A clinical data intelligent processing, analysis and management system and method
By designing an intelligent clinical data processing and analysis management system, using artificial intelligence technology and big data analysis, it solves the problem that traditional methods are difficult to integrate and analyze multiple types of clinical data, and achieves efficient processing and in-depth analysis, supporting the innovative development of medical services and research.
Patent Information
- Application Number
- CN202411555044.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-04
- Publication Date
- 2025-04-04
AI Technical Summary
Traditional clinical data analysis methods are difficult to effectively integrate and manage multiple types of clinical data in a unified manner, and cannot quickly adapt to the automatic update of big data, resulting in inefficient data mining and analysis, and cannot meet the refined and intelligent needs of the medical field.
A clinical data intelligent processing and analysis management system is designed, including data processing module, model selection module, model training module, model verification module, model optimization module, model storage module, trend prediction module, association analysis module, decision support module and result visualization module. Through artificial intelligence technology and big data analysis, efficient data processing and in-depth analysis can be achieved.
The system can quickly integrate and unified management of clinical data, automatically optimize analysis models, improve data analysis and mining speed, realize efficient processing and in-depth analysis of clinical data, and support the improvement of medical service quality and innovative development of medical research.
Smart Images
Figure CN119763846A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of clinical data processing, analysis and management, and in particular to a clinical data intelligent processing, analysis and management system and method. Background Art
[0002] In today's digital age, the medical field is undergoing unprecedented changes. The rapid advancement of information technology has led to an exponential growth in the generation and accumulation of medical data, and clinical data has become a vital resource in medical services and research.
[0003] With the continuous updating of medical equipment and the widespread application of medical information systems, the sources of clinical data are becoming more and more diverse, including electronic medical records, medical images, laboratory test results, patient monitoring equipment data, etc. These data not only have different formats, but also cover a variety of types such as structured, semi-structured and unstructured, making data integration and unified management extremely difficult. In addition, since traditional analysis methods cannot automatically update model algorithms based on big data, it is difficult to effectively mine and analyze these data within an acceptable time.
[0004] With the deepening of medical research and the improvement of patient needs, simple descriptive analysis of data can no longer meet the needs. Medical workers and researchers need to discover potential disease associations from data, predict the development trend of diseases, evaluate treatment effects, etc., in order to develop more personalized and accurate medical plans. However, traditional methods often lack effective means and tools when performing these in-depth analysis and prediction tasks, and it is difficult to meet the growing needs of refinement and intelligence in the medical field.
[0005] In this context, there is an urgent need for an innovative and powerful clinical data processing and analysis management system and method that can fully integrate and utilize big data resources, leverage the advantages of artificial intelligence technology, achieve efficient processing and in-depth analysis of clinical data, and provide strong support for improving the quality of medical services and the innovative development of medical research. Summary of the invention
[0006] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a clinical data intelligent processing and analysis management system and method.
[0007] In order to achieve the above object, the present invention adopts the following technical solutions:
[0008] A clinical data intelligent processing and analysis management system and method, comprising a data processing module, a model selection module, a model training module, a model verification module, a model optimization module, a model storage module, a trend prediction module, a correlation analysis module, a decision support module and a result visualization module, wherein the data processing module is used to collect and store clinical data, the model selection module is used to select a suitable artificial intelligence model according to data characteristics and analysis objectives, the model training module is used to train the selected model using a large amount of training data, the model verification module is used to verify the performance of the model on new data, the model optimization module is used to adjust model parameters based on verification results, the model storage module is used to store artificial intelligence models, the trend prediction module is used to predict disease development trends using methods such as time series analysis, the correlation analysis module is used to mine potential correlations between data, the decision support module provides medical personnel with decision suggestions and treatment plan recommendations based on big data analysis and artificial intelligence reasoning according to the results of the trend prediction module and the correlation analysis module, and the result visualization module displays the complex analysis results of the trend prediction module and the correlation analysis module in the form of clear and intuitive charts, graphs, etc.
[0009] Preferably, the data processing module includes a big data acquisition module, a data standardization module, a data classification module, a feature extraction module, a data fusion module, a data compression module and a data storage module. The big data acquisition module widely collects various types of clinical data from various data sources such as hospital information systems, electronic medical records, medical sensors, etc. The data standardization module uniformly converts the data collected by the big data acquisition module into a standard format. The data classification module classifies the data according to disease type, patient age, gender and other standards. The feature extraction module uses deep learning and other technologies to extract key features of the data. The data fusion module is used to integrate heterogeneous data from different systems and devices. The data compression module uses efficient algorithms to compress data and save storage space. The data storage module is used to store processed clinical data and provide data for the model selection module.
[0010] Preferably, the data processing module further includes a data encryption module, and the data encryption module is used to encrypt the data compressed by the data compression module.
