Disclosure of Invention
In order to solve the technical problem of inaccurate auxiliary selection of a nursing scheme in the prior art, the invention aims to provide an intelligent nursing monitoring system and an automatic analysis method for patient health data of the intelligent nursing monitoring system, and the adopted technical scheme is as follows:
A method of automatic analysis of patient health data for an intelligent care monitoring system, the method comprising:
Acquiring physiological data sets of a patient to be nursed and each reference patient and nursing schemes of each reference patient, wherein the physiological data sets comprise time sequence data of all the physiological sign indexes corresponding to monitoring values before and after an operation;
The method comprises the steps of taking any physiological sign index as a target sign index, obtaining disease influence metrics of the target sign index of a patient to be analyzed according to time sequence data of monitoring values corresponding to the target sign index and the physiological sign index of the patient to be analyzed before and after an operation, synthesizing the disease influence metrics corresponding to the target sign indexes of all reference patients, obtaining disease comprehensive influence metrics of the target sign index, obtaining condition difference metrics of the target sign indexes of the patient to be nursed and the reference patient according to difference conditions of the disease influence metrics of the target sign indexes of the patient to be nursed and the reference patient, obtaining physiological difference indexes of the patient to be nursed and the reference patient according to difference conditions of time sequence data corresponding to the monitoring values of each physiological sign index of the patient to be nursed and the reference patient after the operation and the physiological difference metrics corresponding to the disease comprehensive influence metrics and the condition difference metrics of the physiological sign indexes;
And selecting the nursing scheme of the patient to be nursed according to the physiological difference indexes of the patient to be nursed and the reference patient and the nursing scheme of the reference patient.
Further, the method for obtaining the disease influence metric comprises the following steps:
Acquiring the correlation coefficient of each two monitoring value time sequence data according to the correlation condition of each two monitoring value time sequence data;
Taking each physiological sign index except the target sign index as each reference sign index of the target sign index;
Taking the correlation coefficient of the target physical sign index and the reference physical sign index of the patient to be analyzed, which correspond to the time sequence data of the monitoring value before operation, as the preoperation correlation parameter of the target physical sign index and the reference physical sign index of the patient to be analyzed;
taking the correlation coefficient of the target physical sign index and the reference physical sign index of the patient to be analyzed, which correspond to the time sequence data of the monitoring value after the operation, as the postoperative correlation parameter of the target physical sign index and the reference physical sign index of the patient to be analyzed;
Calculating the absolute value of the difference between the preoperative related parameter and the postoperative related parameter to obtain the target sign index and the reference sign index of the patient to be analyzed;
and calculating the average value of the target physical sign index and the operation influence parameters of all the reference physical sign indexes to obtain the disease influence measurement of the target physical sign index of the patient to be analyzed.
Further, the method for obtaining the correlation coefficient includes:
and calculating absolute values of the pearson correlation coefficients of the time sequence data of each two monitoring values to obtain the correlation coefficients of the time sequence data of each two monitoring values.
Further, the method for obtaining the comprehensive disease influence measure comprises the following steps:
and calculating the average value of the disease influence metrics corresponding to the target sign indexes of all the reference patients to obtain the disease comprehensive influence metrics of the target sign indexes.
Further, the method for obtaining the condition difference measure comprises the following steps:
and calculating the absolute value of the difference value of the disease influence metrics of the target sign indexes of the patient to be nursed and the reference patient to obtain the condition difference metric of the target sign indexes of the patient to be nursed and the reference patient.
Further, the method for obtaining the physiological difference index comprises the following steps:
according to the difference condition of time sequence data of the corresponding monitoring values of each physiological sign index of the patient to be nursed and the reference patient after the operation, acquiring postoperative detection difference values of each physiological sign index of the patient to be nursed and the reference patient;
The physiological sign indexes are forward fused to obtain the adjustment weight of each physiological sign index of the patient to be nursed and the reference patient, wherein the physiological sign indexes correspond to the comprehensive disease influence measurement and the condition difference measurement;
And carrying out weighted summation on the postoperative detection difference value by utilizing the adjustment weight of each physiological sign index of the patient to be nursed and the reference patient to obtain the physiological difference index of the patient to be nursed and the reference patient.
