree of drug intervention, and the difference in the local health of the symptom sample space corresponding to each examination and the previous examination, the degree of drug intervention correction of the patient for each symptom in each examination is obtained; Patients were followed up according to the degree of modification of the described drug intervention.

[0004] Furthermore, obtaining the overall health of the patient in each examination corresponding to the examination sample space, and the local health of each symptom sample space, includes: Within the neighborhood of each examination, for the examination sample space or any symptom sample space, multiple data clusters of each examination are obtained according to the position distribution of different data points in the sample space, and the healthy core clusters are screened out; Obtaining the relative distance between the center point of the data clustering cluster where the corresponding data point of the patient in each examination is located and the healthy core clustering cluster; selecting the maximum value of the corresponding relative distances in all examinations, and calculating the ratio between the relative distance corresponding to each examination and the maximum value of the relative distance as the first ratio; Count the number of examinations in which the first ratio of all examinations is less than the preset ratio, as the number of health examinations; obtain the ratio of the number of health examinations to the number of all examinations, as the health of the corresponding sample space; the health of the sample space includes the overall health of the inspection sample space, or the local health of each symptom sample space.

[0005] Furthermore, the method for obtaining the data clustering clusters includes: Within the neighborhood of each examination, for the examination sample space or any symptom sample space, DBSCAN clustering is performed on all data points according to the location distribution of different data points in the sample space to obtain multiple data clustering clusters for each examination.

[0006] Furthermore, the method for obtaining the healthy core cluster includes: The data cluster with the most corresponding data points of the healthy control population is selected, and the corresponding data cluster is used as the healthy core cluster.

[0007] Furthermore, the method for obtaining the degree of self-control includes: For each symptom sample space, the relative distance between each data point in the data cluster where the corresponding data point is located in each examination of the patient and the center point of the corresponding healthy core cluster is obtained, and the data point with the smallest relative distance is selected as the reference data point; Obtaining a ratio of a relative distance between a data point corresponding to each examination of the patient and a center point of the data cluster to which it belongs, and a maximum relative distance between a data point corresponding to other examinations and a center point of the data cluster to which it belongs before each examination, as a first control coefficient; A first line is constructed from the patient's corresponding data point in the previous examination to the corresponding data point in each examination, and a second line is constructed from the reference data point to the center point of the healthy core cluster. According to the cosine value of the angle between the first line and the second line and the first control coefficient, the patient's degree of self-control over each symptom in each examination is obtained, and the cosine value of the angle and the first control coefficient are positively correlated with the degree of self-control.

[0008] Furthermore, the method for obtaining the degree of drug intervention includes: Obtaining the maximum value of the overall health of the patient in the inspection sample space corresponding to different inspections within the neighborhood of each inspection, and calculating the difference between the maximum value of the overall health and the overall health of the patient corresponding to each inspection as the first difference; The first difference and the ratio of the local health degree of each symptom sample space corresponding to each examination are obtained as the degree of drug intervention for each symptom of the patient in each examination.

[0009] Furthermore, the method for obtaining the degree of drug intervention correction includes: Obtain the difference in the local health of the patient in the corresponding symptom sample space between the previous examination and each examination as the local health difference; Obtain the ratio between the patient's self-control degree for each symptom in each examination and the local health difference, and normalize it as an adjustment parameter; According to the adjustment parameters and the degree of drug intervention of the patient for each symptom in each examination, the modified degree of drug intervention of the patient for each symptom in each examination is obtained.

[0010] Furthermore, the method for obtaining the degree of drug intervention correction includes: The sum of the positive integer 1 and the adjustment parameter is obtained as the weighted value; the product of the weighted value and the degree of drug intervention of the patient for each symptom in each examination is obtained as the degree of drug intervention correction of the patient for each symptom in each examination.

[0011] Furthermore, the preset ratio is 0.5.

[0012] The present invention also proposes a method for remotely monitoring peritoneal dialysis patient information, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any one of the steps of the method for remotely monitoring peritoneal dialysis patient information is implemented.

