A big data-based clinical nursing data analysis method and system

Abstract

The application relates to the technical field of big data analysis, and discloses a clinical nursing data analysis method and system based on big data, wherein the clinical nursing data analysis method based on big data comprises the following steps: S1, data collection: obtaining patient clinical nursing data through integration of a hospital system and a wearable device, and ensuring coverage of outpatient to home scenarios; S2, data preprocessing: cleaning original data to process missing values and abnormal values, and extracting nursing indexes such as complication risks through conversion and feature engineering; S3, data storage; S4, data analysis; S5, model construction; S6, result visualization; S7, application implementation; and S8, evaluation and optimization. The application is reasonable in design, realizes full-life cycle management of clinical nursing data in a modular architecture, and through an end-to-end intelligent analysis chain, meets high safety and strong compliance requirements of a medical scene, provides extensible and self-optimizing decision support capability, and provides a systematic solution for smart nursing.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, and in particular to a method and system for analyzing clinical nursing data based on big data. Background Technology

[0002] In traditional clinical nursing practice, data analysis methods primarily rely on manual data processing and basic statistical tools, which have significant limitations. Nursing data is scattered and diverse in format, including electronic medical records, paper nursing records, and data from various monitoring devices, creating severe information silos. Medical personnel often manually summarize limited samples for trend observation, which is not only inefficient but also struggles to integrate dynamic data from multiple scenarios, resulting in poor timeliness and narrow coverage of analysis results. More importantly, traditional methods lack the ability to effectively process unstructured data, such as patient subjective feedback texts or imaging data, causing a large amount of high-value information to be overlooked. Simultaneously, with the widespread adoption of wearable devices and remote monitoring technologies, massive amounts of real-time data further exacerbate the processing difficulties, making it difficult for existing technologies to support the needs of cross-institutional, full-cycle nursing decision optimization.

[0003] Existing data storage and analysis architectures also suffer from systemic flaws. Most systems use centralized databases to store nursing information, which frequently experience query delays and storage bottlenecks when faced with exponentially increasing data volumes, severely limiting the feasibility of large-scale data analysis. At the analytical level, traditional methods are largely limited to descriptive statistics, unable to utilize advanced technologies such as machine learning to uncover deeper nursing patterns, such as predicting patient recovery inflection points or analyzing the correlation between complication risks. Furthermore, data preprocessing relies on manual cleaning rules, making it difficult to automate the handling of missing and outlier values, and easily introducing subjective biases. Privacy protection mechanisms are also inadequate, with sensitive information facing the risk of leakage during transmission and storage. The lack of an end-to-end analytical closed-loop design prevents nursing insights from being translated into real-time action plans, ultimately leading to delayed clinical decision-making, inaccurate resource allocation, and difficulty in achieving personalized patient care goals. Therefore, we propose a big data-based clinical nursing data analysis method and system to address this problem. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings mentioned in the background section by proposing a clinical nursing data analysis method and system based on big data.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A clinical nursing data analysis method based on big data includes the following steps:

[0007] S1. Data Collection: Acquire patient clinical care data by integrating hospital systems and wearable devices to ensure coverage from outpatient to home settings and verify reliability;

[0008] S2. Data Preprocessing: Clean the raw data to handle missing and outliers, and extract nursing indicators such as complication risk through transformation and feature engineering.

[0009] S3. Data storage: Store data in a distributed database or cloud storage, optimize partitioned indexes to improve query efficiency and back up data to ensure availability;

[0010] S4. Data Analysis: Apply clustering and regression algorithms to analyze nursing patterns, and combine clinical guidelines to generate insight reports to support decision optimization;

[0011] S5. Model Building: Based on the analysis, select a prediction model for training and validation to ensure the practicality and generalization ability of personalized care plans.

[0012] S6. Results Visualization: Design an interactive dashboard to visualize nursing results such as risk scores, and integrate a real-time update function to monitor dynamic data;

[0013] S7. Application Implementation: Deploy tools to the clinical environment and train nursing staff, monitor feedback to achieve seamless integration into workflow decision-making;

[0014] S8. Evaluation and Optimization: Evaluate the performance of the method, such as accuracy, optimize the algorithm and acquisition strategy, and iteratively update to adapt to new requirements.

