Lung inflammation postoperative prediction system based on mycoplasma pneumoniae infection

Abstract

The invention provides a lung inflammation postoperative prediction system based on mycoplasma pneumoniae infection, and relates to the technical field of mycoplasma pneumoniae and computers. The postoperative prediction system comprises a data acquisition module used for collecting basic information of a patient, clinical data after a lung inflammation operation, imaging data and biomarkers; the feature extraction and processing module is used for carrying out standardization and normalization processing on the collected clinical data, extracting key features and carrying out data cleaning, and meanwhile, the module also comprises an image processing module for carrying out image processing on the iconography data; and the multi-dimensional data fusion module is used for carrying out fusion analysis on the multi-dimensional data by utilizing an advanced machine learning algorithm and constructing a postoperative prognosis evaluation model. Through comprehensive consideration of clinical data, iconography data, genome data and other information of the patient, the limitation of a traditional single-index prediction method is broken through, and the accuracy and individuation of prognosis prediction are improved.

Description

Technical Field

[0001] The invention relates to the field of Mycoplasma pneumoniae and computer technology, and in particular to a postoperative prediction system for lung inflammation based on Mycoplasma pneumoniae infection. Background Art

[0002] Pulmonary inflammation caused by Mycoplasma pneumoniae, as a respiratory disease caused by a specific pathogen, has a high incidence rate worldwide, especially in certain regions, posing a serious threat to patients' health. For patients with lung inflammation caused by Mycoplasma pneumoniae who undergo surgical treatment, postoperative prognosis assessment is also a complex and crucial link. Accurately assessing the patient's postoperative survival, risk of recurrence, and possible complications is of vital importance for clinicians to formulate subsequent treatment plans, optimize the allocation of medical resources, and implement personalized management strategies.

[0003] In recent years, with the rapid development of big data and machine learning technologies, many studies have begun to explore the application of these advanced technologies in the prediction of prognosis after surgery for Mycoplasma pneumoniae-infected lung inflammation. These studies attempt to integrate patients' clinical information (such as age, gender, medical history, etc.), imaging data (such as X-rays, CT scans, etc.), biomarker data (such as specific antibody levels, inflammatory factor concentrations, etc.) and possible genomic information to provide more accurate prognostic assessments by building complex prediction models. However, although these models can theoretically output valuable predictive information, how to present these multi-dimensional and complex data and their prediction results to clinicians in an intuitive and easy-to-understand way is still a technical problem that needs to be urgently solved.

[0004] Traditional clinical data reporting methods, such as lengthy text descriptions or cumbersome tabular data, often make it difficult for clinicians to quickly grasp the key points and make timely and accurate decisions. In this context, data visualization technology has gradually emerged in the medical field with its graphical and interactive display advantages. Through a carefully designed visualization interface, doctors can more intuitively understand the patient's various clinical data, model prediction results, and the potential correlations between them, thereby providing strong support for the formulation of more accurate and personalized treatment plans. Therefore, a postoperative prediction system for lung inflammation based on Mycoplasma pneumoniae infection is developed to solve the problems raised in the above background technology. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides a postoperative prediction system for lung inflammation based on Mycoplasma pneumoniae infection, which solves the problem that existing digital reports and tables make it difficult for doctors to quickly and intuitively understand the patient's condition and prognosis.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a postoperative prediction system for pulmonary inflammation based on Mycoplasma pneumoniae infection, comprising:

[0009] Data collection module, used to collect basic information of patients, clinical data after lung inflammation surgery, imaging data and biomarkers;

[0010] The feature extraction and processing module standardizes and normalizes the collected clinical data, extracts key features and performs data cleaning. This module also includes image processing of imaging data to extract lung morphological features;

[0011] Multi-dimensional data fusion module uses advanced machine learning algorithms to perform fusion analysis on multi-dimensional data and build a postoperative prognosis evaluation model;

[0012] The prognosis prediction module outputs the patient's postoperative survival prediction, recurrence risk assessment and complication probability through the model, providing a decision support tool for clinicians;

[0013] The data visualization module is used to present the prediction results to clinicians in the form of charts and curves, facilitating intuitive analysis and decision-making.

[0014] Preferably, the data acquisition module comprises the following steps:

[0015] S1. Collect basic information of patients, including gender, age, weight, height, race and family medical history;

[0016] S2. Preoperative clinical data collection, including lung function, lung status and other key clinical indicators of preoperative patients, providing preliminary information about the patient's pneumonia and overall health status;

[0017] S3. Postoperative clinical data collection, regular postoperative tracking of the patient's health status and lung function, monitoring of postoperative recovery and complications, recording the occurrence of postoperative complications, monitoring and recording the use and side effects of immunosuppressants;

[0018] S4. Postoperative imaging data collection. Postoperative imaging data is used to monitor the patient's lung status and any other postoperative complications. Regular CT or MRI scans of the patient's lungs are performed to evaluate the postoperative effect and possible signs of recurrence. Regular postoperative ultrasound examinations are performed to check for effusion and recurrence.

[0019] S5. Biomarker and molecular data collection: collect patients' molecular biological data, including genomic data, and detect the level of specific antibodies against Mycoplasma pneumoniae in patients, including IgG, IgM and miRNA-inflammatory factor regulatory clusters, for postoperative recurrence risk prediction and prognosis assessment;

[0020] S6. Patient follow-up data collection: Based on the patient's postoperative follow-up records, continue to track the patient's postoperative recovery, complications and recurrence to ensure that the system can update the patient's prognosis information in real time.

