Medical nursing data processing method, system and electronic equipment

Abstract

The present invention relates to the technical field of medical data processing, and proposes a medical nursing data processing method, system, and electronic device, comprising: obtaining medical nursing data from a plurality of patients; performing cluster analysis on physiological data of the same dimension in the medical nursing data of different patients to obtain the degree of physiological deviation of each physiological data in each dimension for each patient; obtaining the deviation amount of all physiological data of the same dimension for the same patient to obtain the degree of physiological difference of each physiological data in each dimension for each patient; obtaining a plurality of abnormal physiological data in each dimension for each patient; obtaining the priority nursing degree for each dimension for each patient; obtaining the treatment priority of each patient; and processing the medical nursing data of the patients. The present invention aims to solve the problem of ignoring differences in patients' physical fitness and symptoms during rehabilitation effect analysis using medical nursing data.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and in particular to a medical nursing data processing method, system and electronic equipment. Background Art

[0002] Medical and nursing data include patients' medical records, treatment records, living habits and other related information. Patients' diagnosis and treatment information corresponds to medical data, and their living habits during treatment correspond to nursing data. By processing medical and nursing data, data collection and integration, data analysis and mining, and data visualization are carried out under medical and nursing big data, which can more accurately assess disease risks and formulate personalized nursing plans, thereby improving treatment outcomes.

[0003] In the process of processing the medical and nursing data of a large number of patients, the processing of medical and nursing data is usually used to analyze the patient's nursing status, that is, to evaluate the patient's rehabilitation effect through the patient's nursing status; usually, the medical and nursing data of a large number of patients are analyzed, and then the deviation of the medical and nursing data of a single patient is processed. However, due to the differences in symptoms among patients and the differences in the degree of rehabilitation after nursing, it is impossible to accurately evaluate the rehabilitation effect. That is, in the process of quantifying the rehabilitation effect by analyzing the deviation of the medical and nursing data of a large number of patients, the differences in symptoms and the physical fitness of the patients themselves will be ignored; therefore, it is necessary to analyze the recovery process of the patient's own medical and nursing data. Summary of the Invention

[0004] The present invention provides a medical nursing data processing method, system, and electronic device to solve the problem of ignoring differences in patients' physical fitness and symptoms during the existing analysis of rehabilitation effects using medical nursing data. The technical solutions adopted are as follows:

[0005] The present invention proposes a medical nursing data processing method, which includes the following steps:

[0006] Acquiring medical nursing data of a plurality of patients, wherein the medical nursing data includes multi-dimensional physiological data;

[0007] Cluster analysis is performed on physiological data of the same dimension in medical nursing data of different patients to obtain several clusters in the clustering results; based on the clusters in which the physiological data are located and the clustering results, the physiological deviation degree of each physiological data in each dimension of each patient is obtained; the deviation amount is obtained for all physiological data of the same dimension of the same patient, and the difference between adjacent time series data of the deviation amount is analyzed to correct the physiological deviation degree to obtain the physiological difference degree of each physiological data in each dimension of each patient;

[0008] Determine the degree of physiological difference to obtain a number of abnormal physiological data in each dimension of each patient; obtain the priority care degree in each dimension of each patient based on the distribution of abnormal physiological data in the same dimension of the same patient and the degree of physiological difference of all physiological data; and obtain the treatment priority of each patient by combining the degree of physiological difference of each dimension of the physiological data of the same patient at the current moment;

[0009] The medical care data of each patient is processed according to the treatment priority of each patient.

[0010] Optionally, the cluster analysis is performed on physiological data of the same dimension in the medical nursing data of different patients to obtain several clusters in the clustering results, including the following specific methods:

[0011] Any patient is taken as the target patient, and the physiological data of the target patient's blood oxygen dimension is taken as the target patient's blood oxygen data; DBSCAN clustering is performed on all the target patient's blood oxygen data and all the blood oxygen data of a large number of patients obtained. The distance measurement uses the absolute value of the difference between the blood oxygen data to obtain the clustering results and several clusters therein.

[0012] Optionally, the physiological deviation degree of each physiological data of each dimension of each patient is obtained by:

[0013] Obtain the number of blood oxygen data in each cluster of the blood oxygen dimension; take the cluster with the largest number of blood oxygen data among all clusters of the blood oxygen dimension as the reference cluster of the blood oxygen dimension; the overall deviation factor of any cluster in the blood oxygen dimension The calculation method is:

[0014]

[0015] in, Indicates the number of blood oxygen data in this cluster, Indicates the number of blood oxygen data in the reference cluster of the blood oxygen dimension, Represents the mean of all blood oxygen data in this cluster, Represents the mean of all blood oxygen data in the reference cluster of the blood oxygen dimension, Indicates the maximum absolute value of the difference between the blood oxygen data in the blood oxygen dimension;

[0016] For any blood oxygen data of the target patient, obtain the absolute value of the difference between the blood oxygen data and the blood oxygen data corresponding to the center point of the cluster where it is located, and obtain the absolute value of the difference and The product of the ratio and the overall deviation factor of the cluster to which the blood oxygen data belongs is used as the physiological deviation degree of the blood oxygen data of the target patient.