[0015] Preferably, step S1 includes the following steps:

[0016] S101. Define data sources: Identify and list the sources of clinical nursing data, including electronic health record systems, sensor devices, and patient self-reporting platforms; secondly, assess the reliability and coverage of each data source to ensure it meets the needs of nursing analysis; finally, establish data acquisition protocols to standardize data formats and frequencies, laying the foundation for subsequent integration.

[0017] S102. Data Acquisition Methods: Automated tools such as API calls and web crawlers are used to acquire structured data, such as vital sign monitoring results, in real time; secondly, unstructured information, such as nursing notes and patient feedback, is supplemented by manual data entry and questionnaires; finally, a data synchronization mechanism is implemented to ensure timely updates and consistency of multi-source data.

[0018] S103. Data Integration: Merge data from different sources, use ETL tools to convert formats and eliminate conflicts; secondly, create a unified data model such as a patient-centric view, linking nursing events and medical history information; finally, verify the integrity and accuracy of the integrated data, and reduce errors through data quality checks.

[0019] Preferably, step S2 includes the following steps:

[0020] S201. Data Cleaning: Detect and process missing values ​​in the data, using mean imputation or deletion strategies to maintain dataset integrity; secondly, identify and correct outliers, filtering invalid records using statistical methods such as Z-score; finally, remove duplicate entries and standardize units to ensure data cleanliness and reliability.

[0021] S202. Data Transformation: Standardize numerical data, such as using Min-Max scaling or Z-score normalization, to eliminate the influence of units; secondly, encode categorical variables into numerical form, for example, using one-hot encoding or label encoding; finally, transform time series data into a format suitable for analysis, such as using sliding window or timestamp alignment.

[0022] S203. Feature Engineering: Extract relevant features from the raw data, such as calculating nursing duration or complication indicators; secondly, perform feature selection and dimensionality reduction operations, and use PCA or correlation analysis to reduce redundancy; finally, create derived variables such as risk scores to enhance the model's predictive ability.

[0023] Preferably, in step S3, it is important to note that during the data storage phase, data privacy regulations such as HIPAA must be strictly followed, and encryption and access control must be implemented to protect patient information. At the same time, the scalability of the storage system should be considered to cope with data growth and avoid performance bottlenecks. In addition, storage security measures should be audited regularly to ensure the confidentiality of data during transmission and at rest.

[0024] Preferably, in step S4, it is important to note that the data analysis process should emphasize the diversity and representativeness of the data to avoid bias affecting nursing decisions; at the same time, the analysis results should be verified in conjunction with the knowledge of clinical experts to ensure that they conform to actual nursing practice; in addition, when processing big data, computing resources should be optimized and parallel processing should be used to accelerate the analysis efficiency.

[0025] Preferably, step S5 includes the following steps:

[0026] S501. Selecting a Model Algorithm: Evaluate different algorithms based on the nursing analysis objectives, such as logistic regression for risk assessment or SVM for classification; secondly, consider data characteristics and computing resources to select the most suitable machine learning framework; finally, define the model's input and output parameters to ensure they are aligned with nursing needs.

[0027] S502, Training the Model: Divide the dataset into training and validation sets, and use cross-validation to optimize the training process; secondly, apply algorithms such as gradient descent to iteratively adjust the model weights and minimize the loss function; finally, monitor the training progress and prevent overfitting, and improve generalization through early stopping or regularization strategies.

[0028] S503, Model Validation: Evaluate the model performance on an independent test set, and calculate metrics such as AUC and F1 score; secondly, conduct sensitivity analysis to test the robustness of the model in different scenarios; finally, compare with the baseline model to ensure the improvement effect and generate a validation report.

[0029] Preferably, step S6 includes the following steps:

[0030] S601. Design a visual interface: Create an interactive dashboard based on user needs, and lay out key nursing indicators such as patient status and resource usage; secondly, select visualization tools such as Tableau or D3.js to achieve dynamic display; finally, optimize the user experience of the interface to ensure ease of use and accessibility.

[0031] S602. Generate Charts: Generate chart types based on analysis results, such as bar charts to show nursing efficiency trends; secondly, customize colors and labels to enhance readability and highlight important insights; finally, automate the chart update process and integrate real-time data sources.

[0032] S603, Interactive Functions: Add user interaction elements such as filters and drill-down functions to allow caregivers to explore detailed data; secondly, implement alarm and notification mechanisms to automatically trigger when anomalies are detected; finally, support report export and sharing functions to promote team collaboration.