[0013] The present invention has the following beneficial effects: The present invention obtains the overall health of the patient in the inspection sample space corresponding to each inspection and the local health of each symptom sample space according to the position distribution of different data points in the sample space within the neighborhood of each inspection, for the inspection sample space or any symptom sample space, which reflects the patient's overall health status at each inspection and the health status under each symptom, obtains the patient's self-control degree for each symptom in each inspection, and evaluates the patient's initiative and ability for disease management; according to the overall health distribution of the inspection sample space corresponding to different inspections of the patient within the neighborhood of each inspection, and the local health of each symptom sample space corresponding to each inspection, obtains the patient's drug intervention degree for each symptom in each inspection, and evaluates the patient's drug demand for each symptom; according to the patient's self-control degree for each symptom in each inspection, the drug intervention degree and the difference in the local health of the corresponding symptom sample space between each inspection and the adjacent previous inspection, obtains the patient's drug intervention correction degree for each symptom in each inspection, can dynamically monitor the patient's health status changes, and help to obtain a more accurate degree of intervention demand; follow up the patient. The present invention improves the effectiveness of patient follow-up by obtaining the patient's accurate drug intervention degree for each symptom in each inspection. 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 flowchart of a method for remote monitoring of peritoneal dialysis patient information provided by one embodiment of the present invention; Figure 2 A flow chart of a method for obtaining a self-control degree provided by an embodiment of the present invention; Figure 3 A flow chart of a method for obtaining the degree of drug intervention correction provided by one 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 specific implementation, structure, features and effects of a peritoneal dialysis patient follow-up system and a patient information remote monitoring method proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[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 following is a detailed description of a peritoneal dialysis patient follow-up system and a patient information remote monitoring method provided by the present invention in conjunction with the accompanying drawings.

[0019] See also Figure 1 , which shows a method flow chart of a method for remote monitoring of peritoneal dialysis patient information provided by an embodiment of the present invention, specifically comprising: Step S1: Acquire multiple physical sign data of peritoneal dialysis patients in each examination, and multiple physical sign and symptom data for each symptom, to form data points in the examination sample space and each symptom sample space, wherein the sample space includes data points of a healthy control population.

[0020] In an embodiment of the present invention, in order to timely intervene in the adverse physical condition of peritoneal dialysis patients, it is necessary to process the patient information of the patients and obtain the physical sign data after each examination for analysis; first, by requiring peritoneal dialysis patients to upload data such as weight, blood sugar, blood pressure, heart rate, etc. that can be monitored at home by themselves at a fixed time, and reminding peritoneal dialysis patients to go to the hospital on time to check and upload data such as blood calcium, uric acid, serum protein, blood lipids, etc.; peritoneal dialysis may cause multiple symptoms such as increased blood lipids, increased cholesterol, malnutrition and high blood sugar, and the physical sign changes of each symptom in each examination are analyzed. For example, the symptoms of high and low blood sugar in peritoneal dialysis patients are related to multiple physical sign data such as blood sugar measurement, glucose tolerance test, glycosylated hemoglobin measurement, glycosylated plasma protein measurement, etc.

[0021] Acquire multiple physical sign data of peritoneal dialysis patients in each examination, as well as multiple physical sign and symptom data for each symptom, to form an examination sample space and data points in each symptom sample space, wherein the examination sample space contains data points consisting of all physical sign data of the patient in each examination, and the symptom sample space contains data points consisting of relevant physical sign and symptom data of a certain symptom.

[0022] It should be noted that, in one embodiment of the present invention, in order to more accurately judge the health status of the patient, the data points of the healthy population can be used as a normal reference standard. Therefore, the sample space includes data points of the healthy control population, and 30 healthy people who are not ill are selected as the healthy control population for the patient; in other embodiments of the present invention, the number of the healthy control population can be set according to the specific circumstances, and is not limited or elaborated here.

