Endocrine patient data mining method for nursing optimization

Abstract

The invention discloses an endocrine patient data mining method for nursing optimization, and relates to the field of data mining, and the method comprises the following steps: obtaining endocrine patient information, and setting data analysis nodes according to the endocrine patient information; the data analysis node obtains patient nursing data corresponding to the endocrine patient and obtains patient nursing feature data according to the patient nursing data; setting a comprehensive analysis node to comprehensively analyze the patient nursing characteristic data corresponding to each data analysis node; corresponding combined data sets are obtained according to the comprehensive analysis result, data mining is conducted on the combined data sets, and whether data between the corresponding combined data sets are associated or not is obtained according to the analysis result; analyzing and processing the associated combined data set to generate a nursing optimization scheme; according to the invention, the accuracy in the nursing optimization management process is improved to a certain extent.

Description

Technical Field

[0001] The present invention relates to the field of data mining, and in particular to an endocrine patient data mining method for nursing optimization. Background Art

[0002] Endocrine diseases are common chronic diseases, such as diabetes, hypothyroidism, and pituitary tumors. Due to the complexity and diversity of these diseases, the treatment and management of patients need to take into account many factors, including drug dosage, frequency, type and time, lifestyle and behavioral habits, physiological indicators, etc. Therefore, the care management of patients with endocrine diseases is a highly personalized and complex task, which requires doctors and related medical staff to conduct comprehensive assessments and adjustments based on the specific circumstances of each patient; However, traditional medical records and monitoring methods often have some limitations and shortcomings; for example, doctors' logs may not be detailed or accurate enough, patients' self-reports may be inaccurate or incomplete, and there may be a lack of shared data and information platforms between different medical institutions; these problems may cause doctors and medical staff to face difficulties in making diagnostic and treatment decisions, thereby affecting patients' treatment effects and health outcomes; therefore, how to effectively manage the data information corresponding to endocrine patients and improve patient care management based on the corresponding data information is a problem we need to solve. To this end, a data mining method for endocrine patients for nursing optimization is now provided. Summary of the invention

[0003] In order to solve the above technical problems, the object of the present invention is to provide an endocrine patient data mining method for nursing optimization.

[0004] The purpose of the present invention can be achieved by the following technical solution: A method for endocrine patient data mining for nursing optimization, comprising the following steps: Step S1: Setting up an endocrine data management platform, inputting endocrine patient information, setting up data analysis nodes according to the endocrine patient information, and analyzing and processing the data corresponding to the corresponding endocrine patients; Step S2: the data analysis node obtains the patient care data corresponding to each endocrine patient, and extracts features from the obtained patient care data to obtain patient care feature data; Step S3: setting a comprehensive analysis node, which obtains the patient care feature data corresponding to each data analysis node in the endocrine data management platform and isolates it from the endocrine patient information; Step S4: performing statistical analysis on the obtained patient care feature data, comprehensively sorting the obtained patient care feature data, sequentially performing permutations and combinations according to the comprehensive sorting results, setting corresponding combined data sets, and performing data mining on each combined data set; Step S5: preset a correlation threshold, compare and analyze the analysis result corresponding to the data mining with the correlation threshold, and determine whether there is a correlation between the data of the corresponding combined data sets according to the comparison and analysis result; Step S6: comprehensively sort the associated combined data sets, compare and analyze the obtained patient care feature data with the corresponding patient care feature data in each combined data set, and generate a nursing optimization plan based on the comparative analysis results.

[0005] Furthermore, the process of setting the data analysis node according to the endocrine patient information includes: Set up an endocrine data management platform and set up an information entry window in it; Enter the endocrine patient information through the information entry window, the endocrine patient information includes the patient's basic information, patient medical history information and laboratory test data; verify the obtained endocrine patient information to determine whether it is an endocrine patient, and set up a patient account for the patient; A corresponding data analysis node is set based on the patient account, and the data analysis node is associated with the corresponding patient account and is used to analyze and process the data corresponding to the patient account.

[0006] Furthermore, the process of the data analysis node acquiring the patient care feature data includes: An information collection terminal is set up to obtain patient care data corresponding to a corresponding patient account through the information collection terminal and mark the collection time. The patient care data includes physiological detection data, imaging detection data and nursing record data. The obtained patient care data is sent to a corresponding data analysis node according to the patient account; There is an endocrine mapping data table in each data analysis node corresponding to the endocrine data management platform. The data analysis node maps the patient care data to the corresponding endocrine mapping data table, obtains the corresponding patient care feature data according to the location information of the endocrine mapping data table to which the patient care data belongs, and marks the corresponding patient care feature data according to the collection time corresponding to the patient care data.