[0033] Preferably, in S7, it is important to note that ethical compliance must be ensured during the application and implementation phase, and patient consent and institutional approval must be obtained. At the same time, continuous training and support should be provided to help nursing staff adapt to the new tool. In addition, the impact of implementation should be monitored, and adjustments should be made in a timely manner to reduce workflow disruptions.

[0034] This invention also provides a big data-based clinical nursing data analysis system, applied to the aforementioned big data-based clinical nursing data analysis method, comprising:

[0035] The heterogeneous data acquisition module integrates multi-source data and automatically collects structured and unstructured data such as vital signs and nursing records through API and real-time streaming technology, supporting multi-scenario coverage and verifying data reliability.

[0036] The intelligent preprocessing module automatically cleans up missing and outlier values, standardizes numerical features, encodes categorical variables, performs feature engineering to extract key indicators, reduces noise interference by dimensionality reduction, and ensures data quality and analyzability.

[0037] The secure storage management module uses distributed databases or cloud storage systems to store data, designs partitioned indexing strategies to optimize query efficiency, and implements encrypted backup and access control to ensure high data availability, privacy security, and disaster recovery capabilities.

[0038] The multimodal analysis engine module integrates algorithms such as clustering, regression, and time series analysis to uncover nursing patterns and risk factors; it also integrates clinical guidelines to generate decision reports and supports parallel computing to accelerate large-scale data processing.

[0039] The dynamic model building module automatically selects the optimal model based on the analysis results, trains and tunes the hyperparameters, and evaluates the robustness of the model through cross-validation and sensitivity testing to ensure the generalization ability of personalized care plans.

[0040] The interactive visualization module creates interactive dashboards that dynamically display indicators such as risk scores and resource utilization; it generates visual charts such as heat maps and line graphs, integrates real-time alerts and drill-down functions, and supports report export and sharing.

[0041] The clinical deployment and feedback module embeds models and tools into nursing workflows, provides staff training and technical support, monitors implementation effectiveness, collects feedback to optimize processes, and ensures ethical compliance and minimizes work disruption.

[0042] Continuously optimize the evaluation module, regularly evaluate system performance through A / B testing and satisfaction surveys, optimize algorithm parameters or data strategies based on feedback, and iterate and upgrade the system to adapt to new requirements and changes in data sources;

[0043] The multi-source integration and verification module executes the ETL process to unify the data model, eliminates source conflicts, and associates patient care information throughout the entire life cycle, automatically verifying the integrity and logical consistency of the integrated data.

[0044] Compared with existing technologies, this invention provides a clinical nursing data analysis method and system based on big data, which has the following beneficial effects:

[0045] (1) By constructing a closed-loop data system covering outpatient, inpatient and home scenarios, integrating multi-source heterogeneous data and dynamically verifying reliability, the problem of data fragmentation and poor timeliness in traditional nursing systems is solved, and the integrity of the dataset and the credibility of the decision-making basis are improved.

[0046] (2) By using an intelligent preprocessing engine and feature engineering to automatically extract key nursing indicators (such as complication risk scores), combined with privacy-compliant storage and a multimodal analysis engine, the noise interference from manual processing is significantly reduced, achieving a dual breakthrough in in-depth data value mining and compliance security.

[0047] (3) Through the dynamic model building and interactive visualization module, the predictive model is seamlessly integrated with the clinical decision-making scenario, which supports nurses to monitor patient risks and adjust nursing plans in real time. At the same time, the system can achieve self-iteration based on the continuous optimization module, which greatly improves nursing efficiency and personalized service level.

[0048] This invention is rationally designed and uses a modular architecture to achieve full lifecycle management of clinical nursing data. Through an end-to-end intelligent analysis chain, it not only meets the high security and strong compliance requirements of medical scenarios, but also provides scalable and self-optimizing decision support capabilities, providing a systematic solution for smart nursing. Attached Figure Description

[0049] Figure 1 This is a flowchart of a clinical nursing data analysis method based on big data proposed in this invention;

[0050] Figure 2 This is a system block diagram of a clinical nursing data analysis system based on big data proposed in this invention.