Further, the method for acquiring the post-operation detection difference value comprises the following steps:
and taking the DTW distance of each physiological sign index of the patient to be nursed and the reference patient corresponding to the time sequence data of the monitoring value after the operation as the postoperative detection difference value of each physiological sign index of the patient to be nursed and the reference patient.
Further, the method for acquiring the adjustment weight comprises the following steps:
and calculating the product of the disease comprehensive influence measurement and the condition difference measurement corresponding to the physiological sign indexes, and carrying out normalization processing to obtain the adjustment weight of each physiological sign index of the patient to be nursed and the reference patient.
Further, the method of selecting a regimen of a patient to be cared for comprises:
For each nursing scheme, the nursing scheme is corresponding to all reference patients and is used as nursing patients of the nursing scheme, and the average value of the physiological difference indexes of the patient to be nursing and all nursing patients is calculated and mapped in a negative correlation way to obtain the nursing matching measurement of the nursing scheme;
The largest care matching metric corresponds to the care plan as the care plan for the patient to be cared for.
The invention provides an intelligent nursing monitoring system, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the automatic analysis method of the health data of a patient of the intelligent nursing monitoring system when executing the computer program.
The invention has the following beneficial effects:
Firstly, the influence degree of diseases on target sign indexes of patients to be analyzed is reflected through disease influence metrics, then the disease comprehensive influence metrics of the target sign indexes of all reference patients are synthesized according to the disease influence metrics corresponding to the target sign indexes of the reference patients, the disease comprehensive influence metrics reflect the influence degree of the target sign indexes commonly subjected to the diseases in the reference patient group, and the disease state difference degree of the patients to be nursed and the reference patients on the target sign indexes is reflected by using the condition difference metrics. The method comprises the steps of integrating the difference condition of time sequence data of each physiological sign index corresponding to a monitoring value after operation of a patient to be nursed and a reference patient, and the physiological sign index corresponding to a comprehensive disease influence metric and a condition difference metric to obtain the physiological difference index of the patient to be nursed and the reference patient, wherein the physiological difference index is an integrated evaluation index used for more accurately quantifying the physiological condition difference degree between the patient to be nursed and the reference patient. The physiological condition difference degree between the patient to be nursed and the reference patient is estimated more accurately by using the physiological difference index, so that the accuracy of selecting the nursing scheme for the patient to be nursed is improved.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purposes, the following description refers to the specific implementation, structure, characteristics and effects of an intelligent care monitoring system and an automatic analysis method for patient health data according to the present invention, with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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.
The following specifically describes a specific scheme of the intelligent nursing monitoring system and the automatic analysis method of patient health data thereof provided by the invention with reference to the accompanying drawings.
The embodiment of the invention provides an intelligent nursing monitoring system and an automatic analysis method for patient health data thereof, referring to fig. 1, which shows a flow chart of the automatic analysis method for patient health data of the intelligent nursing monitoring system, which comprises the following steps:
step S1, acquiring physiological data sets of a patient to be nursed and each reference patient and nursing schemes of each reference patient, wherein the physiological data sets comprise time sequence data of all physiological sign indexes corresponding to monitoring values before and after an operation.