[0023] It should be noted that, in one embodiment of the present invention, in order to facilitate the subsequent data processing, various physical sign data and physical sign symptom data are standardized to eliminate the dimension of the data so that indicators of different units or orders of magnitude can be comprehensively analyzed and compared. Standardization can adopt existing methods such as Z-score standardization and min-max transformation. The specific means are technical means well known to those skilled in the art and will not be described in detail here.

[0024] Step S2: Within the neighborhood of each examination, for the examination sample space or any symptom sample space, according to the position distribution of different data points in the sample space, obtain the patient's overall health status of the examination sample space corresponding to each examination, as well as the local health status of each symptom sample space, and obtain the patient's self-control degree for each symptom in each examination.

[0025] In order to accurately assess the health status of patients, selecting the neighborhood range of each examination helps to focus on different data points before each examination, ensuring the accuracy and relevance of the analysis, thereby more accurately reflecting the changes in the health status of patients. Within the neighborhood range of each examination, for the examination sample space or any symptom sample space, according to the position distribution of different data points in the sample space, the overall health of the patient in each examination corresponding to the examination sample space and the local health of each symptom sample space are obtained.

[0026] Preferably, in one embodiment of the present invention, obtaining the overall health of the patient in each examination corresponding to the examination sample space and the local health of each symptom sample space includes: Within the neighborhood of each examination, for the examination sample space or any symptom sample space, multiple data clusters of each examination are obtained according to the position distribution of different data points in the sample space, and the healthy core clusters are screened out; Preferably, in one embodiment of the present invention, the method for obtaining data clusters includes: Within the neighborhood of each examination, for the examination sample space or any symptom sample space, DBSCAN clustering is performed on all data points according to the location distribution of different data points in the sample space to obtain multiple data clustering clusters for each examination.

[0027] The DBSCAN clustering algorithm can effectively identify data points within the neighborhood radius and divide them into clusters, while excluding the influence of noise points. It should be noted that in one embodiment of the present invention, the parameters of DBSCAN clustering are set as follows: the size of the neighborhood radius parameter determines the density of clustering, the neighborhood radius is 5, and the minimum number of data point sets is 3, that is, only when the cluster corresponding to the data point contains at least 3 data points can the clustering condition be met and clustering be performed; in other embodiments of the present invention, the clustering parameters can be set according to specific circumstances, and are not limited or elaborated here.

[0028] It should be noted that, in one embodiment of the present invention, the neighborhood range of each inspection is based on each inspection and the range formed by all historical inspections; in other embodiments of the present invention, the size of the neighborhood range can be set according to specific circumstances, which is not limited or elaborated here.

[0029] Preferably, in one embodiment of the present invention, the method for obtaining healthy core clusters includes: The data cluster with the most corresponding data points of the healthy control population is selected and the corresponding data cluster is used as the healthy core cluster. The more corresponding data points of the healthy control population, the closer the corresponding data cluster is to the normal physical sign, reflecting the distribution of data points when the physical signs are healthy.

[0030] Obtaining the relative distance between the center point of the data clustering cluster where the corresponding data point of the patient in each examination is located and the healthy core clustering cluster; selecting the maximum value of the corresponding relative distances in all examinations, and calculating the ratio between the relative distance corresponding to each examination and the maximum value of the relative distance as the first ratio; Count the number of examinations in which the first ratio of all examinations is less than the preset ratio, which is used as the number of health examinations; obtain the ratio of the number of health examinations to the number of all examinations, which is used as the health of the corresponding sample space; the health of the sample space includes the overall health of the inspection sample space, or the local health of each symptom sample space.

[0031] It should be noted that, in one embodiment of the present invention, the preset ratio is 0.5. In other embodiments of the present invention, the size of the preset ratio can be set according to specific circumstances, which is not limited or elaborated here.