[0051] The diagram shows: 1. Heterogeneous data acquisition module; 2. Intelligent preprocessing module; 3. Secure storage management module; 4. Multimodal analysis engine module; 5. Dynamic model building module; 6. Interactive visualization module; 7. Clinical deployment and feedback module; 8. Continuous optimization and evaluation module; 9. Multi-source integration and validation module. Detailed Implementation

[0052] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0053] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0054] Reference Figure 1 A clinical nursing data analysis method based on big data includes the following steps:

[0055] S1. Data Collection: Acquire patient clinical care data by integrating hospital systems and wearable devices to ensure coverage from outpatient to home settings and verify reliability;

[0056] S2. Data Preprocessing: Clean the raw data to handle missing and outliers, and extract nursing indicators such as complication risk through transformation and feature engineering.

[0057] S3. Data storage: Store data in a distributed database or cloud storage, optimize partitioned indexes to improve query efficiency and back up data to ensure availability;

[0058] S4. Data Analysis: Apply clustering and regression algorithms to analyze nursing patterns, and combine clinical guidelines to generate insight reports to support decision optimization;

[0059] S5. Model Building: Based on the analysis, select a prediction model for training and validation to ensure the practicality and generalization ability of personalized care plans.

[0060] S6. Results Visualization: Design an interactive dashboard to visualize nursing results such as risk scores, and integrate a real-time update function to monitor dynamic data;

[0061] S7. Application Implementation: Deploy tools to the clinical environment and train nursing staff, monitor feedback to achieve seamless integration into workflow decision-making;

[0062] S8. Evaluation and Optimization: Evaluate the performance of the method, such as accuracy, optimize the algorithm and acquisition strategy, and iteratively update to adapt to new requirements.

[0063] In this embodiment, S1 includes the following steps:

[0064] S101. Define data sources: Identify and list the sources of clinical nursing data, including electronic health record systems, sensor devices, and patient self-reporting platforms; secondly, assess the reliability and coverage of each data source to ensure it meets the needs of nursing analysis; finally, establish data acquisition protocols to standardize data formats and frequencies, laying the foundation for subsequent integration.

[0065] S102. Data Acquisition Methods: Automated tools such as API calls and web crawlers are used to acquire structured data, such as vital sign monitoring results, in real time; secondly, unstructured information, such as nursing notes and patient feedback, is supplemented by manual data entry and questionnaires; finally, a data synchronization mechanism is implemented to ensure timely updates and consistency of multi-source data.

[0066] S103. Data Integration: Merge data from different sources, use ETL tools to convert formats and eliminate conflicts; secondly, create a unified data model such as a patient-centric view, linking nursing events and medical history information; finally, verify the integrity and accuracy of the integrated data, and reduce errors through data quality checks.

[0067] In this embodiment, S2 includes the following steps:

[0068] S201. Data Cleaning: Detect and process missing values ​​in the data, using mean imputation or deletion strategies to maintain dataset integrity; secondly, identify and correct outliers, filtering invalid records using statistical methods such as Z-score; finally, remove duplicate entries and standardize units to ensure data cleanliness and reliability.

[0069] S202. Data Transformation: Standardize numerical data, such as using Min-Max scaling or Z-score normalization, to eliminate the influence of units; secondly, encode categorical variables into numerical form, for example, using one-hot encoding or label encoding; finally, transform time series data into a format suitable for analysis, such as using sliding window or timestamp alignment.

[0070] S203. Feature Engineering: Extract relevant features from the raw data, such as calculating nursing duration or complication indicators; secondly, perform feature selection and dimensionality reduction operations, and use PCA or correlation analysis to reduce redundancy; finally, create derived variables such as risk scores to enhance the model's predictive ability.

[0071] In this embodiment, in S3, it is important to note that during the data storage phase, data privacy regulations such as HIPAA must be strictly followed, and encryption and access control must be implemented to protect patient information. At the same time, the scalability of the storage system should be considered to cope with data growth and avoid performance bottlenecks. In addition, storage security measures should be audited regularly to ensure the confidentiality of data during transmission and at rest.

[0072] In this embodiment, in S4, it is important to note that the data analysis process should focus on the diversity and representativeness of the data to avoid bias affecting nursing decisions; at the same time, the analysis results should be verified in conjunction with the knowledge of clinical experts to ensure that they conform to actual nursing practice; in addition, when processing big data, computing resources should be optimized and parallel processing should be used to accelerate the analysis efficiency.