From the monitoring system, a set of physiological data of the patient to be cared for and of the respective reference patient, and a care plan of the respective reference patient is acquired. The specific acquisition process comprises the following steps:
In order to analyze the actual physiological condition of a patient, the patient wears the intelligent terminal device, and according to the preset sampling frequency, all physiological sign indexes of the patient, including but not limited to heart rate, blood oxygen saturation, blood pressure, body temperature and other physiological sign indexes, are synchronously sampled by utilizing the intelligent terminal device. For any physiological sign index, the invention sequentially counts the corresponding monitoring values of a preset number of sampling moments before operation according to the time sequence to obtain the time sequence data of the corresponding monitoring values of the physiological sign index before operation; according to the time sequence, the invention sequentially counts the monitoring values corresponding to the preset number of sampling moments after the operation to obtain time sequence data of the monitoring values corresponding to the physiological sign indexes after the operation, namely, physiological data sets of the patient to be nursed and each reference patient are obtained, wherein the physiological data sets comprise the time sequence data of all the physiological sign indexes before the operation and after the operation. A plurality of reference patient care regimens are also stored in the monitoring system. These protocols are formulated based on factors such as physiological data, medical history, treatment course, etc. of the reference patient, and can provide valuable references for determining a care protocol for the patient to be cared for. In one embodiment of the present invention, the preset sampling frequency is 1 time/minute, the preset number is 600, and the implementation can be set by the implementation personnel according to the implementation scenario. Wherein, in the invention, all physiological sign indexes of each patient are the same, and each patient corresponds to one nursing scheme.
It should be noted that, in order to facilitate the operation, all index data involved in the operation in the embodiment of the present invention is subjected to data preprocessing, so as to cancel the dimension effect. The specific means for removing the dimension influence is a technical means well known to those skilled in the art, and is not limited herein. The data acquisition in the invention is authorized by the user, does not violate related laws and regulations, and does not violate the popular public order.
Because patient groups with similar physiological sign information after operation often adopt similar nursing schemes, the prior art assists in the selection of the nursing scheme of the patient to be nursed by analyzing the difference condition of all dimension physiological sign indexes corresponding to the time sequence data of the monitoring values after operation of the patient to be nursed and all reference patients. However, in the prior art, when the nursing scheme of the patient to be nursed is assisted, the influence degree of coronary heart disease on physiological sign indexes of different dimensions is ignored, and the same weight is given to the data difference of the physiological sign indexes of all dimensions, so that the physiological difference condition of the patient to be nursed and the reference patient is analyzed inaccurately, and the assisted nursing scheme selection is inaccurate.
Step S2, taking any physiological sign index as a target sign index, acquiring disease influence metrics of the target sign index of the patient to be analyzed according to time sequence data of the target sign index and the physiological sign index of the patient to be analyzed corresponding to the monitoring values before and after the operation, synthesizing the disease influence metrics of the target sign indexes of all reference patients, acquiring the disease comprehensive influence metrics of the target sign index, acquiring the condition difference metrics of the target sign indexes of the patient to be nursed and the reference patient according to the difference conditions of the disease influence metrics of the target sign indexes of the patient to be nursed and the reference patient, and acquiring the physiological difference indexes of the patient to be nursed and the reference patient according to the difference conditions of the time sequence data of the physiological sign indexes of each physiological sign index of the patient to be nursed and the reference patient corresponding to the monitoring values after the operation and the disease comprehensive influence metrics and the condition difference metrics of the physiological sign indexes.
Firstly, the influence degree of diseases on target sign indexes of patients to be analyzed is reflected through disease influence metrics, then the disease comprehensive influence metrics of the target sign indexes of all reference patients are synthesized according to the disease influence metrics corresponding to the target sign indexes of the reference patients, the disease comprehensive influence metrics reflect the influence degree of the target sign indexes commonly subjected to the diseases in the reference patient group, and the disease state difference degree of the patients to be nursed and the reference patients on the target sign indexes is reflected by using the condition difference metrics. The method comprises the steps of integrating the difference condition of time sequence data of each physiological sign index corresponding to a monitoring value after operation of a patient to be nursed and a reference patient, and the physiological sign index corresponding to a comprehensive disease influence metric and a condition difference metric to obtain the physiological difference index of the patient to be nursed and the reference patient, wherein the physiological difference index is an integrated evaluation index used for more accurately quantifying the physiological condition difference degree between the patient to be nursed and the reference patient.