[0011] Preferably, the data processing module further comprises a data quality assessment module, and the data quality assessment module periodically assesses the integrity, accuracy and consistency of the data stored in the data storage module.
[0012] Preferably, the calculation formula of the feature extraction module is: Among them, Z ij is the element of the output feature map, W mn is the element of the convolution kernel, X i+m+j+nare the elements of the input image and b is the bias term.
[0013] Preferably, the model storage module includes a convolutional neural network, a deep belief network and a support vector machine.
[0014] Preferably, the data quality assessment module includes completeness assessment and accuracy assessment. The formula for completeness assessment is: missing rate = number of missing fields / total number of fields. The formula for accuracy assessment is: Among them, y i is the true value, is the predicted value and n is the number of samples.
[0015] Preferably, the calculation formula of the model training module is: Among them, y i is the true label, It is a prediction probability.
[0016] Preferably, the calculation formula of the trend prediction module is: t+1 =αY t +(1-α)F t , where Y t is the actual value at the current time point, F t is the predicted value at the current time point, and α is the smoothing parameter (0<α<1).
[0017] Preferably, the calculation formula of the association analysis module is: support = number of transactions containing the item set / total number of transactions.
[0018] Preferably, a clinical data intelligent processing and analysis management system and method comprises the following steps: S1, collecting clinical data through a big data acquisition module, and unifying the format of clinical data through a data standardization module, S2, classifying the clinical data in a unified format through a data classification module, and extracting data features through a feature extraction module, S3, fusing the classified data according to features through a data fusion module, S4, compressing the fused data through a compression module, and encrypting the compressed data through a data encryption module, S5, storing the encrypted data in a data storage module, and regularly evaluating the integrity, accuracy and consistency of the data stored in the data storage module through a data quality assessment module, S6, selecting a suitable model from the model storage module according to the data stored in the data storage module by a model selection module, and inputting the selected model into a model training module, S7, the model training module performs a data analysis based on the data stored in the data storage module, and the model selection ... The model is trained according to the data stored in the storage module, and the performance of the trained model on new data is verified by the model verification module to evaluate its accuracy and reliability. If the verification is qualified, the model optimization module optimizes the model stored in the model storage module according to the training results of the model training module. If it is unqualified, the model optimization module will not optimize the model stored in the model storage module, and the model will be retrained through the model training module. S8. The trend prediction module uses the optimized model and processed data to predict the development trend of the disease, etc. At the same time, the association analysis module mines the potential associations between data according to the optimized model and the processed data, and discovers hidden relationships and patterns. S9. The decision support module generates suggestions and treatment plans based on the data analyzed by the trend prediction module and the association analysis module. At the same time, the complex analysis results are displayed in the form of clear and intuitive charts and graphics through the result visualization module.
[0019] Compared with the prior art, the present invention has the following beneficial effects:
[0020] (1) The present invention can convert a large amount of clinical data into a standardized format through the data standardization module in the data processing module, thereby simplifying the integration and unified management of the data. At the same time, through the mutual cooperation between the model selection module, the model storage module, the model training module, the model verification module and the model optimization module, the model in the system can be automatically optimized according to the big data, and the system can accelerate the analysis and mining of data by optimizing the model, so that the system can effectively mine and analyze clinical data within an acceptable time.
[0021] (2) The present invention uses a trend prediction module and an association analysis module to enable the system to discover potential disease associations from clinical data and predict the development trend of the disease, thereby achieving efficient processing and in-depth analysis of clinical data, providing strong support for improving the quality of medical services and the innovative development of medical research. At the same time, the decision support module can quickly and accurately provide medical personnel with key support such as diagnostic assistance and treatment plan recommendations, helping medical personnel to make wise decisions more timely, and the result visualization module can display complex analysis results in the form of clear and intuitive charts, graphs, etc., making it easier for medical personnel to view the analysis results. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Figure 1 It is a principle block diagram of the present invention;
[0023] Figure 2 It is a principle block diagram of the data processing module in the present invention;
[0024] Figure 3 It is a schematic diagram of the model storage module in the present invention. DETAILED DESCRIPTION
[0025] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.
[0026] Reference Figure 1-3 A clinical data intelligent processing and analysis management system and method, including a data processing module, a model selection module, a model training module, a model verification module, a model optimization module, a model storage module, a trend prediction module, a correlation analysis module, a decision support module and a result visualization module, the data processing module is used to collect and store clinical data, the model selection module is to select a suitable artificial intelligence model according to data characteristics and analysis objectives, the model training module is to use a large amount of training data to train the selected model, the model verification module is to verify the performance of the model on new data, the model optimization module is to adjust the model parameters based on the verification results, the model storage module is to store the artificial intelligence model, the trend prediction module is to use time series analysis and other methods to predict the development trend of the disease, the correlation analysis module is to mine the potential correlation between data, the decision support module provides medical personnel with decision suggestions and treatment plan recommendations based on big data analysis and artificial intelligence reasoning according to the results of the trend prediction module and the correlation analysis module, and the result visualization module displays the complex analysis results of the trend prediction module and the correlation analysis module in the form of clear and intuitive charts, graphics, etc.