[0073] In this embodiment, S5 includes the following steps:

[0074] S501. Selecting a Model Algorithm: Evaluate different algorithms based on the nursing analysis objectives, such as logistic regression for risk assessment or SVM for classification; secondly, consider data characteristics and computing resources to select the most suitable machine learning framework; finally, define the model's input and output parameters to ensure they are aligned with nursing needs.

[0075] S502, Training the Model: Divide the dataset into training and validation sets, and use cross-validation to optimize the training process; secondly, apply algorithms such as gradient descent to iteratively adjust the model weights and minimize the loss function; finally, monitor the training progress and prevent overfitting, and improve generalization through early stopping or regularization strategies.

[0076] S503, Model Validation: Evaluate the model performance on an independent test set, and calculate metrics such as AUC and F1 score; secondly, conduct sensitivity analysis to test the robustness of the model in different scenarios; finally, compare with the baseline model to ensure the improvement effect and generate a validation report.

[0077] In this embodiment, S6 includes the following steps:

[0078] S601. Design a visual interface: Create an interactive dashboard based on user needs, and lay out key nursing indicators such as patient status and resource usage; secondly, select visualization tools such as Tableau or D3.js to achieve dynamic display; finally, optimize the user experience of the interface to ensure ease of use and accessibility.

[0079] S602. Generate Charts: Generate chart types based on analysis results, such as bar charts to show nursing efficiency trends; secondly, customize colors and labels to enhance readability and highlight important insights; finally, automate the chart update process and integrate real-time data sources.

[0080] S603, Interactive Functions: Add user interaction elements such as filters and drill-down functions to allow caregivers to explore detailed data; secondly, implement alarm and notification mechanisms to automatically trigger when anomalies are detected; finally, support report export and sharing functions to promote team collaboration.

[0081] In this embodiment, S7, it is important to note that ethical compliance must be ensured during the application implementation phase, and patient consent and institutional approval must be obtained. At the same time, continuous training and support should be provided to help nursing staff adapt to the new tool. In addition, the impact of implementation should be monitored, and adjustments should be made in a timely manner to reduce workflow disruptions.

[0082] Reference Figure 2 The present invention also provides a big data-based clinical nursing data analysis system, applied to the above-mentioned big data-based clinical nursing data analysis method, comprising:

[0083] Heterogeneous data acquisition module 1 integrates multi-source data and automatically acquires structured and unstructured data such as vital signs and nursing records through API and real-time streaming technology, supporting multi-scenario coverage and verifying data reliability;

[0084] Intelligent preprocessing module 2 automatically cleans up missing and outlier values, standardizes numerical features, encodes categorical variables, performs feature engineering to extract key indicators, reduces noise interference by dimensionality reduction, and ensures data quality and analyzability.

[0085] Secure storage management module 3 uses distributed databases or cloud storage systems to store data, designs partitioned index strategies to optimize query efficiency, implements encrypted backup and access control, and ensures high data availability, privacy security and disaster recovery capabilities.

[0086] The multimodal analysis engine module 4 integrates algorithms such as clustering, regression, and time series analysis to uncover nursing patterns and risk factors; it also integrates clinical guidelines to generate decision reports and supports parallel computing to accelerate large-scale data processing.

[0087] The dynamic model building module 5 automatically selects the optimal model based on the analysis results, trains and fine-tunes the hyperparameters, and evaluates the robustness of the model through cross-validation and sensitivity testing to ensure the generalization ability of personalized care plans.

[0088] Interactive Visualization Module 6 creates interactive dashboards that dynamically display indicators such as risk scores and resource utilization; it generates visual charts such as heat maps and line graphs, integrates real-time alerts and drill-down functions, and supports report export and sharing.

[0089] Clinical Deployment and Feedback Module 7 embeds models and tools into nursing workflows, provides staff training and technical support, monitors implementation effectiveness, collects feedback to optimize processes, and ensures ethical compliance and minimizes work disruption.

[0090] Continuously optimize the evaluation module 8, regularly evaluate system performance through A / B testing and satisfaction surveys, optimize algorithm parameters or data strategies based on feedback, and iteratively upgrade the system to adapt to new requirements and changes in data sources;

[0091] The multi-source integration and verification module 9 executes the ETL process to unify the data model, eliminates source conflicts and associates patient care information throughout the entire life cycle, and automatically verifies the integrity and logical consistency of the integrated data.