[0032] It should be noted that the method for obtaining the center point is to calculate the mean of the corresponding position coordinates of all data points in the data cluster to obtain the center point position coordinates, and then analyze the relative distance between them; in some embodiments of the present invention, the relative distance can be calculated by calculating the existing distance methods such as Euclidean distance or Manhattan distance. The specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0033] Symptoms caused by peritoneal dialysis can be adjusted through the lifestyle of peritoneal dialysis patients. For example, high blood sugar can be controlled by controlling the intake of foods with high sugar content. Under each symptom, the higher the degree of control over lifestyle, the better the understanding of the patient's ability to self-manage their health status. The degree of self-control of each symptom by the patient was obtained at each examination.

[0034] Preferably, in one embodiment of the present invention, the method for obtaining the degree of self-control is described in Figure 2 , which shows a flow chart of a method for obtaining the degree of self-control, including: Step S201: For each symptom sample space, obtain the relative distance between each data point in the data cluster where the corresponding data point is located in each examination of the patient and the center point of the corresponding healthy core cluster, and select the data point with the smallest relative distance as the reference data point.

[0035] The healthy core cluster represents an ideal or healthy state. The data point with the smallest relative distance to the center point of the healthy core cluster is selected as the reference data point, which can better represent the degree to which the patient is close to the healthy state in this examination.

[0036] Step S202: Obtain the ratio of the relative distance between the patient's corresponding data point in each examination and the center point of the data cluster to the maximum relative distance between the patient's corresponding data point in other examinations and the center point of the data cluster before each examination as the first control coefficient.

[0037] The greater the relative distance between the patient's corresponding data point in each examination and the center point of the data cluster, the farther the physical sign performance under the corresponding examination is from the center point, the better the control is, and the greater the first control coefficient.

[0038] Step S203: construct a first line from the patient's corresponding data point in the previous examination to the corresponding data point in each examination, and a second line from the reference data point to the center point of the healthy core cluster; according to the cosine value of the angle between the first line and the second line and the first control coefficient, the patient's self-control degree for each symptom in each examination is obtained, and the cosine value of the angle and the first control coefficient are positively correlated with the self-control degree.

[0039] In one embodiment of the present invention, for any symptom sample space, the formula for the degree of self-control is expressed as: ; in, Indicates that the patient Check your own control over each symptom; Indicates that the patient Check the relative distance between the corresponding data point and the center point of the data cluster; Indicates that the patient The maximum relative distance between the corresponding data points of other inspections and the center point of the data cluster before the inspection; It represents the angle between the first line from the corresponding data point of the previous examination to the corresponding data point of each examination of the patient, and the second line from the reference data point to the center point of the healthy core cluster.

[0040] In the formula for the degree of self-control, Indicates the cosine value of the angle. The smaller the angle, the The closer the data cluster and the healthy core cluster are to the previous inspection, the The healthier the physical condition shown in the first examination, the larger the cosine value of the angle, indicating a greater degree of self-control; Indicates that the patient The relative distance between the corresponding data point and the center point of the data cluster in the first check, and the The ratio of the maximum relative distance between the corresponding data points of other examinations and the center point of the data cluster before the first examination, that is, the first control coefficient. The larger the ratio, the higher the patient's The greater the relative distance between the corresponding data point of the second inspection and the center point of the data cluster, the greater the degree of self-control.

[0041] Step S3: According to the overall health distribution of the patient's examination sample space corresponding to different examinations within the neighborhood of each examination, and the local health of each symptom sample space corresponding to each examination, the degree of drug intervention of the patient for each symptom in each examination is obtained; according to the patient's self-control degree for each symptom in each examination, the degree of drug intervention, and the difference in the local health of the symptom sample space corresponding to each examination with the previous examination, the degree of drug intervention correction of the patient for each symptom in each examination is obtained.

[0042] Health status changes over time. The overall health distribution reflects the overall trend of the patient's health status in the neighborhood, and the local health reflects the health status of specific symptoms. Combined analysis helps to comprehensively evaluate the patient's physical health status in each examination and quantify the degree of drug intervention. The degree of drug intervention for each symptom of the patient in each examination is obtained based on the overall health distribution of the examination sample space corresponding to different examinations of the patient in the neighborhood of each examination, and the local health of the sample space corresponding to each symptom of each examination.