[0017] Optionally, the method of obtaining the deviation amount for all physiological data of the same dimension of the same patient, analyzing the difference between adjacent temporal data of the deviation amount, correcting the physiological deviation degree, and obtaining the physiological difference degree of each physiological data of each dimension of each patient includes the following specific methods:

[0018] The mean of all blood oxygen data in the reference cluster of the blood oxygen dimension is used as the standard blood oxygen data of the blood oxygen dimension; the difference between any blood oxygen data of the target patient and the standard blood oxygen data is obtained as the blood oxygen offset of the target patient's blood oxygen data; the overall deviation consistency of the target patient's blood oxygen dimension is obtained. The calculation method is:

[0019]

[0020] in, Represents the variance of the blood oxygen offset of all blood oxygen data of the target patient, Indicates the number of blood oxygen data of the target patient, Indicates the target patient The blood oxygen offset of each blood oxygen data, Represents the mean of all blood oxygen offsets of all blood oxygen data of the target patient, represents the standard deviation of all blood oxygen offsets of all blood oxygen data of the target patient; represents the absolute value function, represents an exponential function with a natural constant as its base;

[0021] According to the distribution range of the blood oxygen offset of the target patient's blood oxygen data and the difference in the blood oxygen offset of adjacent blood oxygen data, a local deviation factor of each blood oxygen data of the target patient is obtained;

[0022] Target patients The physiological difference of blood oxygen data The calculation method is:

[0023]

[0024] in, Indicates the target patient The local deviation factor of blood oxygen data, Indicates the target patient The physiological deviation degree of blood oxygen data, Indicates the overall deviation consistency of the target patient's blood oxygen dimension.

[0025] Optionally, the method of obtaining the local deviation factor of each blood oxygen data of the target patient according to the distribution range of the blood oxygen offset of the blood oxygen data of the target patient and the difference in the blood oxygen offset of adjacent blood oxygen data includes the following specific methods:

[0026] For any blood oxygen data of the target patient, the blood oxygen offset of the blood oxygen data is removed, and the variance of the blood oxygen offset of other blood oxygen data of the target patient is recalculated and used as the denoted offset variance of the blood oxygen data;

[0027] The difference between the variance of the blood oxygen offset of all the blood oxygen data of the target patient and the decentered offset variance of the blood oxygen data is used as the variance deviation factor of the blood oxygen data; the variance deviation factor of all the blood oxygen data of the target patient is linearly normalized, and the result is used as the variance deviation of each blood oxygen data of the target patient;

[0028] Construct target patient The neighborhood range of blood oxygen data of the target patient Local deviation factor of blood oxygen data The calculation method is:

[0029]

[0030] in, Indicates the target patient The variance deviation of blood oxygen data, Indicates the target patient The number of blood oxygen data in the neighborhood of blood oxygen data, Indicates the target patient The blood oxygen offset of each blood oxygen data, Indicates the target patient The blood oxygen data is within the neighborhood of The blood oxygen offset of each blood oxygen data, Indicates the maximum blood oxygen offset value among all blood oxygen data of the target patient.

[0031] Optionally, the method of obtaining the priority care degree of each dimension of each patient based on the distribution of abnormal physiological data of the same dimension of the same patient and the degree of physiological difference of all physiological data includes the following specific methods:

[0032] Recording the target patient's blood oxygen data other than the abnormal blood oxygen data as non-abnormal blood oxygen data; and treating the target patient's continuous non-abnormal blood oxygen data in time series as a blood oxygen recovery segment of the target patient;

[0033] Compare the number of non-abnormal blood oxygen data in adjacent blood oxygen recovery segments, combine the abnormal blood oxygen data between adjacent blood oxygen recovery segments, and the physiological difference between the non-abnormal blood oxygen data in the blood oxygen recovery segments, and obtain the physiological recovery degree of each blood oxygen recovery segment of the target patient;

[0034] The number of non-abnormal blood oxygen data in all blood oxygen rehabilitation segments of the target patient is weighted and normalized, and the result is used as the rehabilitation weight of each blood oxygen rehabilitation segment of the target patient; the priority care degree of the target patient's blood oxygen dimension The calculation method is:

[0035]

[0036] in, It represents the mean value of the physiological difference of all non-abnormal blood oxygen data in the last blood oxygen rehabilitation segment of the target patient. Indicates the number of non-abnormal blood oxygen data in the last blood oxygen recovery segment of the target patient. Indicates the number of blood oxygen recovery segments of the target patient, Indicates the target patient The recovery weight of each blood oxygen recovery segment, Indicates the target patient Physiological recovery level of each blood oxygen recovery segment; Represents an exponential function with a natural constant as its base.

[0037] Optionally, the method of comparing the number of non-abnormal blood oxygen data in adjacent blood oxygen rehabilitation segments, combining the abnormal blood oxygen data between adjacent blood oxygen rehabilitation segments and the physiological difference degree of the non-abnormal blood oxygen data in the blood oxygen rehabilitation segments, and obtaining the physiological rehabilitation degree of each blood oxygen rehabilitation segment of the target patient includes the following specific methods:

[0038]

[0039] in, Indicates the target patient The number of non-abnormal blood oxygen data in the blood oxygen recovery segment, Indicates the target patient The number of non-abnormal blood oxygen data in the blood oxygen recovery segment, Indicates the target patient The blood oxygen recovery stage and the The number of consecutive abnormal blood oxygen data between blood oxygen recovery segments, Indicates the target patient The mean of the physiological difference levels of all non-abnormal blood oxygen data in the blood oxygen recovery segment.

[0040] Optionally, the specific method for obtaining the treatment priority of each patient includes:

[0041] The physiological difference degree of each dimension of the physiological data of the target patient at the current moment is used as the weight of the priority treatment degree of each dimension of the target patient. The priority treatment degrees of all dimensions of the target patient are weighted and averaged, and the result is used as the treatment priority of the target patient.

