Intelligent processing system for physical sign data of patient in department of cardiology
By designing an intelligent processing system for sign data of cardiology patients, the problem that single integrated sign data processing results in the existing technology cannot describe multiple associations, and the accurate description and analysis of the associations between multiple sign data is achieved, which improves the diagnostic accuracy of doctors.
Patent Information
- Application Number
- CN202411750036.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-02
- Publication Date
- 2025-03-21
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The prior art is difficult to effectively describe the association between multiple cardiology sign data, affecting the diagnostic accuracy of a doctor.
An intelligent processing system for signs data of cardiology patients is designed, including data collection, sorting, storage, preprocessing, analysis and visualization modules. The system is standardized through the data sorting module, the data preprocessing module performs deduplication, missing values and outliers, the data analysis module performs descriptive statistics, correlation and regression analysis, and graphically displays through the data visualization module, helping doctors to intuitively understand patient data.
Through the system's multiple module processing, the correlation between multiple sign data can be accurately described, the doctor's accuracy in judging the patient's condition, and assist the doctor in diagnosis and treatment.
Smart Images
Figure CN119673474A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent processing system for vital sign data of cardiology patients. Background Art
[0002] Cardiology refers to cardiovascular medicine, which is an important department in clinical medicine that specializes in the diagnosis, treatment and prevention of cardiovascular diseases. During the diagnosis and treatment process of cardiology, monitoring and observing the patient's vital signs data is an important basis for doctors' diagnosis and treatment, and plays an important role in the treatment of patients. In the process of obtaining and observing the patient's vital signs data, the cardiology department usually detects multiple vital signs data of the patient at the same time, thereby realizing multi-faceted monitoring of the patient's physical condition, and as the treatment progresses, the vital signs data will gradually increase. In order to ensure the efficiency of the doctor's diagnosis process, a large amount of vital signs data needs to be processed, so that the doctor can clearly understand the patient's condition.
[0003] At present, the processing of patient vital sign data is to use computers and other equipment to calculate and process the same data to obtain the analysis results of single data. However, various vital sign data in cardiology are mutually influenced and correlated. Therefore, the processing results of single data cannot describe the correlation between multiple vital sign data, thus affecting the accuracy of doctors' judgment of patients' conditions based on the data. Summary of the invention
[0004] The purpose of the present invention is to provide an intelligent processing system for vital sign data of cardiology patients to solve the problem that the processing result of a single vital sign data proposed in the above-mentioned background technology cannot describe the impact of multiple related vital sign data on the patient.
[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent processing system for vital sign data of patients in the Department of Cardiology, comprising a data acquisition module, a data sorting module, a data storage module, a data preprocessing module, a data analysis module and a data visualization module, each of which is mounted on a computer terminal, and the data acquisition module is used to obtain various vital sign data of patients in the Department of Cardiology;
[0006] Preferably, the data acquisition module is connected to a data collation module, the data collation module receives the vital sign data from the data acquisition module and sorts the vital sign data, the data collation module includes a data standardization processing part and a data set processing part, the data standardization processing part converts the received vital sign data uniformly according to international standard units, and transmits the converted vital sign data to the data set processing part, the data set processing part groups the vital sign data into data sets according to certain rules, such as different vital sign data sets of the same patient and different patient data sets of the same vital sign data.
[0007] By adopting the above technical solution, the vital sign data collected by the data collection module can be sorted using the data sorting module to facilitate subsequent analysis and processing.
[0008] Preferably, the data set processed by the data sorting module is transmitted to a data storage module for storage, and the data storage module is provided with a database, and the database stores the data set in partitions.
[0009] By adopting the above technical solution, the sorted data can be stored in partitions using the data storage module.
[0010] Preferably, the data storage module is connected to a data preprocessing module, which calls the patient's cardiology vital signs data stored in the data storage module and preprocesses the called data. The data preprocessing module includes a data cleaning part and a data sorting part.
[0011] By adopting the above technical solution, the data preprocessing module can be used to preprocess the data before data analysis, thereby increasing the efficiency and accuracy of data analysis.
