Drawing assistant based on clinical data analysis

Abstract

The invention is suitable for the technical field of intelligent medical treatment, and provides a drawing assistant based on clinical data analysis, which comprises a processor, a data import and preprocessing module, an analysis module, a visual drawing module, a statistical analysis integration module, a simulation comparison module, a layering and clustering module, a generation module, an AI auxiliary analysis module and a storage module are arranged in the processor; the layering and clustering module is used for carrying out layering analysis on patients based on clinical features and carrying out patient clustering by using a machine learning algorithm; the AI auxiliary analysis module is used for integrating an AI model, providing predictive analysis and providing data insight suggestions, and the drawing assistant helps medical professionals to more intuitively understand and analyze clinical data by combining data processing, visualization and clinical medical knowledge, reduces human subjective intervention, integrates the AI model and more advanced analysis functions, and improves the clinical data analysis efficiency. The objectivity of the scheme is further improved, the objectivity of the scheme is improved, and the working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent medical technology, and more specifically, to a drawing assistant based on clinical data analysis. Background Art

[0002] With the development of medical service informatization, more and more medical institutions are accelerating the construction of information-based platforms. The development of information platforms not only improves the work efficiency of medical staff, but also increases the opportunities for communication between doctors and patients.

[0003] At present, medical institutions of all sizes have more or less built independent information systems according to their own needs. The information systems generally record case information, diagnosis and treatment information, test information, etc. of different departments and patients. Doctors make analysis and judgments based on clinical data. However, many decision-making processes in the management system require human intervention to complete, which not only wastes a lot of time and resources, but is also easily affected by personal subjective bias, increasing costs and reducing work efficiency. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a drawing assistant based on clinical data analysis, which helps medical professionals understand and analyze clinical data more intuitively by combining data processing, visualization and clinical medical knowledge, reduces human subjective intervention, improves the objectivity of the plan, and improves work efficiency; saves time through automated data processing and drawing, helps users quickly understand complex data through visualization, supports multiple data formats and analysis requirements, improves the practicality of the assistant, and at the same time, can integrate AI models and more advanced analysis functions to further improve the objectivity of the plan.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A drawing assistant based on clinical data analysis includes a processor; the processor is internally provided with a data import and preprocessing module, an analysis module, a visualization drawing module, a statistical analysis integration module, a simulation control module, a stratification and clustering module, a generation module, an AI-assisted analysis module and a storage module; the visualization drawing module is used to generate survival analysis curves, ROC curves, heat maps and time series analysis; the statistical analysis integration module is used to automatically generate descriptive statistics, support hypothesis testing and provide functions such as regression analysis and correlation analysis; the stratification and clustering module performs stratified analysis on patients based on clinical characteristics and uses a machine learning algorithm to cluster patients; the AI-assisted analysis module is used to integrate AI models, provide predictive analysis, and provide data insight suggestions.

[0007] The present invention is further configured such that: the data import and preprocessing module can support multiple data formats, automatically clean the data in the database, and support standardization and normalization processing to ensure that the data is suitable for analysis.

[0008] The present invention is further configured as follows: the analysis module is composed of a word vector model, a pre-trained risk prediction model, and model selection and tuning; the visualization module is composed of result presentation and interpretation as well as visualization and analysis tools.

[0009] The present invention is further configured as follows: the word vector model converts text data into numerical representation, and at the same time, the pre-trained risk prediction model is trained according to the classification prediction result data and the actual result data in the disease risk prediction model for predicting disease risk, and the model selection and tuning select the best model and perform parameter tuning through cross-validation and grid search technology, and then the results generated by the trained model are visualized so as to intuitively observe and understand the output of the model.

[0010] The present invention is further configured as follows: the visualization drawing module can also be used to generate basic bar graphs and interactive charts; the basic charts include bar graphs, line graphs, scatter graphs, box plots, etc.; the interactive charts support zooming, filtering, and hovering to view data point details.

[0011] The present invention is further configured as follows: the simulation comparison module is used to compare and analyze the charts generated by the drawing module with the data information in the database.

[0012] The present invention is further configured as follows: the generation module is used to automatically generate a visual report and provide a customizable template to facilitate users to quickly generate a report that meets their needs.

[0013] The present invention is further configured as follows: the storage module is used to store the results generated by the trained model in a database.

[0014] The advantages of the present invention are:

[0015] 1. The present invention combines data processing, visualization and clinical medical knowledge to help medical professionals understand and analyze clinical data more intuitively, reduce human subjective intervention, improve the objectivity of the plan, and improve work efficiency.