[0042] The present invention also proposes a medical nursing data processing system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0043] The present invention also provides a medical nursing data processing electronic device, the device comprising:

[0044] A medical and nursing data acquisition module is used to acquire medical and nursing data of several patients;

[0045] The medical nursing data analysis module is used to perform cluster analysis on physiological data of the same dimension in the medical nursing data of different patients to obtain a number of clusters in the clustering results; based on the clusters in which the physiological data are located and the clustering results, obtain the physiological deviation degree of each physiological data in each dimension of each patient; obtain the deviation amount for all physiological data of the same dimension of the same patient, analyze the difference between adjacent data in the time series of the deviation amount, correct the physiological deviation degree, and obtain the physiological difference degree of each physiological data in each dimension of each patient;

[0046] Determine the degree of physiological difference to obtain a number of abnormal physiological data in each dimension of each patient; obtain the priority care degree in each dimension of each patient based on the distribution of abnormal physiological data in the same dimension of the same patient and the degree of physiological difference of all physiological data; and obtain the treatment priority of each patient by combining the degree of physiological difference of each dimension of the physiological data of the same patient at the current moment;

[0047] The medical nursing data processing module is used to process the medical nursing data of patients according to the processing priority of each patient.

[0048] The beneficial effects of the present invention are as follows: the present invention performs cluster analysis on the physiological data of various dimensions of a large number of patients, quantifies the degree of physiological deviation through the distribution of physiological data in the clustering results, further analyzes the changes in the physiological data of the same dimension of the same patient, corrects the degree of physiological difference, and quantifies the rehabilitation effect of the patient in each dimension based on the degree of physiological difference; wherein, by clustering the physiological data of the same dimension of different patients based on the fact that they contain a large amount of normal physiological data, the deviation of the cluster and the deviation of the physiological data in the cluster are quantified respectively, and the possibility of abnormal physiological data is preliminarily quantified; and then the overall deviation analysis is performed on the physiological data of the same dimension of the same patient to avoid the physiological data deviation caused by the difference in the physical fitness of the patients affecting the abnormality of the physiological data. Frequent analysis is performed while considering the possibility of actual abnormalities in the patient's physiological data within a local time period, so as to correct the degree of physiological differences in each physiological data; abnormal physiological data are judged based on the degree of physiological differences, and the rehabilitation effect of the patient's corresponding dimension is quantified through the distribution of abnormal physiological data in the same dimension of the patient and the change in the degree of physiological differences of other non-abnormal physiological data, so as to reflect whether the symptoms in the patient's corresponding dimension relapse and the severity of the relapse, and obtain the priority care of the patient in each dimension; further overall analysis of the priority care of each dimension is performed, and the patient's treatment priority is comprehensively quantified; finally, priority processing of medical nursing data and corresponding care for patients with poor rehabilitation effects are achieved, so as to improve the efficiency of medical nursing data processing and the applicability of use in hospital databases. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0050] Figure 1 A flowchart of a medical nursing data processing method provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] See also Figure 1, which shows a flow chart of a medical nursing data processing method provided by one embodiment of the present invention, the method comprising the following steps:

[0053] Step S001: Obtain medical nursing data of several patients.

[0054] The purpose of this embodiment is to improve the accuracy of the evaluation of the patient's rehabilitation effect by analyzing the changes in the patient's multi-dimensional physiological data and considering the differences in physical fitness between patients during the medical nursing data processing process. Therefore, it is necessary to collect the patient's medical nursing data, that is, the patient's multi-dimensional physiological data.

[0055] Specifically, for all patients currently in the hospital, physiological data of multiple physiological indicators of the patients are collected through smart bracelets or monitoring devices when they are admitted to the hospital. The multiple physiological indicators include but are not limited to electrocardiogram data, blood pressure, blood oxygen and body temperature. This embodiment does not limit the specific physiological indicators; each physiological indicator is used as a dimension to obtain multi-dimensional physiological data of each patient, wherein the physiological data of each physiological indicator are continuously monitored; the physiological data of multiple physiological indicators recorded by a large number of patients during the hospitalization treatment process are obtained in the hospital database to obtain the multi-dimensional physiological data of the corresponding patients; the multi-dimensional physiological data of each patient are respectively constituted into the medical nursing data of each patient.

[0056] Step S002: cluster analysis is performed on the physiological data of the same dimension in the medical nursing data of different patients to obtain several clusters in the clustering results; based on the clusters in which the physiological data are located and the clustering results, the physiological deviation degree of each physiological data of each dimension of each patient is obtained; the deviation amount is obtained for all physiological data of the same dimension of the same patient, and the difference between the time-series adjacent data of the deviation amount is analyzed, the physiological deviation degree is corrected, and the physiological difference degree of each physiological data of each dimension of each patient is obtained.

[0057] It should be noted that, since a large number of patients have been undergoing treatment in the hospital for a long time, and the physiological data of each dimension are abnormal only in the corresponding symptoms, that is, they have large deviations. Therefore, the physiological data of a large number of patients in the same dimension have a certain reference value, that is, they contain a large amount of normal physiological data and a small amount of abnormal physiological data. For the physiological data of the target patients to be analyzed, cluster analysis is performed on them with a large amount of physiological data of the same dimension, and then deviation analysis is performed on the single-dimensional data based on the clustering results. Since the number of abnormal physiological data is much smaller than the normal number, and there will be large deviations in the values, the degree of physiological deviation is analyzed and quantified based on this.

[0058] Preferably, in one embodiment of the present invention, cluster analysis is performed on physiological data of the same dimension in medical nursing data of different patients to obtain several clusters in the clustering results, including the following specific methods:

[0059] Take any patient as the target patient and analyze the target patient's blood oxygen dimension as an example, that is, the target patient's blood oxygen saturation. The physiological data of the target patient's blood oxygen dimension is the target patient's blood oxygen data; DBSCAN clustering is performed on all the target patient's blood oxygen data and all the blood oxygen data of a large number of patients obtained. The distance measurement uses the absolute value of the difference between the blood oxygen data to obtain the clustering results and several clusters therein.