[0043] Preferably, in one embodiment of the present invention, the method for obtaining the degree of drug intervention includes: Obtaining the maximum value of the overall health of the patient in the inspection sample space corresponding to different inspections within the neighborhood of each inspection, and calculating the difference between the maximum value of the overall health and the overall health of the patient corresponding to each inspection as the first difference; The first difference and the ratio of the local health degree of each symptom sample space corresponding to each examination are obtained as the degree of drug intervention for each symptom of the patient in each examination.

[0044] In one embodiment of the present invention, the formula for the degree of drug intervention is expressed as: ; in, Indicates that the patient The second inspection is for the degree of drug intervention for each symptom; Indicates that the patient The overall health of each checkup; Indicates that the patient The maximum value of the overall health corresponding to different inspections within the neighborhood of the inspection; Indicates that the patient The second inspection corresponds to The local health of a symptom state space.

[0045] In the formula for the degree of drug intervention, Indicates the maximum overall health and the patient's The difference between the overall health scores of the first and second examinations is the first difference. The larger the difference, the better the patient is in the first The lower the overall health score corresponding to the first examination, the worse the patient's overall physical condition is during the examination, and the more medication intervention is needed. The second inspection corresponds to The smaller the local health degree of a symptom state space, the worse the patient's physical condition in terms of the corresponding symptom, and the worse the degree of drug intervention.

[0046] The degree of self-control of patients reflects their initiative and ability to manage symptoms, which directly affects the effect of drug intervention. Patients with high self-control can delay drug intervention through good self-control; the degree of drug intervention directly reflects the strength of drug demand for patients' symptoms; the difference in local health reflects the change of patients' symptoms between two adjacent examinations. The greater the change, the greater the possibility of drug intervention; according to the degree of self-control of patients for each symptom in each examination, the degree of drug intervention, and the difference in local health of the corresponding symptom sample space between each examination and the previous examination, the degree of drug intervention correction for each symptom in each examination is obtained.

[0047] Preferably, in one embodiment of the present invention, the method for obtaining the drug intervention correction degree can be found in Figure 3 , which shows a flow chart of a method for obtaining the degree of drug intervention correction, including: Step S301: obtaining the difference in the local health degree of the patient in the corresponding symptom sample space between the previous examination and each examination as the local health difference.

[0048] In order to quantify the change in the patient's health status between two examinations, it is necessary to calculate the difference in local health status. The larger the difference, the greater the change in health status.

[0049] Step S302: Obtain the ratio between the patient's self-control level for each symptom in each examination and the local health difference, and normalize it as an adjustment parameter.

[0050] The degree of self-control of the patient has an important impact on health status. The better the self-control, that is, the better the living habits and other conditions, the greater the possibility of changes between examinations. At this time, the larger the local health difference, the greater the health change and the larger the adjustment parameter.

[0051] Step S303: Obtain the degree of drug intervention correction for each symptom of the patient in each examination according to the adjustment parameters and the degree of drug intervention for each symptom of the patient in each examination.

[0052] Preferably, in one embodiment of the present invention, the method for obtaining the degree of drug intervention correction includes: The sum of the positive integer 1 and the adjustment parameter is obtained as the weighted value; the product of the weighted value and the degree of drug intervention of the patient for each symptom in each examination is obtained as the degree of drug intervention correction of the patient for each symptom in each examination.

[0053] In one embodiment of the present invention, the formula for the degree of drug intervention correction is expressed as: ; in, Indicates that the patient The second inspection is for the degree to which the drug intervention modifies each symptom; Indicates that the patient The second inspection is for the degree of drug intervention for each symptom; Indicates that the patient The second inspection is for The degree of self-control over the symptoms; Indicates that the patient The second inspection corresponds to The local health of the symptom state space; Indicates that the patient The second inspection corresponds to The local health of the symptom state space; represents the hyperbolic tangent function.