In order to quantify the extent to which the target sign index of the patient to be analyzed is affected by the disease, preferably, in one embodiment of the present invention, the method for obtaining the disease impact metric includes:
Acquiring the correlation coefficient of each two monitoring value time sequence data according to the correlation condition of each two monitoring value time sequence data;
Taking each physiological sign index except the target sign index as each reference sign index of the target sign index;
taking the correlation coefficient of the target physical sign index and the reference physical sign index of the patient to be analyzed, which correspond to the time sequence data of the monitoring value before the operation, as the preoperative correlation parameter of the target physical sign index and the reference physical sign index of the patient to be analyzed;
Taking the correlation coefficient of the time sequence data of the monitoring value corresponding to the target physical sign index and the reference physical sign index of the patient to be analyzed after the operation as the postoperative correlation parameter of the target physical sign index and the reference physical sign index of the patient to be analyzed;
calculating the absolute value of the difference between the preoperative related parameter and the postoperative related parameter to obtain the surgical influence parameters of the target physical sign index and the reference physical sign index of the patient to be analyzed;
and calculating the average value of the surgical influence parameters of the target physical sign index and all the reference physical sign indexes to obtain the disease influence measurement of the target physical sign index of the patient to be analyzed. The method for acquiring the correlation coefficient in one embodiment of the invention comprises the steps of calculating absolute values of pearson correlation coefficients of time sequence data of every two monitoring values to obtain the correlation coefficient of the time sequence data of every two monitoring values. It should be noted that, the method for obtaining the pearson correlation coefficient is known to those skilled in the art, and is not described herein in detail, and the patient to be analyzed in the present invention may be a patient to be treated or a reference patient.
As an example, the acquisition formula of the disease impact metric in one embodiment of the present invention includes:
Wherein, the method comprises the steps of,A disease impact metric representing a target sign indicator of a patient to be analyzed; target sign index and the first of the patient to be analyzedPreoperative relevant parameters of the individual reference sign indicators; target sign index and the first of the patient to be analyzedPostoperative related parameters of the individual reference sign indicators; target sign index and the first of the patient to be analyzedSurgical influencing parameters of the individual reference sign indicators; The total number of all the reference sign indexes is corresponding to the target sign index; is an absolute value sign.
For the above steps, in order to analyze the specific influence of the disease on the target physical sign index of the patient to be analyzed, the correlation coefficient is first used to measure the linear correlation between each two monitoring values and time series data. This step is critical in understanding how the disease affects the interactions between different physiological signs. For example, in the case of a coronary heart disease episode, a decrease in heart blood supply may directly lead to a decrease in blood oxygen physiological signs, whereas in order to cope with this change, the human body may increase the heart's blood supply by increasing the heart rate. This physiological response reflects the correlation between different physiological signs. For in-depth analysis of the change situation of the target physical sign index, all physical sign indexes except the target physical sign index are regarded as reference physical sign indexes. This setting helps to build a comprehensive frame of reference. And calculating the correlation coefficient between the target sign index before and after the operation and each reference sign index as the correlation parameters before and after the operation. This step is critical to reveal changes in correlation between pre-operative and post-operative physiological signs, as it can reflect the specific impact of the disease on the patient's physiological state. In order to quantify the influence of the surgery on the correlation between the target sign index and the reference sign index, the absolute value of the difference value of the correlation parameters before and after the surgery is further calculated, and the surgery influence parameters are obtained. The index can intuitively reflect the degree of change of the correlation between the target sign index and different reference sign indexes during the operation. Finally, calculating the average value of the surgical influence parameters of the target physical sign index and all the reference physical sign indexes, wherein the average value is used as a disease influence measure of the target physical sign index of the patient to be analyzed, the disease influence measure can comprehensively reflect the overall influence degree of the target physical sign index of the patient to be analyzed on the disease, and the larger the disease influence measure is, the larger the overall influence degree of the target physical sign index of the patient to be analyzed on the disease is represented.