[0060] Preferably, in one embodiment of the present invention, based on the clusters of the physiological data and the clustering results, the physiological deviation degree of each physiological data of each dimension of each patient is obtained, including the specific method of:

[0061] Obtain the number of blood oxygen data in each cluster of the blood oxygen dimension; take the cluster with the largest number of blood oxygen data among all clusters of the blood oxygen dimension as the reference cluster of the blood oxygen dimension; then the overall deviation factor of any cluster in the blood oxygen dimension is The calculation method is:

[0062]

[0063] in, Indicates the number of blood oxygen data in this cluster, Indicates the number of blood oxygen data in the reference cluster of the blood oxygen dimension, Represents the mean of all blood oxygen data in this cluster, Represents the mean of all blood oxygen data in the reference cluster of the blood oxygen dimension, Indicates the maximum absolute value of the difference between the blood oxygen data in the blood oxygen dimension, that is, the absolute value of the difference between any two blood oxygen data in the blood oxygen dimension is obtained, and the maximum value is obtained among all the absolute values of the difference.

[0064] It should be noted that in the cluster analysis of blood oxygen data in the blood oxygen dimension, the reference cluster contains the most blood oxygen data and is more likely to be a cluster containing a large amount of normal blood oxygen data. The greater the difference in the number of blood oxygen data between other clusters and the reference cluster, and the greater the difference between the mean values of blood oxygen data, the more likely it is that the cluster contains some abnormal blood oxygen data, and the larger the overall deviation factor of the corresponding cluster is, which is used to reflect the possibility that the cluster is a cluster of abnormal blood oxygen data.

[0065] Furthermore, for any blood oxygen data of the target patient, the absolute value of the difference between the blood oxygen data and the blood oxygen data corresponding to the center point of the cluster in which it is located is obtained, and the absolute value of the difference is obtained. The product of the ratio and the overall deviation factor of the cluster to which the blood oxygen data belongs is used as the physiological deviation degree of the blood oxygen data of the target patient; the physiological deviation degree of each blood oxygen data of each patient's blood oxygen dimension is obtained according to the above method; similarly, the physiological deviation degree of each physiological data of each dimension of each patient is obtained.

[0066] Preferably, in one embodiment of the present invention, the deviation amount is obtained for all physiological data of the same dimension of the same patient, and the difference between the time-series adjacent data of the deviation amount is analyzed, and the physiological deviation degree is corrected to obtain the physiological difference degree of each physiological data of each dimension of each patient. The specific method includes:

[0067] It should be noted that due to differences in physical fitness between different patients, there are large differences between the physiological data corresponding to the same physiological indicators in different patients, even if they are all normal. Therefore, it is necessary to perform analysis based on the physiological data of the corresponding dimensions, and the reference clusters of each dimension can reflect a large amount of normal physiological data of the corresponding dimensions; for the target patient to be analyzed, if the distribution range of the deviation between the physiological data of the target patient in this dimension and the physiological data of the reference cluster is smaller, and the distribution is more concentrated, it indicates that the deviation of the overall physiological data of the target patient tends to be consistent, which may not be due to abnormal physiological data of the target patient, but deviation caused by the physical fitness itself.

[0068] Specifically, taking the blood oxygen dimension as an example, the mean of all blood oxygen data in the reference cluster of the blood oxygen dimension is used as the standard blood oxygen data of the blood oxygen dimension; the difference between any blood oxygen data of the target patient and the standard blood oxygen data is obtained as the blood oxygen offset of the target patient's blood oxygen data, and the blood oxygen offset of each blood oxygen data of the target patient is obtained; then the overall deviation consistency of the target patient's blood oxygen dimension is obtained. The calculation method is:

[0069]

[0070] in, Represents the variance of the blood oxygen offset of all blood oxygen data of the target patient, Indicates the number of blood oxygen data of the target patient, Indicates the target patient The blood oxygen offset of each blood oxygen data, Represents the mean of all blood oxygen offsets of all blood oxygen data of the target patient, represents the standard deviation of all blood oxygen offsets of all blood oxygen data of the target patient; represents the absolute value function, Represents an exponential function with a natural constant as the base. In this embodiment, Model to present inverse proportional relationship and normalization processing, As the input of the model, the implementer can set the inverse proportional function and normalization function according to the actual situation.

[0071] It should be noted that the variance of the blood oxygen offset reflects the overall distribution range of the deviation of the target patient's blood oxygen data compared with the standard blood oxygen data. The smaller the variance, the smaller the distribution range, and the more consistent the overall deviation. At the same time, the kurtosis of the blood oxygen offset is calculated. The greater the kurtosis, the more concentrated the blood oxygen offset, further indicating that the overall deviation tends to be consistent, and the greater the consistency of the overall deviation of the blood oxygen dimension.

[0072] It should be further explained that, in terms of overall deviation consistency, it is necessary to analyze a single blood oxygen data. The greater its impact on the variance, that is, the greater the difference in the variance of the blood oxygen offset before and after removing the blood oxygen data, the less consistent the blood oxygen data is with the overall consistent deviation, and it is more likely that a larger deviation is caused by a local abnormality. By analyzing the changing trend of the blood oxygen offset with the adjacent blood oxygen data, it is further quantified as a deviation caused by a real local abnormality, rather than a single deviation occurring during the data acquisition process, that is, the abnormality of the physiological data will last for a period of time, and will not mutate at a single moment and then immediately return to normal.

[0073] Specifically, for any blood oxygen data of the target patient, the blood oxygen offset of the blood oxygen data is removed, and the variance of the blood oxygen offsets of other blood oxygen data of the target patient is recalculated and used as the decentered offset variance of the blood oxygen data; the difference between the variance of the blood oxygen offset of all blood oxygen data of the target patient and the decentered offset variance of the blood oxygen data is used as the variance deviation factor of the blood oxygen data; the variance deviation factors of all blood oxygen data of the target patient are linearly normalized, and the result obtained is used as the variance deviation of each blood oxygen data of the target patient.