[0054] In the formula for the degree of drug intervention modification, Indicates that the patient The second inspection and The second inspection corresponds to The difference in the local health degree of the symptom state space is the local health difference. The larger the difference, the better the patient is in the first The second inspection corresponds to The greater the local health degree of a symptom state space, the worse the patient's health status in the corresponding symptom, the greater the local health difference, and the more drug intervention is needed; Indicates that the patient The ratio between the degree of self-control of each symptom and the local health difference is checked and Normalization is performed as an adjustment parameter. The larger the adjustment parameter, the greater the degree of self-control, the greater the credibility of the local health difference, the greater the local health, the greater the degree of correction for drug intervention, and the smaller the local health, the smaller the degree of correction for drug intervention.

[0055] In order to more comprehensively analyze the patient's overall physical condition at each examination, the degree of drug intervention correction for each symptom was summarized to obtain the degree of drug intervention correction for the patient at each examination, which helps to integrate the correction degree of different symptoms into an overall evaluation value. The degree of drug intervention correction for the patient at each examination was obtained.

[0056] Step S4: Follow up the patient based on the degree of drug intervention modification.

[0057] Each patient's health status, disease type and severity are different, so the degree of need for drug intervention will also vary. Follow-up based on the degree of drug intervention correction can develop a personalized follow-up plan for the specific circumstances of different patients, which will help to observe patients more promptly.

[0058] It should be noted that, in another embodiment of the present invention, the degree of correction of drug intervention for all symptoms of the patient in each examination is obtained, and the patient is followed up, including: selecting the maximum value of the degree of correction of drug intervention for all symptoms of different patients in each examination as the reference degree of drug intervention, and arranging follow-up for the patients in descending order of the reference degree of drug intervention. The greater the reference degree of drug intervention, the worse the patient's physical condition, and the more early observation is needed, which helps to monitor the patient's physical condition more timely and accurately.

[0059] In summary, the present invention obtains the overall health of the patient in the corresponding inspection sample space of each inspection and the local health of each symptom sample space according to the position distribution of different data points in the sample space within the neighborhood of each inspection, and obtains the patient's self-control degree for each symptom in each inspection; and then obtains the patient's drug intervention degree for each symptom in each inspection; combines the difference in the local health of the corresponding symptom sample space between each inspection and the adjacent previous inspection, obtains the patient's drug intervention correction degree in each inspection; and follows up the patient. The present invention improves the rationality of patient follow-up by obtaining the patient's accurate drug intervention degree for each symptom in each inspection.

[0060] The present invention also proposes a peritoneal dialysis patient follow-up system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any one of the steps of a method for remotely monitoring peritoneal dialysis patient information is implemented.

[0061] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0062] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for remote monitoring of peritoneal dialysis patient information, characterized in that: The method comprises: Acquire multiple physical sign data of peritoneal dialysis patients in each examination, and multiple physical sign and symptom data for each symptom, to form data points in the examination sample space and each symptom sample space, wherein the sample space includes data points of a healthy control population; Within the neighborhood of each examination, for the examination sample space or any symptom sample space, according to the position distribution of different data points in the sample space, the patient's overall health degree in the corresponding examination sample space at each examination and the local health degree in the corresponding symptom sample space are obtained, and the patient's self-control degree for each symptom at each examination is obtained; According to the overall health distribution of the patient's examination sample space corresponding to different examinations within the neighborhood of each examination, and the local health of each symptom sample space corresponding to each examination, the degree of drug intervention of the patient for each symptom in each examination is obtained; according to the patient's self-control degree for each symptom in each examination, the degree of drug intervention, and the difference in the local health of the symptom sample space corresponding to each examination and the previous examination, the degree of drug intervention correction of the patient for each symptom in each examination is obtained; Patients were followed up according to the degree of modification of the described drug intervention.