[0016] 2. The present invention saves time by automating data processing and drawing, helps users quickly understand complex data through visualization, supports multiple data formats and analysis requirements, improves the practicality of the assistant, and can integrate AI models and more advanced analysis functions to further improve the objectivity of the solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1This is a framework diagram of a drawing assistant system based on clinical data analysis of the present invention.

[0018] Figure 2 It is a block diagram of the composition of the analysis module of the present invention.

[0019] Figure 3 It is a block diagram of the composition of the visual drawing module of the present invention. DETAILED DESCRIPTION

[0020] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meanings as commonly understood by ordinary technicians in the technical field to which this application belongs.

[0022] In the present invention, unless otherwise specified, the directions used, such as "up" and "down", usually refer to the directions shown in the drawings, or to the vertical, perpendicular or gravity directions; similarly, for ease of understanding and description, "left" and "right" usually refer to the left and right shown in the drawings; "inside" and "outside" refer to the inside and outside relative to the outline of each component itself, but the above-mentioned directions are not used to limit the present invention.

[0023] For example, see Figure 1-3 , the present invention provides the following technical solutions:

[0024] A drawing assistant based on clinical data analysis, specifically, includes a processor; the processor is internally provided with a data import and preprocessing module, an analysis module, a visualization drawing module, a statistical analysis integration module, a simulation control module, a stratification and clustering module, a generation module, an AI-assisted analysis module and a storage module; the visualization drawing module is used to generate survival analysis curves, ROC curves, heat maps and time series analysis; the statistical analysis integration module is used to automatically generate descriptive statistics, support hypothesis testing and provide functions such as regression analysis and correlation analysis; the stratification and clustering module is to perform stratified analysis on patients based on clinical characteristics, and at the same time, use machine learning algorithms to cluster patients; the AI-assisted analysis module is used to integrate AI models, provide predictive analysis, and at the same time, provide data insight suggestions.

[0025] Working principle of the first embodiment:

[0026] By combining data processing, visualization and clinical medical knowledge, it helps medical professionals understand and analyze clinical data more intuitively, reduces subjective human intervention, improves the objectivity of the plan, and improves work efficiency; by automating data processing and drawing, it saves time, helps users quickly understand complex data through visualization, supports multiple data formats and analysis requirements, and improves the practicality of the assistant. At the same time, it can integrate AI models and more advanced analysis functions to further improve the objectivity of the plan.

[0027] For example 2, please refer to Figure 1-3 , the second embodiment makes the following improvements on the basis of the first embodiment. Specifically, the data import and preprocessing module can support multiple data formats, automatically clean the data in the database, and support standardization and normalization processing to ensure that the data is suitable for analysis.

[0028] The analysis module consists of a word vector model, a pre-trained risk prediction model, and model selection and tuning; the visualization module consists of result presentation and interpretation as well as visualization and analysis tools.

[0029] The word vector model converts text data into numerical representation. At the same time, the pre-trained risk prediction model is trained based on the classification prediction result data and actual result data in the disease risk prediction model to predict disease risk. Model selection and tuning select the best model and perform parameter tuning through cross-validation and grid search technology. The results generated by the trained model are then visualized to intuitively observe and understand the output of the model.

[0030] Working principle of the second embodiment:

[0031] The data import and preprocessing module can support multiple data formats, such as CSV, Excel, SQL database, DICOM, etc.; the data import and preprocessing module can automatically clean data, such as processing missing values, outliers, and duplicate data.

[0032] The pre-trained risk prediction model is used to show the contribution of each data feature to the prediction result. The SHAP (SHapley Additive exPlanations) value is used to explain the impact of each feature on the individual prediction result. At the same time, local interpretability analysis is provided for the prediction results of a single patient.

[0033] SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. SHAP bridges game theory with local explanations, uniting several previous approaches and representing the only possible consistent and locally accurate additive feature attribution method based on expectations.

[0034] At the same time, the pre-trained risk prediction model is trained according to the classified prediction result data and actual result data in the disease risk prediction model to predict disease risk. Model selection and tuning select the best model and perform parameter tuning through cross-validation and grid search technology. The results generated by the trained model are then visualized to intuitively observe and understand the output of the model.

[0035] For example 3, please refer to Figure 1-3 , this third embodiment makes the following improvements on the basis of the second embodiment. Specifically, the visualization drawing module can also be used to generate basic bar charts and interactive charts; basic charts include bar charts, line charts, scatter plots, box plots, etc.; interactive charts support zooming, filtering, and hovering to view data point details.