[0074] Furthermore, a neighborhood range is constructed. In this embodiment, the neighborhood range is described using 3, and the target patient is The left and right sides of the blood oxygen data are 3 blood oxygen data, as the target patient's The neighborhood range of the blood oxygen data; In particular, if there are less than 3 blood oxygen data on the left or right side of the blood oxygen data, which is insufficient to constitute the neighborhood range, the neighborhood range will be constructed based on the actual blood oxygen data; then the target patient’s Local deviation factor of blood oxygen data The calculation method is:

[0075]

[0076] in, Indicates the target patient The variance deviation of blood oxygen data, Indicates the target patient The number of blood oxygen data in the neighborhood of blood oxygen data, Indicates the target patient The blood oxygen offset of each blood oxygen data, Indicates the target patient The blood oxygen data is within the neighborhood of The blood oxygen offset of each blood oxygen data, Indicates the maximum blood oxygen offset value among all blood oxygen data of the target patient.

[0077] It should be noted that the greater the variance deviation of the blood oxygen data, the more consistent it is with the overall consistent deviation of the patient, and the more likely it is that a larger degree of deviation will occur locally or individually compared to the standard blood oxygen data; and by performing a difference analysis with the blood oxygen offset of adjacent blood oxygen data, the smaller the difference, the greater the possibility that the blood oxygen data has continuously deviated significantly over a period of time, and the greater the possibility that a larger deviation will occur locally during that period, rather than a single deviation caused by the data acquisition process.

[0078] Furthermore, the target patient The physiological difference of blood oxygen data The calculation method is:

[0079]

[0080] in, Indicates the target patient The physiological difference of blood oxygen data, Indicates the target patient The local deviation factor of blood oxygen data, Indicates the target patient The physiological deviation degree of blood oxygen data, Indicates the overall deviation consistency of the target patient's blood oxygen dimension; according to the above method, the physiological difference degree of each dimension of the physiological data of each patient is obtained.

[0081] At this point, by performing cluster analysis on the physiological data of the same dimension of different patients based on the fact that they contain a large amount of normal physiological data, the deviation of the clusters and the deviation of the physiological data within the clusters are quantified respectively, and the possibility of abnormal physiological data is preliminarily quantified; then, by performing an overall deviation analysis on the physiological data of the same dimension of the same patient, the physiological data deviation caused by the difference in the physical fitness of the patients is avoided to affect the abnormal analysis of the physiological data. At the same time, the possibility of actual abnormality of the physiological data of the patient in the local time is considered, so as to correct the degree of physiological difference in the obtained physiological data.

[0082] Step S003: determine the degree of physiological difference and obtain a number of abnormal physiological data of each dimension for each patient; obtain the priority care degree of each dimension for each patient based on the distribution of abnormal physiological data of the same dimension of the same patient and the degree of physiological difference of all physiological data; and obtain the treatment priority of each patient in combination with the degree of physiological difference of physiological data of each dimension of the same patient at the current moment.

[0083] Preferably, in one embodiment of the present invention, the specific method for determining the degree of physiological difference and obtaining a number of abnormal physiological data of each patient in each dimension is as follows:

[0084] Taking the blood oxygen dimension as an example, an abnormal threshold is preset. In this embodiment, the abnormal threshold is described as 0.6. For any blood oxygen data of the target patient, if the physiological difference degree of the blood oxygen data is greater than or equal to the abnormal threshold, the blood oxygen data is recorded as abnormal blood oxygen data of the target patient. Several abnormal blood oxygen data of the target patient are obtained. According to the above method, several abnormal physiological data of each dimension of each patient are obtained.

[0085] Preferably, in one embodiment of the present invention, based on the distribution of abnormal physiological data of the same dimension of the same patient and the degree of physiological difference of all physiological data, the priority care level of each patient in each dimension is obtained, including the specific method of:

[0086] It should be noted that after obtaining abnormal physiological data, the physiological data of the corresponding dimension of the same patient are segmented according to the abnormal physiological data, and the change in the time length of the segment can reflect the rehabilitation effect of the corresponding dimension. The gradually longer the segment, the smaller the possibility of recurrence of abnormal physiological data in the corresponding dimension, and the better the rehabilitation effect. At the same time, the degree of physiological difference of the physiological data within the segment is analyzed. The smaller the overall physiological difference, the better the recovery of the physiological data of the corresponding dimension within the segment; and the degree of physiological difference of the physiological data of the most recent segment is used to further quantify the rehabilitation effect of the corresponding dimension.

[0087] Specifically, taking the blood oxygen dimension of the target patient as an example, after obtaining several abnormal blood oxygen data of the target patient, the blood oxygen data of the target patient other than the abnormal blood oxygen data is recorded as non-abnormal blood oxygen data; the non-abnormal blood oxygen data of the target patient that is continuous in time series is regarded as a blood oxygen recovery segment of the target patient. It should be noted that discontinuous single non-abnormal blood oxygen data is also regarded as a blood oxygen recovery segment; and several blood oxygen recovery segments of the target patient are obtained.

[0088] Furthermore, for the target patient Blood oxygen recovery stage, its physiological recovery level The calculation method is:

[0089]

[0090] in, Indicates the target patient The number of non-abnormal blood oxygen data in the blood oxygen recovery segment, Indicates the target patient The number of non-abnormal blood oxygen data in the blood oxygen recovery segment, Indicates the target patient The blood oxygen recovery stage and the The number of consecutive abnormal blood oxygen data between blood oxygen recovery segments, Indicates the target patient The average of the physiological difference degrees of all non-abnormal blood oxygen data in the blood oxygen rehabilitation segment; In particular, the physiological recovery degree of the target patient in the first blood oxygen rehabilitation segment is The results are expressed as follows, where It represents the mean of the physiological difference degree of all non-abnormal blood oxygen data in the first blood oxygen rehabilitation segment of the target patient.