[0027] The data processing module includes big data acquisition module, data standardization module, data classification module, feature extraction module, data fusion module, data compression module and data storage module. The big data acquisition module widely collects various types of clinical data from various data sources such as hospital information systems, electronic medical records, medical sensors, etc. The data standardization module uniformly converts the data collected by the big data acquisition module into a standardized format. The data classification module classifies the data according to disease type, patient age, gender and other standards. The feature extraction module uses deep learning and other technologies to extract key features of the data. The data fusion module is used to integrate heterogeneous data from different systems and devices. The data compression module uses efficient algorithms to compress data and save storage space. The data storage module is used to store processed clinical data and provide data for the model selection module.
[0028] The data processing module also includes a data encryption module, which is used to encrypt the data compressed by the data compression module.
[0029] The data processing module also includes a data quality assessment module, which regularly assesses the integrity, accuracy and consistency of the data stored in the data storage module.
[0030] The calculation formula of the feature extraction module is: Among them, Z ij is the element of the output feature map, W mn is the element of the convolution kernel, X i+m+j+n are the elements of the input image and b is the bias term.
[0031] The model storage modules include convolutional neural networks, deep belief networks, and support vector machines.
[0032] The data quality assessment module includes completeness assessment and accuracy assessment. The formula for completeness assessment is: missing rate = number of missing fields / total number of fields. The formula for accuracy assessment is: Among them, y i is the true value, is the predicted value and n is the number of samples.
[0033] The calculation formula of the model training module is: Among them, y i is the true label, It is a prediction probability.
[0034] The calculation formula of the trend prediction module is F t+1 =αY t +(1-α)F t , where Y t is the actual value at the current time point, F t is the predicted value at the current time point, and α is the smoothing parameter (0<α<1).
[0035] The calculation formula of the association analysis module is: support = number of transactions containing the item set / total number of transactions.
[0036] A clinical data intelligent processing and analysis management system and method, comprising the following steps: S1, collecting clinical data through a big data acquisition module, and unifying the format of clinical data through a data standardization module, S2, classifying the clinical data in the unified format through a data classification module, and extracting data features through a feature extraction module, S3, fusing the classified data according to features through a data fusion module, S4, compressing the fused data through a compression module, and encrypting the compressed data through a data encryption module, S5, storing the encrypted data in a data storage module, and regularly evaluating the integrity, accuracy and consistency of the data stored in the data storage module through a data quality assessment module, S6, a model selection module selects a suitable model from a model storage module according to the data stored in the data storage module, and inputs the selected model into a model training module, S7, the model training module selects a suitable model from a model storage module according to the data stored in the data storage module, and inputs the selected model into a model training module, S8, the model training module selects a suitable model from a model storage module according to the data stored in the data storage module, and inputs the selected model into a model training module, S9, the model training module selects a suitable model from a model storage module according to the data stored in the data storage module, and inputs the selected model into a model training module, S10, the model training module selects a suitable model from a model storage module according to the data stored in the data storage module, and inputs the selected model into a model training module, S11, the model training module selects a suitable model from a model storage module according to the data stored in the data storage module, and inputs the selected model into a model training module, S12, the model training module selects a suitable model from a model storage module according to the data stored in the data storage module, and inputs the selected model into a model training module, S13, the model training module selects a suitable model from a model storage module according to the data stored in the data storage module, and inputs the selected model into a model training module, S14, the model training module selects a suitable model from a model storage module according to the data stored in the data storage module, The model is trained with the data stored in the storage module, and the performance of the trained model on new data is verified by the model verification module to evaluate its accuracy and reliability. If the verification is qualified, the model optimization module optimizes the model stored in the model storage module according to the training results of the model training module. If it is unqualified, the model optimization module will not optimize the model stored in the model storage module, and retrain the model through the model training module. S8. The trend prediction module uses the optimized model and processed data to predict the development trend of the disease, etc. At the same time, the association analysis module mines the potential associations between data according to the optimized model and the processed data, and discovers hidden relationships and patterns. S9. The decision support module generates suggestions and treatment plans based on the data analyzed by the trend prediction module and the association analysis module. At the same time, the complex analysis results are displayed in the form of clear and intuitive charts and graphics through the result visualization module.
[0037] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.