In order to quantify the extent to which the target sign index is affected by the disease generally in the reference patient population, preferably, in one embodiment of the present invention, the method for obtaining the disease integrated influence metric includes:
and calculating the average value of the disease influence metrics corresponding to the target sign indexes of all the reference patients to obtain the disease comprehensive influence metrics of the target sign indexes.
For the above steps, in order to quantify the degree of influence of the disease on the target sign index in the reference patient group, the average value of the disease influence metrics corresponding to the target sign indexes of all the reference patients is calculated to obtain the disease comprehensive influence metrics of the target sign index, and the larger the disease comprehensive influence metrics are, the larger the influence degree of the disease on the target sign index in the reference patient group is represented, and the more important the target sign index is for evaluating the physiological characteristic difference degree of the patient to be nursed and the reference patient.
In order to quantify the difference in condition of a patient to be cared for from a reference patient on a target sign index, preferably, in one embodiment of the present invention, the method for acquiring a condition difference metric includes:
And calculating the absolute value of the difference value of the disease influence measures of the target sign indexes of the patient to be nursed and the reference patient, and obtaining the condition difference measure of the target sign indexes of the patient to be nursed and the reference patient.
And aiming at the steps, calculating the absolute value of the difference value of the patient to be nursed and the reference patient on the disease influence measurement of the target sign index to obtain the condition difference measurement of the target sign index of the patient to be nursed and the reference patient. The condition difference measure reflects the condition difference degree of the patient to be nursed and the reference patient on the target sign index, and the larger the condition difference measure is, the larger the condition difference degree of the patient to be nursed and the reference patient is, the more important the target sign index is for evaluating the physiological characteristic difference degree of the patient to be nursed and the reference patient is.
In order to more comprehensively evaluate the degree of physiological characteristic difference between the patient to be nursed and the reference patient, preferably, in one embodiment of the present invention, the method for acquiring the physiological difference index includes:
according to the difference condition of time sequence data of the corresponding monitoring values of each physiological sign index of the patient to be nursed and the reference patient after the operation, acquiring postoperative detection difference values of each physiological sign index of the patient to be nursed and the reference patient;
The physiological sign indexes are forward fused to obtain the adjustment weight of each physiological sign index of the patient to be nursed and the reference patient, wherein the physiological sign indexes correspond to the comprehensive disease influence measurement and the condition difference measurement;
And carrying out weighted summation on the post-operation detection difference value by utilizing the adjustment weight of each physiological sign index of the patient to be nursed and the reference patient to obtain the physiological difference index of the patient to be nursed and the reference patient.
The specific acquisition method comprises the step of taking the DTW distance of each physiological sign index of a patient to be nursed and a reference patient after an operation, which corresponds to the time sequence data of the monitoring value, as the postoperative detection difference value of each physiological sign index of the patient to be nursed and the reference patient. It should be noted that, the forward fusion is a well known prior art for those skilled in the art, and the forward fusion may be performed by a simple product, an arithmetic average or other suitable fusion method. And carrying out weighted summation on the post-operation detection difference value by utilizing the adjustment weight of each physiological sign index of the patient to be nursed and the reference patient to obtain the physiological difference index of the patient to be nursed and the reference patient. It should be noted that, the DTW distance is well known to those skilled in the art, and may be obtained by a DTW (DYNAMIC TIME WARPING ) algorithm, and the specific obtaining method is not described herein. It should be noted that the normalization method is to normalize by using a norm normalization function, and limit the numerical range to 0 to 1. The normalization is a technical means well known to those skilled in the art, and the normalization function may be selected from linear normalization, standard normalization, etc., and the specific normalization method is not limited herein.