[0091] It should be noted that the more non-abnormal blood oxygen data added compared to the previous blood oxygen rehabilitation segment, the longer the recurrence hiding time of the corresponding blood oxygen rehabilitation segment. At the same time, the fewer the number of abnormal blood oxygen data between the two blood oxygen rehabilitation segments, the less serious the abnormal recurrence in the blood oxygen dimension, and the better the rehabilitation effect; and the smaller the physiological difference of non-abnormal blood oxygen data within the blood oxygen rehabilitation segment, the better the local rehabilitation effect.

[0092] Furthermore, the number of non-abnormal blood oxygen data in all blood oxygen rehabilitation segments of the target patient is weighted and normalized, and the result is used as the rehabilitation weight of each blood oxygen rehabilitation segment of the target patient; then the priority care degree of the target patient's blood oxygen dimension is The calculation method is:

[0093]

[0094] in, It represents the mean value of the physiological difference of all non-abnormal blood oxygen data in the last blood oxygen rehabilitation segment of the target patient. Indicates the number of non-abnormal blood oxygen data in the last blood oxygen recovery segment of the target patient. Indicates the number of blood oxygen recovery segments of the target patient, Indicates the target patient The recovery weight of each blood oxygen recovery segment, Indicates the target patient Physiological recovery level of each blood oxygen recovery segment; Represents an exponential function with a natural constant as the base. In this embodiment, Model to present inverse proportional relationship and normalization processing, As the input of the model, the implementer can set the inverse proportional function and normalization function according to the actual situation.

[0095] It should be noted that the greater the mean value of the physiological difference in the most recent blood oxygen rehabilitation segment and the smaller the number of non-abnormal blood oxygen data, the more the non-abnormal blood oxygen data can reflect the length of time without relapse of the disease in the blood oxygen dimension. The more serious the recent relapse of the disease in the blood oxygen dimension, the worse the rehabilitation effect, and the priority care should be given. At the same time, the number of non-abnormal blood oxygen data in each blood oxygen rehabilitation segment, that is, the length of time without relapse, is used as the weight to quantify the physiological rehabilitation degree of the blood oxygen rehabilitation segment as a whole. The greater the physiological rehabilitation degree and the longer the length of time without relapse of the blood oxygen rehabilitation segment, the greater its proportion in the quantification process, that is, the more it can reflect the rehabilitation effect. The smaller the overall quantification result, the greater the rehabilitation effect, the more priority care is needed, and the greater the priority care degree of the corresponding dimension.

[0096] Preferably, in one embodiment of the present invention, the treatment priority of each patient is obtained by combining the physiological difference degree of each dimension of the physiological data of the same patient at the current moment, including the specific method of:

[0097] It should be noted that the nursing priority of a single dimension of a patient reflects the rehabilitation effect of the physiological indicators of the corresponding dimension, while for the overall treatment priority of the patient, it is necessary to conduct an overall analysis of the rehabilitation effect of all physiological indicators. On this basis, it is necessary to combine the physiological differences of the physiological data of each dimension of the patient at the current moment. The greater the physiological differences of the physiological data of the corresponding dimension at the current moment, the more likely the corresponding dimension is to be abnormal at the current moment, and timely nursing adjustment is required; and the more physiological indicators that require timely nursing adjustment, the higher the treatment priority the patient should have.

[0098] Specifically, the degree of physiological difference of the physiological data of each dimension of the target patient at the current moment is used as the weight of the priority treatment of each dimension of the target patient, and the priority treatment of all dimensions of the target patient is weighted and averaged, and the result is used as the treatment priority of the target patient; the treatment priority of each patient is obtained according to the above method.

[0099] At this point, abnormal physiological data are judged based on the degree of physiological differences, and the rehabilitation effect of the patient's corresponding dimension is quantified through the distribution of abnormal physiological data in the same dimension of the patient and the changes in the degree of physiological differences of other non-abnormal physiological data. This reflects whether the symptoms in the corresponding dimension of the patient relapse and the severity of the relapse, and obtains the priority care of the patient in each dimension; further overall analysis of the priority care of each dimension is performed to comprehensively quantify the patient's treatment priority.

[0100] Step S004: Process the patient's medical and nursing data according to the treatment priority of each patient.

[0101] Specifically, after obtaining the processing priorities of all patients based on their medical and nursing data since admission, all patients currently being treated in the hospital are arranged in descending order of processing priority from large to small to obtain a patient priority processing sequence; the patients' medical and nursing data are processed and analyzed in the order of the patient priority processing sequence, and after processing and analysis, the corresponding patients are cared for, thereby completing the processing of the medical and nursing data of the patients being treated in the hospital.

[0102] At this point, by performing cluster analysis on the physiological data of various dimensions of a large number of patients, quantifying the degree of physiological deviation through the distribution of physiological data in the clustering results, and further analyzing the changes in physiological data of the same dimension of the same patient, the degree of physiological difference is corrected, and the rehabilitation effect of each dimension of the patient is quantified based on the degree of physiological difference. Ultimately, the medical nursing data of patients with poor rehabilitation effects are given priority processing and corresponding care, thereby improving the efficiency of medical nursing data processing and the applicability of use in hospital databases.

[0103] Another embodiment of the present invention provides a medical nursing data processing system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, steps S001 to S004 of the above method are implemented.