[0007] Furthermore, the process of obtaining the patient care feature data corresponding to each data analysis node in the endocrine data management platform by the comprehensive analysis node includes: A comprehensive analysis node is set in the endocrine data management platform, and the comprehensive analysis node is used to analyze and process the patient care feature data corresponding to the data analysis node in the endocrine data management platform; The comprehensive analysis node sets a dynamic shared key according to the volume of comprehensive data in the endocrine data management platform. The data analysis node performs verification based on the dynamic shared key. If the verification is successful, the corresponding patient care feature data is obtained, and the basic patient information corresponding to the patient account is isolated and processed, and the patient care feature data from the unified data analysis node is marked; The comprehensive analysis node traverses and verifies the data analysis nodes in the endocrine data management platform in turn, sets up a comprehensive storage pool, and stores the obtained patient care feature data.

[0008] Furthermore, the process of analyzing and processing the patient care characteristic data obtained by the comprehensive analysis node includes: Obtain endocrine mapping data tables corresponding to all patient care feature data in the comprehensive storage pool, obtain endocrine mapping data tables of the same type, set the endocrine mapping data tables of the same type as a summary data table set, concatenate the endocrine mapping data tables with the same data analysis node marks in each summary data table set obtained according to the collection time, sort the concatenation results of the table sets in sequence according to the collection time, and obtain endocrine care data elements; Performing statistical analysis on the obtained endocrine nursing data elements, obtaining consistent endocrine nursing data elements, and setting them as a combined data set; Obtaining the number of endocrine nursing data elements corresponding to the combined data set; presetting a number threshold, when the number of endocrine nursing data elements is greater than the number threshold, marking the combined data set as valid, otherwise, the combined data set is invalid; Perform data mining on valid combined data sets.

[0009] Furthermore, the process of determining whether the data between the corresponding combined data sets are associated includes: Arrange and combine the valid combined data sets obtained by the comprehensive analysis nodes, and set time series for the endocrine nursing data elements in the corresponding combined data sets according to the collection time according to the combination results; According to the time series, the corresponding endocrine care data elements are obtained for correlation analysis, and the corresponding correlation data are obtained in sequence. The correlation data obtained in the combination result are integrated to obtain comprehensive correlation data, and a correlation threshold is preset. The comprehensive correlation data is compared and analyzed with the correlation threshold. When the comprehensive correlation data is greater than or equal to the correlation threshold, the data between the correlation data sets corresponding to the corresponding combination sorting are correlated, otherwise there is no correlation.

[0010] Further, the associated combined data sets are comprehensively sorted, the size of the comprehensive associated data between the corresponding combined data set and other combined data sets is obtained, the comprehensive associated data are sorted from large to small according to the size relationship, and a data association queue is generated according to the sorting result; The patient care feature data corresponding to the data analysis node is obtained, and the obtained patient care feature data is matched and analyzed with other combined data sets in turn according to the data association queue to obtain the matching results corresponding to the patient care feature data.

[0011] Furthermore, the process of generating a nursing optimization plan based on the comparative analysis results includes: Obtain the corresponding combined data set according to the matching results corresponding to the patient care feature data, perform cluster analysis on the endocrine care data elements in the patient data set, and obtain all common data and all demand data; A risk optimization model is preset, and all common data and all demand data obtained are analyzed and processed based on the risk optimization model to obtain nursing optimization data, and a nursing optimization plan is generated based on the nursing optimization data.

[0012] Compared with the prior art, the beneficial effects of the present invention are: by setting data analysis nodes and comprehensive analysis nodes to analyze and process the patient care data corresponding to the patient and the patient care data corresponding to the platform respectively, the accuracy of the endocrine patient data analysis process is improved to a certain extent; In addition, by setting up an endocrine mapping data table, the patient care data corresponding to each patient is analyzed and processed according to the endocrine mapping data table, the corresponding table set series is set according to the consistent data in the endocrine mapping data table, and the endocrine care data elements and the corresponding combined data sets are obtained according to the table set series results. The combined data sets are analyzed and processed separately to determine whether the combined data sets are valid, thereby improving the effectiveness and accuracy of the nursing data management process to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a schematic diagram of a method for endocrine patient data mining for nursing optimization according to an embodiment of the present application. DETAILED DESCRIPTION