[0012] Preferably, the data cleaning part includes data deduplication processing, data missing value processing and data outlier processing. The data deduplication processing part uses a computer algorithm to retrieve and call out data, removes repetitive patient vital signs data, and ensures the accuracy of the vital signs data. The data missing value processing deletes the missing data part in the patient vital signs data set to ensure the accuracy of data analysis. The data outlier processing highlights the data with obvious abnormalities in the patient vital signs data. The data sorting part sorts the cleaned patient vital signs data to facilitate medical staff to observe the data. The sorting processing includes time series sorting and data size sorting. The time series sorting is based on date and time sorting. The patient vital signs data are arranged in ascending order according to time. The time series sorting is convenient for subsequent screening out patient data within a specific time. The data size sorting arranges a certain patient vital signs data in ascending order. The data size sorting is convenient for subsequent screening out the patient treatment cycle corresponding to a certain abnormal data.
[0013] Using the above technical solution, the data preprocessing part sets a variety of data processing methods to remove duplicate data in the data set and highlight abnormal data to remind doctors to focus on observation.
[0014] Preferably, the data analysis module calls the vital sign data processed by the data preprocessing module, and uses the algorithm installed in the computer to realize the processing and analysis of the vital sign data. The data analysis module includes a data screening part, an existing data analysis part and a framework prediction model part. The data screening part extracts from the vital sign data that has been sorted in the data preprocessing module, such as extracting a data set of a specific time from the time sorting part of the data or extracting a data set of a certain segment of data that obviously exceeds the standard from the data size sorting part. After the data screening, the data is transmitted to the existing data analysis part for processing and analysis.
[0015] By adopting the above technical solution, the data analysis module can be used to analyze the pre-processed data to assist doctors in diagnosis and treatment.
[0016] Preferably, the existing data analysis part includes performing descriptive statistical analysis on the data to obtain the central tendency such as the mean, median and mode in the data set, as well as the degree of dispersion such as the range, variance and standard deviation of the data, so as to quickly understand the central position and degree of dispersion of the patient's vital sign data, and facilitate the rapid discovery of abnormal values in the vital sign data set. The existing data analysis part sets correlation analysis to perform correlation analysis on multiple vital sign data to obtain the correlation between multiple vital sign indicators. The existing data analysis part sets regression analysis to process the data. The regression analysis uses a certain vital sign indicator as an independent variable and another vital sign indicator as a dependent variable to construct a regression model between the variables. The constructed regression model is used to describe the relationship between multiple vital sign indicator variables. The data analysis module sets a prediction model construction part. The prediction model construction part generates a disease prediction model using clinical treatment data. After the vital sign indicator data is input into the prediction model, a prediction of the patient's recovery period can be obtained.
[0017] By adopting the above technical solution, the data analysis part can be used to perform multiple analyses on the data, thereby obtaining changes between the same indicator data and correlation analysis between different types of data.
[0018] Preferably, the data visualization module is connected to the data analysis module and receives the analyzed data. The data visualization module is provided with a statistical table display part and a statistical chart display part. The statistical table display part displays the received data in the form of a statistical table to obtain a statistical table of different indicator data of a certain patient or a statistical table of different patients with the same indicator. The statistical chart display part displays the received data in the form of a statistical chart, and the statistical chart includes a bar chart, a line chart and a pie chart.
[0019] By adopting the above technical solution and using a variety of statistical chart display formats, the processed characteristic data can be visualized, making it easier for doctors to observe patient data intuitively.
[0020] Compared with the prior art, the invention has the following beneficial effects: the intelligent processing system for cardiology patient vital sign data:
[0021] 1. In the present invention, before data analysis, a data sorting module is set to perform standardized processing on the data, unify the acquired data units, and facilitate subsequent data preprocessing. The data preprocessing part sets multiple data processing methods to remove duplicate data and missing data in a large amount of data, and highlight obvious abnormal value data, so as to reduce the analysis errors caused by abnormal data that may exist in the data analysis process. The processed data is arranged in a certain order to generate a data group. For example, in the sorting of time series, by extracting data within a period of time, the data within a specific time period of the patient can be analyzed, thereby realizing accurate analysis of the patient's vital sign data;
[0022] 2. The present invention provides a data analysis module and a data visualization module to realize data analysis, processing and visualization, so as to facilitate doctors to observe and analyze patient data intuitively. A variety of analysis methods are provided in the data analysis module to realize the relationship analysis between single indicator data and multiple indicator data, and the data are displayed in the form of charts through the visualization module. The analysis between multiple indicator data can describe the impact of multiple indicator data on patients. Different forms of charts are used for visualization, which can intuitively display the numerical changes, change trends and other influencing factors of patient vital signs data. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] Figure 1 It is a schematic diagram of the process structure of the present invention;
[0024] Figure 2 This is a schematic diagram of the structure of the data sorting module of the present invention;
[0025] Figure 3 This is a structural diagram of a data storage module of the present invention;
[0026] Figure 4 This is a schematic diagram of the structure of the data preprocessing module of the present invention;
[0027] Figure 5 This is a structural diagram of a data analysis module of the present invention;
[0028] Figure 6 This is a schematic diagram of the data visualization module structure of the present invention. DETAILED DESCRIPTION
[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.