[0104] Another embodiment of the present invention provides a medical nursing data processing electronic device, the device comprising:

[0105] Medical nursing data acquisition module: acquires medical nursing data of several patients;

[0106] Medical nursing data analysis module: cluster analysis is performed on physiological data of the same dimension in the medical nursing data of different patients to obtain several clusters in the clustering results; based on the clusters in which the physiological data are located and the clustering results, the physiological deviation degree of each physiological data in each dimension of each patient is obtained; the deviation amount is obtained for all physiological data of the same dimension of the same patient, and the difference between the time-series adjacent data of the deviation amount is analyzed, the physiological deviation degree is corrected, and the physiological difference degree of each physiological data in each dimension of each patient is obtained;

[0107] Determine the degree of physiological difference to obtain a number of abnormal physiological data in each dimension of each patient; obtain the priority care degree in each dimension of each patient based on the distribution of abnormal physiological data in the same dimension of the same patient and the degree of physiological difference of all physiological data; and obtain the treatment priority of each patient by combining the degree of physiological deviation of each dimension of the physiological data of the same patient at the current moment;

[0108] Medical nursing data processing module: processes the patient's medical nursing data according to the treatment priority of each patient.

[0109] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A medical nursing data processing method, characterized in that: The method comprises the following steps: Acquiring medical nursing data of a plurality of patients, wherein the medical nursing data includes multi-dimensional physiological data; Cluster analysis is performed on physiological data of the same dimension in medical nursing data of different patients to obtain several clusters in the clustering results; based on the clusters in which the physiological data are located and the clustering results, the physiological deviation degree of each physiological data in each dimension of each patient is obtained; the deviation amount is obtained for all physiological data of the same dimension of the same patient, and the difference between adjacent time series data of the deviation amount is analyzed to correct the physiological deviation degree to obtain the physiological difference degree of each physiological data in each dimension of each patient; Determine the degree of physiological difference to obtain a number of abnormal physiological data in each dimension of each patient; obtain the priority care degree in each dimension of each patient based on the distribution of abnormal physiological data in the same dimension of the same patient and the degree of physiological difference of all physiological data; and obtain the treatment priority of each patient by combining the degree of physiological difference of each dimension of the physiological data of the same patient at the current moment; Process the patient's medical and nursing data according to each patient's treatment priority; The specific method for obtaining the physiological deviation degree of each physiological data of each dimension of each patient is as follows: Obtain the number of blood oxygen data in each cluster of the blood oxygen dimension; take the cluster with the largest number of blood oxygen data among all clusters of the blood oxygen dimension as the reference cluster of the blood oxygen dimension; the overall deviation factor of any cluster in the blood oxygen dimension The calculation method is: in, Indicates the number of blood oxygen data in this cluster, Indicates the number of blood oxygen data in the reference cluster of the blood oxygen dimension, Represents the mean of all blood oxygen data in this cluster, Represents the mean of all blood oxygen data in the reference cluster of the blood oxygen dimension, Indicates the maximum absolute value of the difference between the blood oxygen data in the blood oxygen dimension; For any blood oxygen data of the target patient, obtain the absolute value of the difference between the blood oxygen data and the blood oxygen data corresponding to the center point of the cluster where it is located, and obtain the absolute value of the difference and The product of the ratio and the overall deviation factor of the cluster to which the blood oxygen data belongs is taken as the physiological deviation degree of the blood oxygen data of the target patient.

2. A medical nursing data processing method according to claim 1, characterized in that: The cluster analysis of physiological data of the same dimension in the medical nursing data of different patients is performed to obtain several clusters in the clustering results, including the following specific methods: Any patient is taken as the target patient, and the physiological data of the target patient's blood oxygen dimension is taken as the target patient's blood oxygen data; DBSCAN clustering is performed on all the target patient's blood oxygen data and all the blood oxygen data of a large number of patients obtained. The distance measurement uses the absolute value of the difference between the blood oxygen data to obtain the clustering results and several clusters therein.

3. A medical nursing data processing method according to claim 1, characterized in that: The method of obtaining the deviation of all physiological data of the same dimension of the same patient, analyzing the difference of adjacent temporal data of the deviation, correcting the physiological deviation degree, and obtaining the physiological difference degree of each physiological data of each dimension of each patient includes the following specific methods: The mean of all blood oxygen data in the reference cluster of the blood oxygen dimension is used as the standard blood oxygen data of the blood oxygen dimension; the difference between any blood oxygen data of the target patient and the standard blood oxygen data is obtained as the blood oxygen offset of the target patient's blood oxygen data; the overall deviation consistency of the target patient's blood oxygen dimension is obtained. The calculation method is: in, Represents the variance of the blood oxygen offset of all blood oxygen data of the target patient, Indicates the number of blood oxygen data of the target patient, Indicates the target patient The blood oxygen offset of each blood oxygen data, Represents the mean of all blood oxygen offsets of all blood oxygen data of the target patient, represents the standard deviation of all blood oxygen offsets of all blood oxygen data of the target patient; represents the absolute value function, represents an exponential function with a natural constant as its base; According to the distribution range of the blood oxygen offset of the target patient's blood oxygen data and the difference in the blood oxygen offset of adjacent blood oxygen data, a local deviation factor of each blood oxygen data of the target patient is obtained; Target patients The physiological difference of blood oxygen data The calculation method is: in, Indicates the target patient The local deviation factor of blood oxygen data, Indicates the target patient The physiological deviation degree of blood oxygen data, Indicates the overall deviation consistency of the target patient's blood oxygen dimension.

4. A medical nursing data processing method according to claim 3, characterized in that: The method of obtaining the local deviation factor of each blood oxygen data of the target patient according to the distribution range of the blood oxygen offset of the blood oxygen data of the target patient and the blood oxygen offset difference of adjacent blood oxygen data includes the following specific methods: For any blood oxygen data of the target patient, the blood oxygen offset of the blood oxygen data is removed, and the variance of the blood oxygen offset of other blood oxygen data of the target patient is recalculated and used as the denoted offset variance of the blood oxygen data; The difference between the variance of the blood oxygen offset of all the blood oxygen data of the target patient and the decentered offset variance of the blood oxygen data is used as the variance deviation factor of the blood oxygen data; Linearly normalize the variance deviation factor of all blood oxygen data of the target patient, and use the obtained result as the variance deviation of each blood oxygen data of the target patient; Construct target patient The neighborhood range of blood oxygen data of the target patient Local deviation factor of blood oxygen data The calculation method is: in, Indicates the target patient The variance deviation of blood oxygen data, Indicates the target patient The number of blood oxygen data in the neighborhood of blood oxygen data, Indicates the target patient The blood oxygen offset of each blood oxygen data, Indicates the target patient The blood oxygen data is within the neighborhood of The blood oxygen offset of each blood oxygen data, Indicates the maximum blood oxygen offset value among all blood oxygen data of the target patient.