[0036] The simulation comparison module is used to compare and analyze the charts generated by the drawing module with the data information in the database.

[0037] The generation module is used to automatically generate visual reports and provide customizable templates to facilitate users to quickly generate reports that meet their needs.

[0038] The storage module is used to store the results generated by the trained model in the database.

[0039] Working principle of the third embodiment:

[0040] Survival analysis curve (Kaplan-Meier curve); ROC curve for diagnostic performance evaluation; heat map for gene expression or patient feature clustering; time series analysis for analysis of changes in patients' vital signs; automatic generation of descriptive statistics such as mean, median, and standard deviation; support for hypothesis testing such as t-test and chi-square test; stratification analysis of patients based on clinical characteristics such as age, gender, and disease stage; use of machine learning algorithms such as K-means and PCA for patient clustering; integrated AI models to provide predictive analysis such as disease risk prediction and treatment effect prediction; provide data insight suggestions such as outlier detection and trend analysis.

[0041] The present invention visualizes clinical trial data, analyzes treatment effects, compares survival rates or side effects of different treatment options, analyzes patient admission, discharge, and hospital stay data, and visualizes hospital resource usage such as bed occupancy rate and operating room utilization rate; tracks changing trends in patient health indicators such as blood pressure and blood sugar, generates personalized reports, and helps doctors communicate with patients; analyzes disease incidence and epidemic trends, and visualizes geographic distribution data such as epidemic maps.

[0042] In summary, the drawing assistant based on clinical data analysis in this application has the following advantages:

[0043] 1. Efficiency: Automated data processing and drawing save time.

[0044] 2. Intuitiveness: Help users quickly understand complex data through visualization.

[0045] 3. Flexibility: Supports multiple data formats and analysis requirements.

[0046] 4. Scalability: AI models and more advanced analytical functions can be integrated.

[0047] Through this drawing assistant based on clinical data analysis, medical professionals can more efficiently mine the value of data to assist in decision-making and research.

[0048] Obviously, the above-described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0049] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0050] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0052] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A drawing assistant based on clinical data analysis, comprising a processor; characterized in that: The processor is internally provided with a data import and preprocessing module, an analysis module, a visualization drawing module, a statistical analysis integration module, a simulation control module, a stratification and clustering module, a generation module, an AI-assisted analysis module and a storage module; The visualization drawing module is used to generate survival analysis curves, ROC curves, heat maps and time series analysis; The statistical analysis integration module is used to automatically generate descriptive statistics, support hypothesis testing, and provide functions such as regression analysis and correlation analysis; The stratification and clustering module performs stratified analysis on patients based on clinical characteristics and uses machine learning algorithms to cluster patients; The AI-assisted analysis module is used to integrate AI models, provide predictive analysis, and provide data insight suggestions.

2. A drawing assistant based on clinical data analysis according to claim 1, characterized in that: The data import and preprocessing module can support multiple data formats, automatically clean the data in the database, and support standardization and normalization processing to ensure that the data is suitable for analysis.

3. A drawing assistant based on clinical data analysis according to claim 2, characterized in that: The analysis module consists of a word vector model, a pre-trained risk prediction model, and model selection and tuning; the visualization module consists of result presentation and interpretation as well as visualization and analysis tools.

4. A drawing assistant based on clinical data analysis according to claim 3, characterized in that: The word vector model converts text data into numerical representation. At the same time, the pre-trained risk prediction model is trained according to the classification prediction result data and the actual result data in the disease risk prediction model to predict disease risk. Model selection and tuning select the best model and perform parameter tuning through cross-validation and grid search technology, and then visualize the results generated by the trained model to intuitively observe and understand the output of the model.

5. A drawing assistant based on clinical data analysis according to claim 4, characterized in that: The visualization drawing module can also be used to generate basic bar charts and interactive charts; the basic charts include bar charts, line charts, scatter plots, box plots, etc.; the interactive charts support zooming, filtering, and hovering to view data point details.

6. A drawing assistant based on clinical data analysis according to claim 5, characterized in that: The simulation comparison module is used to compare and analyze the chart generated by the drawing module with the data information in the database.

7. A drawing assistant based on clinical data analysis according to claim 6, characterized in that: The generation module is used to automatically generate visual reports and provide customizable templates to facilitate users to quickly generate reports that meet their needs.

8. A drawing assistant based on clinical data analysis according to claim 7, characterized in that: The storage module is used to store the results generated by the trained model in a database.

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