[0030] See also Figure 1-Figure 6 The present invention provides a technical solution: an intelligent processing system for vital sign data of cardiology patients.
[0031] The modules are all mounted on a computer terminal. The data acquisition module is used to obtain various vital sign data of patients in the cardiology department. The data acquisition module is connected to the data sorting module. The data sorting module receives the vital sign data from the data acquisition module and sorts the vital sign data. The data sorting module includes a data standardization processing part and a data set processing part. The data standardization processing part converts the received vital sign data uniformly according to international standard units, and transmits the converted vital sign data to the data set processing part. The data set processing part collects the vital sign data into a data set according to a certain rule, such as different vital sign data sets of the same patient and different patient data sets of the same vital sign data. The data set processed by the data sorting module is transmitted to the data storage module for storage. The data storage module sets a database, and the database stores the data set in partitions.
[0032] like Figure 1 , Figure 2 and Figure 3 As shown, in the process of using this system to process the vital sign data of cardiology patients, the data acquisition module is connected to the patient's vital sign monitoring equipment to obtain the patient's vital sign data, and the vital sign data is input into the data sorting module. The data sorting module standardizes the received data so that the same vital sign indicator data are marked with a unified international standard unit. The converted data is transmitted to the data set processing part and is assembled into a data set according to a certain rule. For example, the data of the same patient forms a data set, and the data set is transmitted to the database of the data storage module for partitioned storage to facilitate subsequent data analysis and processing.
[0033] The data storage module is connected to the data preprocessing module. The data preprocessing module calls the cardiology vital signs data of the patient stored in the data storage module and preprocesses the called data. The data preprocessing module includes a data cleaning part and a data sorting part. The data cleaning part includes data deduplication processing, data missing value processing and data abnormal value processing. The data deduplication processing part uses a computer algorithm to retrieve the called data and removes the repetitive patient vital signs data to ensure the accuracy of the vital signs data. The data missing value processing deletes the missing data part in the patient vital signs data set to ensure the accuracy of data analysis. The data abnormal value processing highlights the data with obvious abnormalities in the patient vital signs data. The data sorting part sorts the cleaned patient vital signs data to facilitate medical staff to observe the data. The sorting processing includes time series sorting and data size sorting. The time series sorting is based on date and time sorting. The patient vital signs data are arranged in ascending order according to the time sequence. The time series sorting is convenient for subsequent screening of patient data within a specific time. The data size sorting arranges a certain patient vital signs data in ascending order. The data size sorting is convenient for subsequent screening of the patient treatment cycle corresponding to a certain abnormal data.
[0034] like Figure 1 and Figure 4 As shown, the data preprocessing module calls the patient data from the database and preprocesses the data, including deduplication of a large amount of data, removal of duplicate records, reducing the processing volume of subsequent data analysis, ensuring the accuracy of the vital sign data, and performing data missing value processing after deduplication, deleting some missing data in the record to avoid the amount of missing data affecting the subsequent analysis and causing errors in the correlation analysis. For the data with obvious abnormal values, the data is highlighted to remind the doctor to focus on the patient's condition in this part of the data. The processed data is sorted in order, such as sorting by date and time, arranging the patient's vital sign data in ascending order according to time, or sorting by data size to arrange a certain vital sign data of the patient in ascending order. Time series sorting can realize the extraction and analysis of data in a special time period in the subsequent analysis process, and data size sorting can filter out the patient treatment cycle corresponding to a certain abnormal data segment, assisting doctors in the diagnosis and treatment process.