For the above steps, the patient to be cared and the reference patient are time-series data of the monitoring value of each physiological sign index after the operation. These data reflect the patient's physiological state changes after surgery. And calculating the difference condition of time sequence data of the corresponding monitoring value after the operation on each physiological sign index of the patient to be nursed and the reference patient. The DTW distance is used here as a post-operative detection difference value, which can measure the degree of difference between two time series data. The larger the postoperative detection difference value is, the greater the physiological state difference degree between the patient to be nursed and the reference patient is. Next, the disease complex impact metric and the condition difference metric for each physiological sign index are forward fused to obtain an adjustment weight. The disease integrated influence metric and the condition difference metric mentioned here reflect the importance of the physiological sign index in disease assessment, and the adjustment weight of each index can be obtained by calculating the product of the disease integrated influence metric and the condition difference metric and performing normalization processing. The adjustment weight comprehensively reflects the importance degree of physiological sign indexes of the patient to be nursed and the reference patient in evaluating the disease difference degree. And finally, carrying out weighted summation on the postoperative detection difference value of each physiological sign index by utilizing the obtained adjustment weight to obtain the physiological difference index of the patient to be nursed and the reference patient. The physiological difference index reflects the physiological characteristic difference condition of the patient to be nursed and the reference patient more accurately by considering the importance of different indexes in disease evaluation.
And S3, selecting a nursing scheme of the patient to be nursed according to the physiological difference indexes of the patient to be nursed and the reference patient and the nursing scheme of the reference patient.
The physiological condition difference degree between the patient to be nursed and the reference patient is estimated more accurately by using the physiological difference index, so that the accuracy of selecting the nursing scheme for the patient to be nursed is improved.
In order to select an appropriate care regimen for a patient to be cared for, preferably, in one embodiment of the present invention, a method of selecting a care regimen for a patient to be cared for includes:
for each nursing scheme, the nursing scheme is corresponding to all reference patients and is used as nursing patients of the nursing scheme, and the average value of physiological difference indexes of the patient to be nursing and all nursing patients is calculated and mapped in a negative correlation way to obtain the nursing matching measurement of the nursing scheme;
The largest care matching metric corresponds to the care plan as the care plan for the patient to be cared for. It should be noted that other negative correlation mapping methods such as the opposite number and the inverse number may be selected, which are all technical means well known to those skilled in the art, and are not described and limited herein.
For each care plan, the mean value of the physiological difference index of the patient to be cared and all the care patients under the plan is calculated for the steps. This step aims to obtain a generalized difference measure reflecting the overall level of difference between the patient to be cared for and the patient under the regimen. Since the goal is to find the care plan with the highest degree of matching, a smaller physiological difference generally means a higher degree of matching, and performing the negative correlation mapping results in a care matching metric for the care plan. Finally, the care matching metrics of all care regimens are compared and the care regimen with the largest matching metric is selected as the care regimen for the patient to be cared for, which means that the care regimen employed by the reference patient population that is most similar to the disease condition of the patient to be cared for is found, thereby hopefully providing the most suitable care for the patient to be cared for. It should be noted that, the same care regimen will be used as one care regimen, and in the present invention, each care regimen corresponds to a plurality of reference patients, and these reference patients are used for comparing with the data of the patient to be cared for to evaluate the matching degree of the different care regimens.
The invention provides an intelligent nursing monitoring system, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of an automatic analysis method for patient health data of the intelligent nursing monitoring system when executing the computer program.
In summary, the embodiment of the invention provides an intelligent nursing monitoring system and an automatic analysis method for patient health data thereof, wherein the disease influence measurement is obtained according to target physical sign indexes and time sequence data of monitoring values of the physical sign indexes before and after an operation; the method comprises the steps of obtaining a condition difference measurement of target physical sign indexes of a patient to be nursed and a reference patient, obtaining the physical difference indexes of the patient to be nursed and the reference patient according to the difference condition of time sequence data of monitoring values corresponding to each physical sign index of the patient to be nursed and the reference patient after operation and comprehensive influence measurement and condition difference measurement of diseases corresponding to the physical sign indexes, and further selecting a nursing scheme of the patient to be nursed. According to the invention, the physiological condition difference degree between the patient to be nursed and the reference patient is more accurately estimated by deeply analyzing the condition that the physiological sign index is affected by the disease, and the accuracy of selecting the nursing scheme for the patient to be nursed is improved.
It should be noted that the sequence of the 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 particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.