5. A medical nursing data processing method according to claim 2, characterized in that: The method of obtaining the priority care degree of each dimension of each patient based on the distribution of abnormal physiological data of the same dimension of the same patient and the physiological difference degree of all physiological data includes the following specific methods: Recording the target patient's blood oxygen data other than the abnormal blood oxygen data as non-abnormal blood oxygen data; and treating the target patient's continuous non-abnormal blood oxygen data in time series as a blood oxygen recovery segment of the target patient; Compare the number of non-abnormal blood oxygen data in adjacent blood oxygen recovery segments, combine the abnormal blood oxygen data between adjacent blood oxygen recovery segments, and the physiological difference between the non-abnormal blood oxygen data in the blood oxygen recovery segments, and obtain the physiological recovery degree of each blood oxygen recovery segment of the target patient; The number of non-abnormal blood oxygen data in all blood oxygen rehabilitation segments of the target patient is weighted and normalized, and the result is used as the rehabilitation weight of each blood oxygen rehabilitation segment of the target patient; the priority care degree of the target patient's blood oxygen dimension The calculation method is: in, It represents the mean value of the physiological difference of all non-abnormal blood oxygen data in the last blood oxygen rehabilitation segment of the target patient. Indicates the number of non-abnormal blood oxygen data in the last blood oxygen recovery segment of the target patient. Indicates the number of blood oxygen recovery segments of the target patient, Indicates the target patient The recovery weight of each blood oxygen recovery segment, Indicates the target patient Physiological recovery level of each blood oxygen recovery segment; Represents an exponential function with a natural constant as its base.

6. A medical nursing data processing method according to claim 5, characterized in that: The method of comparing the number of non-abnormal blood oxygen data in adjacent blood oxygen rehabilitation segments, combining the abnormal blood oxygen data between adjacent blood oxygen rehabilitation segments and the physiological difference degree of the non-abnormal blood oxygen data in the blood oxygen rehabilitation segments, and obtaining the physiological recovery degree of each blood oxygen rehabilitation segment of the target patient includes the following specific methods: in, Indicates the target patient The number of non-abnormal blood oxygen data in the blood oxygen recovery segment, Indicates the target patient The number of non-abnormal blood oxygen data in the blood oxygen recovery segment, Indicates the target patient The blood oxygen recovery stage and the The number of consecutive abnormal blood oxygen data between blood oxygen recovery segments, Indicates the target patient The mean of the physiological difference levels of all non-abnormal blood oxygen data in the blood oxygen recovery segment.

7. A medical nursing data processing method according to claim 2, characterized in that: The specific method of obtaining the treatment priority of each patient includes: The physiological difference degree of each dimension of the physiological data of the target patient at the current moment is used as the weight of the priority treatment degree of each dimension of the target patient. The priority treatment degrees of all dimensions of the target patient are weighted and averaged, and the result is used as the treatment priority of the target patient.

8. A medical nursing data processing system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the medical nursing data processing method according to any one of claims 1 to 7 are implemented.

9. An electronic device for processing medical nursing data, the device comprising: A medical and nursing data acquisition module is used to acquire medical and nursing data of several patients; The medical nursing data analysis module is used to perform cluster analysis on physiological data of the same dimension in the medical nursing data of different patients to obtain a number of clusters in the clustering results; based on the clusters in which the physiological data are located and the clustering results, obtain the physiological deviation degree of each physiological data in each dimension of each patient; obtain the deviation amount for all physiological data of the same dimension of the same patient, analyze the difference between adjacent data in the time series of the deviation amount, correct the physiological deviation degree, and obtain the physiological difference degree of each physiological data in each dimension of each patient; Determining the degree of the physiological difference and obtaining a number of abnormal physiological data of each patient in each dimension; Based on the distribution of abnormal physiological data of the same dimension of the same patient and the degree of physiological difference of all physiological data, the priority of care in each dimension of each patient is obtained; combined with the degree of physiological difference of physiological data of each dimension of the same patient at the current moment, the treatment priority of each patient is obtained; A medical nursing data processing module is used to process the medical nursing data of patients according to the treatment priority of each patient; The medical nursing data analysis module is specifically used to obtain the number of blood oxygen data in each cluster of the blood oxygen dimension; the cluster with the largest number of blood oxygen data among all clusters of the blood oxygen dimension is used as the reference cluster of the blood oxygen dimension; the overall deviation factor of any cluster of the blood oxygen dimension The calculation method is: in, Indicates the number of blood oxygen data in this cluster, Indicates the number of blood oxygen data in the reference cluster of the blood oxygen dimension, Represents the mean of all blood oxygen data in this cluster, Represents the mean of all blood oxygen data in the reference cluster of the blood oxygen dimension, Indicates the maximum absolute value of the difference between the blood oxygen data in the blood oxygen dimension; For any blood oxygen data of the target patient, obtain the absolute value of the difference between the blood oxygen data and the blood oxygen data corresponding to the center point of the cluster where it is located, and obtain the absolute value of the difference and The product of the ratio and the overall deviation factor of the cluster to which the blood oxygen data belongs is taken as the physiological deviation degree of the blood oxygen data of the target patient.

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