[0014] like Figure 1 As shown, a method for endocrine patient data mining for nursing optimization comprises the following steps: The specific implementation process of setting up an endocrine data management platform, inputting endocrine patient information, setting up data analysis nodes according to the endocrine patient information, and analyzing and processing the data corresponding to the corresponding endocrine patients includes: Set up an endocrine data management platform and set up an information entry window in it; Enter the endocrine patient information through the information entry window, which includes the patient's basic information, patient medical history information, and laboratory test data, among which: The patient's basic information includes name, gender, age, date of birth, contact information, and body mass index; Patient medical history information includes past medical history, family medical history, allergy history, surgical history, and medication history; Laboratory test data include blood sugar levels, hormone levels, metabolic indicators and other laboratory tests; Verify the obtained endocrine patient information to determine whether the experimental test data meets the standards for endocrine patients. If the data meets the preset standards, the patient is judged as an endocrine patient and a patient account is set for the patient. If the data does not meet the preset standards, the patient is judged as a non-endocrine patient and no patient account is set for the patient. A corresponding data analysis node is set based on the patient account, and the data analysis node is associated with the corresponding patient account and is used to analyze and process the data corresponding to the patient account.

[0015] The data analysis node obtains the patient care data corresponding to each endocrine patient, and extracts features from the obtained patient care data. The specific implementation process of obtaining the patient care feature data includes: An information collection terminal is set up to obtain patient care data corresponding to a corresponding patient account through the information collection terminal and mark the collection time. The patient care data includes physiological detection data, imaging detection data and nursing record data. The obtained patient care data is sent to a corresponding data analysis node according to the patient account; Each corresponding data analysis node in the endocrine data management platform has an endocrine mapping data table, which is the possible data results of various patient care data collected in the information collection terminal. The corresponding cells are set in the endocrine mapping data table, and the positions of the corresponding cells in the endocrine mapping data table are marked according to the collected patient care data, thereby realizing the mapping process of the patient care data; The data analysis node maps the patient care data to the corresponding endocrine mapping data table, obtains the corresponding patient care feature data according to the location information of the endocrine mapping data table to which the patient care data belongs, and marks the corresponding patient care feature data according to the collection time corresponding to the patient care data. It should be further explained that in the specific implementation process: The corresponding cells in the endocrine mapping data table set in the data analysis node contain the characteristic standards corresponding to the endocrine patients. When the patient care data is mapped to the corresponding cells in the endocrine mapping data table, the corresponding patient care characteristic data is obtained.

[0016] The specific implementation process of setting a comprehensive analysis node, obtaining the patient care feature data corresponding to each data analysis node in the endocrine data management platform by the comprehensive analysis node, and isolating the data from the endocrine patient information includes: A comprehensive analysis node is set in the endocrine data management platform, and the comprehensive analysis node is used to analyze and process the patient care feature data corresponding to the data analysis node in the endocrine data management platform; The comprehensive analysis node sets a dynamic shared key according to the volume of comprehensive data in the endocrine data management platform. The data analysis node performs verification based on the dynamic shared key. If the verification is successful, the corresponding patient care feature data is obtained, and the basic patient information corresponding to the patient account is isolated and processed, and the patient care feature data from the unified data analysis node is marked; The comprehensive analysis node traverses and verifies the data analysis nodes in the endocrine data management platform in turn, sets up a comprehensive storage pool, and stores the obtained patient care feature data.

[0017] The specific implementation process of performing statistical analysis on the obtained patient care feature data, comprehensively sorting the obtained patient care feature data, sequentially arranging and combining according to the comprehensive sorting results, setting corresponding combined data sets, and performing data mining on each combined data set includes: Obtain endocrine mapping data tables corresponding to all patient care feature data in the comprehensive storage pool, obtain endocrine mapping data tables of the same type, set the endocrine mapping data tables of the same type as a summary data table set, and concatenate the endocrine mapping data tables with the same data analysis node marks in each summary data table set obtained according to the collection time, wherein the table set concatenation is to concatenate the contents in each summary data table, and extract the corresponding endocrine mapping data tables from the summary data table in sequence according to the concatenation results; It should be further explained that, in the specific implementation process, the endocrine mapping data table described is the endocrine patient data table corresponding to the data analysis results after being mapped through the patient care data, rather than the endocrine patient data table that has not been processed through the analysis; Sort the concatenation results of the table set in sequence according to the collection time to obtain the endocrine nursing data elements; Performing statistical analysis on the obtained endocrine nursing data elements, obtaining consistent endocrine nursing data elements, and setting them as a combined data set; Obtaining the number of endocrine nursing data elements corresponding to the combined data set; presetting a number threshold, when the number of endocrine nursing data elements is greater than the number threshold, marking the combined data set as valid, otherwise, the combined data set is invalid; Perform data mining on valid combined data sets.