[0035] The data analysis module calls the vital sign data processed by the data preprocessing module, and uses the algorithm installed in the computer to realize the processing and analysis of the vital sign data. The data analysis module includes a data screening part, an existing data analysis part and a prediction model construction part. The data screening part extracts from the vital sign data sorted in the data preprocessing module, such as extracting a data set of a specific time from the time sorting part of the data or extracting a data set of a certain period of data that obviously exceeds the standard from the data size sorting part. After data screening, the data is transmitted to the existing data analysis part for processing and analysis. The existing data analysis part includes descriptive statistical analysis of the data to obtain the central tendency such as the mean, median and mode in the data set, as well as the range and variance of the data. and standard deviation, etc., to quickly understand the central position and discreteness of the patient's vital signs data, so as to quickly find outliers in the vital signs data set. The existing data analysis part sets correlation analysis to perform correlation analysis between multiple vital signs data, and obtains the correlation between multiple vital signs indicators. The existing data analysis part sets regression analysis to process the data. The regression analysis uses a certain vital sign indicator as an independent variable and another vital sign indicator as a dependent variable, and constructs a regression model between the variables. The constructed regression model is used to describe the relationship between multiple vital sign indicator variables. The data analysis module sets a prediction model construction part. The prediction model construction part uses clinical treatment data to generate a disease prediction model. After the vital sign indicator data is input into the prediction model, the patient's recovery period prediction can be obtained.
[0036] like Figure 1 and Figure 5 As shown, the vital sign data after preprocessing is input into the data analysis module for analysis and processing, and the vital sign data after data sorting in the data preprocessing module is extracted through the data screening part, such as screening the vital sign data of a specific time period from the time series sorting for analysis, and descriptive statistical analysis of the data is performed to calculate the central tendency of the data group such as the mean, median and mode, as well as the degree of dispersion of the data such as the range, variance and standard deviation, so as to quickly understand the central position and degree of dispersion of the patient's vital sign data, so as to quickly find outliers in the vital sign data set and assist doctors in diagnosis and analysis. In addition to basic analysis of the data, correlation analysis can also be performed on the data to obtain the correlation between multiple vital sign indicators and a regression model constructed with a certain vital sign indicator as an independent variable and another vital sign indicator as a dependent variable. The regression model is used to realize data support for the correlation between the data, and a prediction model is constructed. After the vital sign indicator data is input into the prediction model, the recovery period of the patient can be predicted to assist doctors in treatment.
[0037] The data visualization module is connected to the data analysis module and receives the analyzed data. The data visualization module is provided with a statistical table display part and a statistical graph display part. The statistical table display part displays the received data in the form of a statistical table to obtain a statistical table of different index data of a certain patient or a statistical table of different patients with the same index. The statistical graph display part displays the received data in the form of a statistical graph, which includes a bar graph, a line graph and a pie chart.
[0038] like Figure 1 and Figure 6 As shown, the data after data analysis and processing is transmitted to the data visualization module and visualized in the form of statistical charts to obtain statistical tables of different indicators of the same patient or statistical tables of different patients with the same indicators. The statistical charts can be set in various forms. The bar chart can intuitively display the value of a certain data of the patient, while the line chart can show the changing trend of the patient's vital signs data. The pie chart can be used to show the proportion of the impact of a certain vital sign data on the patient. The patient's vital signs data can be visualized from different directions to facilitate the doctor's observation and diagnosis.
[0039] Working principle: In the process of processing the patient's vital sign data, the data acquisition module is used to obtain the patient's vital sign data, and the acquired data is input into the data sorting module for data standardization and arrangement in a certain order, and stored in the database of the data storage module. The data preprocessing module can be used to preprocess the data, remove duplicate data and missing values, and mark obviously abnormal data to remind doctors to pay special attention. The vital sign data after preprocessing is input into the data analysis module for analysis and processing. Through descriptive statistical analysis, abnormal values in the vital sign data set are quickly discovered to assist doctors in diagnosis and analysis, and a prediction model is constructed. After the vital sign indicator data is input into the prediction model, the patient's recovery period can be predicted to assist doctors in treatment. The data after data analysis and processing is transmitted to the data visualization module and visualized in the form of statistical charts to facilitate doctors' observation and diagnosis.