[0092] All standard parts used in this invention can be purchased from the market, and irregular parts can be customized according to the description and drawings. The specific connection methods of each part adopt conventional methods such as bolts, rivets, and welding that are mature in the prior art. The machinery, parts and equipment adopt conventional models in the prior art, and the circuit connection adopts conventional connection methods in the prior art, which will not be described in detail here.

Claims

1. A clinical nursing data analysis method based on big data, characterized in that, Includes the following steps: S1. Data Collection: Acquire patient clinical care data by integrating hospital systems and wearable devices to ensure coverage from outpatient to home settings and verify reliability; S2. Data Preprocessing: Clean the raw data to handle missing and outliers, and extract nursing indicators such as complication risk through transformation and feature engineering. S3. Data storage: Store data in a distributed database or cloud storage, optimize partitioned indexes to improve query efficiency and back up data to ensure availability; S4. Data Analysis: Apply clustering and regression algorithms to analyze nursing patterns, and combine clinical guidelines to generate insight reports to support decision optimization; S5. Model Building: Based on the analysis, select a prediction model for training and validation to ensure the practicality and generalization ability of personalized care plans. S6. Results Visualization: Design an interactive dashboard to visualize nursing results such as risk scores, and integrate a real-time update function to monitor dynamic data; S7. Application Implementation: Deploy tools to the clinical environment and train nursing staff, monitor feedback to achieve seamless integration into workflow decision-making; S8. Evaluation and Optimization: Evaluate the performance of the method, such as accuracy, optimize the algorithm and acquisition strategy, and iteratively update to adapt to new requirements.

2. The clinical nursing data analysis method based on big data according to claim 1, characterized in that, S1 includes the following steps: S101. Define data sources: Identify and list the sources of clinical nursing data, including electronic health record systems, sensor devices, and patient self-reporting platforms; secondly, assess the reliability and coverage of each data source to ensure it meets the needs of nursing analysis; finally, establish data acquisition protocols to standardize data formats and frequencies, laying the foundation for subsequent integration. S102. Data Acquisition Methods: Automated tools such as API calls and web crawlers are used to acquire structured data, such as vital sign monitoring results, in real time; secondly, unstructured information, such as nursing notes and patient feedback, is supplemented by manual data entry and questionnaires; finally, a data synchronization mechanism is implemented to ensure timely updates and consistency of multi-source data. S103. Data Integration: Merge data from different sources, use ETL tools to convert formats and eliminate conflicts; secondly, create a unified data model such as a patient-centric view, linking nursing events and medical history information; finally, verify the integrity and accuracy of the integrated data, and reduce errors through data quality checks.

3. The clinical nursing data analysis method based on big data according to claim 2, characterized in that, S2 includes the following steps: S201. Data Cleaning: Detect and process missing values ​​in the data, using mean imputation or deletion strategies to maintain dataset integrity; secondly, identify and correct outliers, filtering invalid records using statistical methods such as Z-score; finally, remove duplicate entries and standardize units to ensure data cleanliness and reliability. S202. Data Transformation: Standardize numerical data, such as using Min-Max scaling or Z-score normalization, to eliminate the influence of units; secondly, encode categorical variables into numerical form, for example, using one-hot encoding or label encoding; finally, transform time series data into a format suitable for analysis, such as using sliding window or timestamp alignment. S203, Feature Engineering: Extracting relevant features from raw data, such as calculating nursing duration or complication indicators; Secondly, feature selection and dimensionality reduction are performed, and PCA or correlation analysis is used to reduce redundancy. Finally, derived variables such as risk scores are created to enhance the model's predictive power.

4. The clinical nursing data analysis method based on big data according to claim 3, characterized in that, In S3, it is important to note that during the data storage phase, data privacy regulations such as HIPAA must be strictly followed, and encryption and access control must be implemented to protect patient information. At the same time, the scalability of the storage system should be considered to cope with data growth and avoid performance bottlenecks. In addition, storage security measures should be audited regularly to ensure the confidentiality of data during transmission and at rest.

5. The clinical nursing data analysis method based on big data according to claim 4, characterized in that, In S4, it is important to note that the data analysis process should focus on the diversity and representativeness of the data to avoid bias affecting nursing decisions. At the same time, the analysis results should be verified in conjunction with the knowledge of clinical experts to ensure that they conform to actual nursing practice. In addition, when processing big data, computing resources should be optimized and parallel processing should be used to accelerate the analysis efficiency.