[0018] The specific implementation process of presetting the correlation threshold, comparing the analysis result corresponding to the data mining with the correlation threshold, and judging whether the data between the corresponding combined data sets are correlated according to the comparison analysis result includes: Arrange and combine the valid combined data sets obtained by the comprehensive analysis nodes, and set time series for the endocrine nursing data elements in the corresponding combined data sets according to the collection time according to the combination results; According to the time series, the corresponding endocrine care data elements are obtained for correlation analysis, and the corresponding correlation data are obtained in sequence. The correlation data obtained in the combination result are integrated to obtain comprehensive correlation data, and a correlation threshold is preset. The comprehensive correlation data is compared and analyzed with the correlation threshold. When the comprehensive correlation data is greater than or equal to the correlation threshold, the data between the correlation data sets corresponding to the corresponding combination sorting are correlated, otherwise there is no correlation.

[0019] The specific implementation process of comprehensively sorting the associated combined data sets, comparing and analyzing the obtained patient nursing feature data with the corresponding patient nursing feature data in each combined data set, and generating a nursing optimization plan according to the comparative analysis results includes: Comprehensively sort the associated combined data sets, obtain the size of the comprehensive associated data between the corresponding combined data set and other combined data sets, sort the data from large to small according to the size relationship of the comprehensive associated data, and generate a data association queue according to the sorting result; Obtain the patient care feature data corresponding to the data analysis node, match and analyze the obtained patient care feature data with other combined data sets in turn according to the data association queue, and obtain the matching results corresponding to the patient care feature data Obtain the corresponding combined data set according to the matching results corresponding to the patient care feature data, perform cluster analysis on the endocrine care data elements in the patient data set, and obtain all common data and all demand data; A risk optimization model is preset, and all common data and all demand data obtained are analyzed and processed based on the risk optimization model to obtain nursing optimization data, and a nursing optimization plan is generated based on the nursing optimization data.

[0020] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for endocrine patient data mining for nursing optimization, characterized in that: The following steps are involved: Step S1: Setting up an endocrine data management platform, inputting endocrine patient information, setting up data analysis nodes according to the endocrine patient information, and analyzing and processing the data corresponding to the corresponding endocrine patients; Step S2: the data analysis node obtains the patient care data corresponding to each endocrine patient, and extracts features from the obtained patient care data to obtain patient care feature data; Step S3: setting a comprehensive analysis node, which obtains the patient care feature data corresponding to each data analysis node in the endocrine data management platform and isolates it from the endocrine patient information; Step S4: performing statistical analysis on the obtained patient care feature data, comprehensively sorting the obtained patient care feature data, sequentially performing permutations and combinations according to the comprehensive sorting results, setting corresponding combined data sets, and performing data mining on each combined data set; Step S5: preset a correlation threshold, compare and analyze the analysis result corresponding to the data mining with the correlation threshold, and determine whether there is a correlation between the data of the corresponding combined data sets according to the comparison and analysis result; Step S6: comprehensively sort the associated combined data sets, compare and analyze the obtained patient care feature data with the corresponding patient care feature data in each combined data set, and generate a nursing optimization plan based on the comparative analysis results.

2. The method for endocrine patient data mining for nursing optimization according to claim 1, characterized in that: The process of setting the data analysis node according to the endocrine patient information includes: Set up an endocrine data management platform and set up an information entry window in it; Enter the endocrine patient information through the information entry window, the endocrine patient information includes the patient's basic information, patient medical history information and laboratory test data; verify the obtained endocrine patient information to determine whether it is an endocrine patient, and set up a patient account for the patient; A corresponding data analysis node is set based on the patient account, and the data analysis node is associated with the corresponding patient account and is used to analyze and process the data corresponding to the patient account.