[0040] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. An intelligent processing system for cardiology patient vital sign data, comprising a data acquisition module, a data sorting module, a data storage module, a data preprocessing module, a data analysis module and a data visualization module, characterized in that: Each of the modules is mounted on a computer terminal, and the data acquisition module is used to obtain various vital sign data of cardiology patients.
2. The intelligent processing system for cardiology patient vital sign data according to claim 1, characterized in that: The data acquisition module is connected to the data sorting module, which receives the vital sign data from the data acquisition module and sorts the vital sign data. The data sorting module includes a data standardization processing part and a data set processing part. The data standardization processing part converts the received vital sign data uniformly according to international standard units, and transmits the converted vital sign data to the data set processing part. The data set processing part collects the vital sign data into data sets according to certain rules, such as different vital sign data sets of the same patient and different patient data sets of the same vital sign data.
3. The intelligent processing system for cardiology patient vital sign data according to claim 1, characterized in that: The data set processed by the data sorting module is transmitted to the data storage module for storage. The data storage module sets up a database, and the database stores the data set in partitions.
4. The intelligent processing system for cardiology patient vital sign data according to claim 1, characterized in that: The data storage module is connected to the data preprocessing module, which calls the patient's cardiology vital signs data stored in the data storage module and preprocesses the called data. The data preprocessing module includes a data cleaning part and a data sorting part.
5. The intelligent processing system for cardiology patient vital sign data according to claim 4, characterized in that: The data cleaning part includes data deduplication processing, data missing value processing and data abnormal value processing. The data deduplication processing part uses computer algorithm to retrieve and call out data, removes repetitive patient vital sign data, and ensures the accuracy of vital sign data. The data missing value processing deletes the missing data part in the patient vital sign data set to ensure the accuracy of data analysis. The data abnormal value processing highlights the data with obvious abnormalities in the patient vital sign data. The data sorting part sorts the cleaned patient vital sign data to facilitate medical staff to observe the data. The sorting processing includes time series sorting and data size sorting. The time series sorting is based on date and time sorting. The patient vital sign data is arranged in ascending order according to time. The time series sorting is convenient for subsequent screening of patient data within a specific time. The data size sorting arranges a certain patient vital sign data in ascending order. The data size sorting is convenient for subsequent screening of patient treatment cycles corresponding to a certain abnormal data.
6. The intelligent processing system for cardiology patient vital sign data according to claim 1, characterized in that: The data analysis module calls the vital sign data processed by the data preprocessing module, and uses the algorithm installed in the computer to realize the processing and analysis of the vital sign data. The data analysis module includes a data screening part, an existing data analysis part and a framework prediction model part. The data screening part extracts from the vital sign data that has been sorted in the data preprocessing module, such as extracting a data set of a specific time from the time sorting part of the data or extracting a data set of a certain section of data that obviously exceeds the standard from the data size sorting part. After the data screening, the data is transmitted to the existing data analysis part for processing and analysis.
7. The intelligent processing system for cardiology patient vital sign data according to claim 6, characterized in that: The existing data analysis part includes descriptive statistical analysis of the data to obtain the central tendency such as the mean, median and mode in the data set, as well as the degree of dispersion such as the range, variance and standard deviation of the data, so as to quickly understand the central position and degree of dispersion of the patient's vital sign data, and facilitate the rapid discovery of abnormal values in the vital sign data set. The existing data analysis part sets correlation analysis to perform correlation analysis on multiple vital sign data to obtain the correlation between multiple vital sign indicators. The existing data analysis part sets regression analysis to process the data. The regression analysis uses a certain vital sign indicator as an independent variable and another vital sign indicator as a dependent variable to construct a regression model between the variables. The constructed regression model is used to describe the relationship between multiple vital sign indicator variables. The data analysis module sets a prediction model construction part. The prediction model construction part uses clinical treatment data to generate a disease prediction model. After the vital sign indicator data is input into the prediction model, a prediction of the patient's recovery period can be obtained.
8. The intelligent processing system for cardiology patient vital sign data according to claim 1, characterized in that: The data visualization module is connected to the data analysis module and receives the analyzed data. The data visualization module is provided with a statistical table display part and a statistical chart display part. The statistical table display part displays the received data in the form of a statistical table to obtain a statistical table of different indicator data of a certain patient or a statistical table of different patients with the same indicator. The statistical chart display part displays the received data in the form of a statistical chart, and the statistical chart includes a bar chart, a line chart and a pie chart.