2. A method for remote monitoring of peritoneal dialysis patient information according to claim 1, characterized in that: The method of obtaining the overall health of the patient in the examination sample space corresponding to each examination, and the local health of each symptom sample space corresponding to each symptom sample space, includes: Within the neighborhood of each examination, for the examination sample space or any symptom sample space, multiple data clusters of each examination are obtained according to the position distribution of different data points in the sample space, and the healthy core clusters are screened out; Obtaining the relative distance between the center point of the data clustering cluster where the corresponding data point of the patient in each examination is located and the healthy core clustering cluster; selecting the maximum value of the corresponding relative distances in all examinations, and calculating the ratio between the relative distance corresponding to each examination and the maximum value of the relative distance as the first ratio; Count the number of examinations in which the first ratio of all examinations is less than the preset ratio, as the number of health examinations; obtain the ratio of the number of health examinations to the number of all examinations, as the health of the corresponding sample space; the health of the sample space includes the overall health of the inspection sample space, or the local health of each symptom sample space.

3. A method for remote monitoring of peritoneal dialysis patient information according to claim 2, characterized in that: The method for obtaining the data clustering clusters includes: Within the neighborhood of each examination, for the examination sample space or any symptom sample space, DBSCAN clustering is performed on all data points according to the location distribution of different data points in the sample space to obtain multiple data clustering clusters for each examination.

4. A method for remote monitoring of peritoneal dialysis patient information according to claim 3, characterized in that: The method for obtaining the healthy core cluster includes: The data cluster with the most corresponding data points of the healthy control population is selected, and the corresponding data cluster is used as the healthy core cluster.

5. A method for remote monitoring of peritoneal dialysis patient information according to claim 4, characterized in that: The method for obtaining the degree of self-control includes: For each symptom sample space, the relative distance between each data point in the data cluster where the corresponding data point is located in each examination of the patient and the center point of the corresponding healthy core cluster is obtained, and the data point with the smallest relative distance is selected as the reference data point; Obtaining a ratio of a relative distance between a data point corresponding to each examination of the patient and a center point of the data cluster to which it belongs, and a maximum relative distance between a data point corresponding to other examinations and a center point of the data cluster to which it belongs before each examination, as a first control coefficient; A first line is constructed from the patient's corresponding data point in the previous examination to the corresponding data point in each examination, and a second line is constructed from the reference data point to the center point of the healthy core cluster. According to the cosine value of the angle between the first line and the second line and the first control coefficient, the patient's degree of self-control over each symptom in each examination is obtained, and the cosine value of the angle and the first control coefficient are positively correlated with the degree of self-control.

6. A method for remote monitoring of peritoneal dialysis patient information according to claim 1, characterized in that: The method for obtaining the drug intervention degree includes: Obtaining the maximum value of the overall health of the patient in the inspection sample space corresponding to different inspections within the neighborhood of each inspection, and calculating the difference between the maximum value of the overall health and the overall health of the patient corresponding to each inspection as the first difference; The first difference and the ratio of the local health degree of each symptom sample space corresponding to each examination are obtained as the degree of drug intervention for each symptom of the patient in each examination.

7. A method for remote monitoring of peritoneal dialysis patient information according to claim 1, characterized in that: The method for obtaining the degree of drug intervention correction includes: Obtain the difference in the local health of the patient in the corresponding symptom sample space between the previous examination and each examination as the local health difference; Obtain the ratio between the patient's self-control degree for each symptom in each examination and the local health difference, and normalize it as an adjustment parameter; According to the adjustment parameters and the degree of drug intervention of the patient for each symptom in each examination, the modified degree of drug intervention of the patient for each symptom in each examination is obtained.

8. A method for remote monitoring of peritoneal dialysis patient information according to claim 7, characterized in that: The method for obtaining the degree of drug intervention correction includes: The sum of the positive integer 1 and the adjustment parameter is obtained as the weighted value; the product of the weighted value and the degree of drug intervention of the patient for each symptom in each examination is obtained as the degree of drug intervention correction of the patient for each symptom in each examination.

9. A method for remote monitoring of peritoneal dialysis patient information according to claim 2, characterized in that: The preset ratio is 0.

5.

10. A peritoneal dialysis patient follow-up system, 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, the steps of the method for remote monitoring of peritoneal dialysis patient information as described in any one of claims 1 to 9 are implemented.

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