3. The method for endocrine patient data mining for nursing optimization according to claim 2, characterized in that: The process of the data analysis node acquiring patient care feature data includes: An information collection terminal is set up, and patient care data corresponding to a corresponding patient account is obtained through the information collection terminal, and the collection time is marked. The patient care data includes physiological detection data, imaging detection data, and nursing record data. The obtained patient care data is sent to a corresponding data analysis node according to the patient account; There is an endocrine mapping data table in each data analysis node corresponding to the endocrine data management platform. The data analysis node maps the patient care data to the corresponding endocrine mapping data table, obtains the corresponding patient care feature data according to the location information of the endocrine mapping data table to which the patient care data belongs, and marks the corresponding patient care feature data according to the collection time corresponding to the patient care data.

4. The method for endocrine patient data mining for nursing optimization according to claim 3, characterized in that: The process of obtaining the patient care characteristic data corresponding to each data analysis node in the endocrine data management platform by the comprehensive analysis node includes: A comprehensive analysis node is set in the endocrine data management platform, and the comprehensive analysis node is used to analyze and process the patient care feature data corresponding to the data analysis node in the endocrine data management platform; The comprehensive analysis node sets a dynamic shared key according to the volume of comprehensive data in the endocrine data management platform. The data analysis node performs verification based on the dynamic shared key. If the verification is successful, the corresponding patient care feature data is obtained, and the basic patient information corresponding to the patient account is isolated and processed, and the patient care feature data from the unified data analysis node is marked; The comprehensive analysis node traverses and verifies the data analysis nodes in the endocrine data management platform in turn, sets up a comprehensive storage pool, and stores the obtained patient care feature data.

5. The method for endocrine patient data mining for nursing optimization according to claim 4, characterized in that: The process of analyzing and processing the patient care characteristic data obtained by the comprehensive analysis node includes: Obtain endocrine mapping data tables corresponding to all patient care feature data in the comprehensive storage pool, obtain endocrine mapping data tables of the same type, set the endocrine mapping data tables of the same type as a summary data table set, concatenate the endocrine mapping data tables with the same data analysis node marks in each summary data table set obtained according to the collection time, sort the concatenation results of the table sets in sequence according to the collection time, and obtain endocrine care data elements; Performing statistical analysis on the obtained endocrine nursing data elements, obtaining consistent endocrine nursing data elements, and setting them as a combined data set; Obtaining the number of endocrine nursing data elements corresponding to the combined data set; presetting a number threshold, when the number of endocrine nursing data elements is greater than the number threshold, marking the combined data set as valid, otherwise, the combined data set is invalid; Perform data mining on valid combined data sets.

6. The method for endocrine patient data mining for nursing optimization according to claim 5, characterized in that: The process of determining whether the data between corresponding combined data sets are associated includes: Arrange and combine the valid combined data sets obtained by the comprehensive analysis nodes, and set time series for the endocrine nursing data elements in the corresponding combined data sets according to the collection time according to the combination results; According to the time series, the corresponding endocrine care data elements are obtained for correlation analysis, and the corresponding correlation data are obtained in sequence. The correlation data obtained in the combination result are integrated to obtain comprehensive correlation data, and a correlation threshold is preset. The comprehensive correlation data is compared and analyzed with the correlation threshold. When the comprehensive correlation data is greater than or equal to the correlation threshold, the data between the correlation data sets corresponding to the corresponding combination sorting are correlated, otherwise there is no correlation.

7. The method for endocrine patient data mining for nursing optimization according to claim 6, characterized in that: Comprehensively sort the associated combined data sets, obtain the size of the comprehensive associated data between the corresponding combined data set and other combined data sets, sort the data from large to small according to the size relationship of the comprehensive associated data, and generate a data association queue according to the sorting result; The patient care feature data corresponding to the data analysis node is obtained, and the obtained patient care feature data is matched and analyzed with other combined data sets in turn according to the data association queue to obtain the matching results corresponding to the patient care feature data.

8. The method for endocrine patient data mining for nursing optimization according to claim 7, characterized in that: The process of generating a nursing optimization plan according to the comparative analysis results includes: Obtain the corresponding combined data set according to the matching results corresponding to the patient care feature data, perform cluster analysis on the endocrine care data elements in the patient data set, and obtain all common data and all demand data; A risk optimization model is preset, and all common data and all demand data obtained are analyzed and processed based on the risk optimization model to obtain nursing optimization data, and a nursing optimization plan is generated based on the nursing optimization data.

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