[0038] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
Claims
1. A clinical data intelligent processing and analysis management system and method, comprising a data processing module, a model selection module, a model training module, a model verification module, a model optimization module, a model storage module, a trend prediction module, a correlation analysis module, a decision support module and a result visualization module, characterized in that: The data processing module is used to collect and store clinical data, the model selection module is to select a suitable artificial intelligence model according to data characteristics and analysis objectives, the model training module is to use a large amount of training data to train the selected model, the model verification module is to verify the performance of the model on new data, the model optimization module is to adjust the model parameters based on the verification results, the model storage module is to store artificial intelligence models, the trend prediction module is to use time series analysis and other methods to predict the development trend of the disease, the association analysis module is to explore the potential associations between data, the decision support module provides medical personnel with decision suggestions and treatment plan recommendations based on big data analysis and artificial intelligence reasoning according to the results of the trend prediction module and the association analysis module, and the result visualization module displays the complex analysis results of the trend prediction module and the association analysis module in the form of clear and intuitive charts, graphs, etc.
2. A clinical data intelligent processing, analysis and management system and method according to claim 1, characterized in that: The data processing module includes a big data acquisition module, a data standardization module, a data classification module, a feature extraction module, a data fusion module, a data compression module and a data storage module. The big data acquisition module widely collects various types of clinical data from various data sources such as hospital information systems, electronic medical records, medical sensors, etc. The data standardization module uniformly converts the data collected by the big data acquisition module into a standardized format. The data classification module classifies the data according to disease type, patient age, gender and other standards. The feature extraction module uses deep learning and other technologies to extract key features of the data. The data fusion module is used to integrate heterogeneous data from different systems and devices. The data compression module uses efficient algorithms to compress data and save storage space. The data storage module is used to store processed clinical data and provide data for the model selection module.
3. A clinical data intelligent processing, analysis and management system and method according to claim 2, characterized in that: The data processing module also includes a data encryption module, which is used to encrypt the data compressed by the data compression module.
4. A clinical data intelligent processing, analysis and management system and method according to claim 2, characterized in that: The data processing module also includes a data quality assessment module, which regularly assesses the integrity, accuracy and consistency of the data stored in the data storage module.
5. A clinical data intelligent processing, analysis and management system and method according to claim 2, characterized in that: The calculation formula of the feature extraction module is: Among them, Z ij is the element of the output feature map, W mn is the element of the convolution kernel, X i+m+j+n are the elements of the input image and b is the bias term.
6. A clinical data intelligent processing, analysis and management system and method according to claim 1, characterized in that: The model storage module includes a convolutional neural network, a deep belief network and a support vector machine.
7. A clinical data intelligent processing, analysis and management system and method according to claim 4, characterized in that: The data quality assessment module includes completeness assessment and accuracy assessment. The formula for completeness assessment is: missing rate = number of missing fields / total number of fields. The formula for accuracy assessment is: Among them, y i is the true value, is the predicted value and n is the number of samples.
8. A clinical data intelligent processing, analysis and management system and method according to claim 1, characterized in that: The calculation formula of the model training module is: Among them, y i is the true label, is the predicted probability.
9. A clinical data intelligent processing, analysis and management system and method according to claim 1, characterized in that: The calculation formula of the trend prediction module is: t+1 =αY t +(1-α)F t , where Y t is the actual value at the current time point, F t is the predicted value at the current time point, and α is the smoothing parameter (0<α<1).
10. A clinical data intelligent processing, analysis and management system and method according to claim 1, characterized in that: The calculation formula of the association analysis module is: support = the number of transactions containing the item set / the total number of transactions.
11. A clinical data intelligent processing and analysis management system and method according to claim 1, comprising the following steps: S1, collecting clinical data through a big data acquisition module, and unifying the format of clinical data through a data standardization module, S2, classifying the clinical data in a unified format through a data classification module, and extracting data features through a feature extraction module, S3, fusing the classified data according to features through a data fusion module, S4, compressing the fused data through a compression module, and encrypting the compressed data through a data encryption module, S5, storing the encrypted data in a data storage module, and regularly evaluating the integrity, accuracy and consistency of the data stored in the data storage module through a data quality assessment module, S6, selecting a suitable model from the model storage module according to the data stored in the data storage module by a model selection module, and inputting the selected model into a model training module, S7, model training The module trains the model according to the data stored in the data storage module, and verifies the performance of the trained model on the new data through the model verification module to evaluate its accuracy and reliability. If the verification is qualified, the model optimization module optimizes the model stored in the model storage module according to the training results of the model training module. If it is unqualified, the model optimization module will not optimize the model stored in the model storage module, and retrain the model through the model training module. S8. The trend prediction module uses the optimized model and processed data to predict the development trend of the disease, etc. At the same time, the association analysis module mines the potential associations between data based on the optimized model and processed data, and discovers hidden relationships and patterns. S9. The decision support module generates suggestions and treatment plans based on the data analyzed by the trend prediction module and the association analysis module. At the same time, the complex analysis results are displayed in the form of clear and intuitive charts and graphics through the result visualization module.