6. The clinical nursing data analysis method based on big data according to claim 5, characterized in that, S5 includes the following steps: S501. Selecting a Model Algorithm: Evaluate different algorithms based on the nursing analysis objectives, such as logistic regression for risk assessment or SVM for classification; secondly, consider data characteristics and computing resources to select the most suitable machine learning framework; finally, define the model's input and output parameters to ensure they are aligned with nursing needs. S502, Training the Model: Divide the dataset into training and validation sets, and use cross-validation to optimize the training process; secondly, apply algorithms such as gradient descent to iteratively adjust the model weights and minimize the loss function; finally, monitor the training progress and prevent overfitting, and improve generalization through early stopping or regularization strategies. S503, Model Validation: Evaluate model performance on independent test sets and calculate metrics such as AUC and F1 score; Secondly, sensitivity analysis is conducted to test the robustness of the model in different scenarios; finally, the model is compared with the baseline model to ensure the improvement effect and generate a validation report.

7. The clinical nursing data analysis method based on big data according to claim 6, characterized in that, S6 includes the following steps: S601. Design a visual interface: Create an interactive dashboard based on user needs, and lay out key nursing indicators such as patient status and resource usage; secondly, select visualization tools such as Tableau or D3.js to achieve dynamic display; finally, optimize the user experience of the interface to ensure ease of use and accessibility. S602. Generate charts: Generate chart types based on the analysis results, such as bar charts, to show the trend of nursing efficiency. Secondly, custom colors and labels enhance readability and highlight key insights; finally, the chart update process is automated and integrated with real-time data sources. S603, Interactive Functions: Add user interaction elements such as filters and drill-down functions to allow caregivers to explore detailed data; secondly, implement alarm and notification mechanisms to automatically trigger when anomalies are detected; finally, support report export and sharing functions to promote team collaboration.

8. The clinical nursing data analysis method based on big data according to claim 7, characterized in that, Regarding S7, it is important to note that ethical compliance must be ensured during the application and implementation phase, and patient consent and institutional approval must be obtained. At the same time, continuous training and support should be provided to help nursing staff adapt to the new tool. In addition, monitoring implementation impacts the process, and timely adjustments should be made to minimize workflow disruptions.

9. A big data-based clinical nursing data analysis system, applied to the big data-based clinical nursing data analysis method of claim 8, characterized in that, include: Heterogeneous data acquisition module (1) integrates multi-source data and automatically acquires structured and unstructured data such as vital signs and nursing records through API and real-time streaming technology, supporting multi-scenario coverage and verifying data reliability; The intelligent preprocessing module (2) automatically cleans up missing and outlier values, standardizes numerical features, encodes categorical variables, performs feature engineering to extract key indicators, reduces noise interference by dimensionality reduction, and ensures data quality and analyzability. The secure storage management module (3) uses a distributed database or cloud storage system to store data, designs a partitioned index strategy to optimize query efficiency, implements encrypted backup and access control, and ensures high data availability, privacy security and disaster recovery capabilities. The multimodal analysis engine module (4) integrates algorithms such as clustering, regression, and time series analysis to mine nursing patterns and risk factors; it also integrates clinical guidelines to generate decision reports and supports parallel computing to accelerate large-scale data processing. The dynamic model building module (5) automatically selects the optimal model based on the analysis results, trains and tunes the hyperparameters, evaluates the robustness of the model through cross-validation and sensitivity testing, and ensures the generalization ability of the personalized nursing plan. Interactive visualization module (6) creates interactive dashboards that dynamically display indicators such as risk scores and resource utilization; generates visualization charts such as heat maps and line charts; integrates real-time alarms and drill-down functions; and supports report export and sharing. The clinical deployment and feedback module (7) embeds models and tools into the nursing workflow, providing personnel training and technical support; Monitor implementation effectiveness, collect feedback to optimize processes, and ensure ethical compliance while minimizing work disruptions; Continuously optimize the evaluation module (8), regularly evaluate the system performance through A / B testing and satisfaction surveys, optimize algorithm parameters or data strategies based on feedback, and iteratively upgrade the system to adapt to new requirements and changes in data sources; The multi-source integration and verification module (9) executes the ETL process to unify the data model, eliminates source conflicts and associates patient full-cycle nursing information, and automatically verifies the integrity and logical consistency of the integrated data.

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