[0021] Preferably, the feature extraction and processing module comprises the following steps:

[0022] S1. Data cleaning and preprocessing, through missing value processing, outlier detection and processing and data duplication check, ensure the premise of the validity of subsequent analysis, with the purpose of removing invalid, missing or erroneous records;

[0023] S2. Feature selection: Use chi-square test, t-test or ANOVA method to evaluate the correlation between each feature and the target variable, select the most significant features, perform correlation analysis on numerical features, remove features with high correlation, avoid information redundancy, perform heat map analysis on categorical features, evaluate the correlation between features, evaluate the importance of features through machine learning algorithms, and recursively delete unimportant features based on the importance of features. Use tree-based models to evaluate feature importance and select features that have a greater impact on postoperative prognosis.

[0024] S3. Feature engineering, the process of extracting, transforming and combining features from raw data, with the goal of providing more representative information for the prediction model, including numerical feature processing, categorical feature processing, time series feature processing and imaging data processing;

[0025] S4. Feature construction: Create new features based on existing data that can better reflect postoperative prognosis, including biomarker combinations, immunosuppressant use, and survival prediction features;

[0026] S5. Data integration and fusion: integrate features from different sources to create a multimodal dataset to provide more information for the model, integrate clinical data, imaging data and biomarkers into a comprehensive dataset, and use convolutional neural networks for joint analysis of multimodal data;

[0027] S6. Feature evaluation and model input. A tree-based feature importance evaluation method was used to evaluate the impact of each feature on postoperative prognosis prediction, assess the correlation between features, and avoid multicollinearity problems.

[0028] Preferably, the multi-dimensional data fusion module comprises the following steps:

[0029] S1. Data preprocessing and standardization: clean each data type, including missing value filling, outlier processing and data duplication check. Clinical data, imaging data and biomarkers should be cleaned separately to ensure the quality of each data set, and perform data standardization and normalization.

[0030] S2. Feature extraction and representation, extracting clinical data, imaging data and biomarker data respectively;

[0031] S3. Alignment of multimodal data. For clinical data involving time series, ensure that the timestamps corresponding to each data point are consistent. For imaging data and biomarker data, if multiple measurements are taken, they need to be time aligned to ensure that data of different modalities correspond to the same time nodes. Imaging data usually has spatial information, and it is necessary to ensure that the location of lung inflammation in the imaging data is consistent with the location information in the clinical or biological data.

[0032] S4. Data fusion method selection: early fusion, late fusion, mid-term fusion and weighted fusion can be selected according to specific usage needs to merge data from different sources into a unified feature set;

[0033] S5. Model training and optimization. After completing data fusion, the fused features can be input into the prediction model for training and optimization.

[0034] S6. Evaluation and validation: Use traditional evaluation indicators to evaluate the performance of the model on different data sets, pay special attention to the accuracy of the model in postoperative prognosis prediction, and use an independent validation set to validate the model to ensure that the fused model has good generalization ability;

[0035] S7. Deployment and Application,The trained and optimized multi-dimensional data fusion model can be deployed in the clinical system for practical application, helping doctors to conduct postoperative prognosis assessment.

[0036] Preferably, the prognosis prediction module comprises the following steps:

[0037] S1. Data collection and integration: collecting clinical data, imaging data, case data, and biomarker data;

[0038] S2. Data preprocessing, including data cleaning, data standardization / normalization, and feature engineering, so that it can be input into the prediction model;

[0039] S3. Feature selection and dimensionality reduction: Use correlation analysis, chi-square test and information gain methods to select the features most relevant to prognosis, use regularization methods for feature selection, reduce the complexity of the model, and use PCA to reduce the dimensionality of high-dimensional data, reducing the data dimension while retaining as much useful information as possible;

[0040] S4. Model selection, predicting the patient's prognosis based on the training data, including but not limited to the following models: logistic regression, support vector machine, random forest and K nearest neighbor algorithm;

[0041] S5. Model training and optimization: Use the collected training data to train the model, use grid search to tune the model's hyperparameters to further improve model performance, and use L1 / L2 regularization and Dropout methods to prevent overfitting;

[0042] S6. Model evaluation, including the following evaluation indicators: accuracy, ROC curve and AUC value and time-to-time analysis;

[0043] S7. Model validation: Use independent data sets to ensure the stability of the model under different data sources and environments. For prognostic prediction with time series data, use time windows to verify the stability of the model in different time periods.

[0044] S8. Model deployment and application. The validated model can be deployed into the clinical decision support system to provide real-time postoperative prognosis assessment for patients.

[0045] Preferably, the data visualization module comprises the following steps:

[0046] S1. Determine the visualization goals. In the prediction system after lung surgery, the visualization goals include displaying the patient's clinical information, displaying the survival rate and recurrence risk, displaying the postoperative prognosis prediction results, and displaying the comprehensive evaluation of multimodal data;

[0047] S2. Data preparation and organization, preprocessing steps include data cleaning, data conversion, and data aggregation and statistics;

[0048] S3. Choose the appropriate visualization type. According to different data characteristics and preset goals, choose the appropriate visualization chart type, including basic statistical data display, survival analysis and risk assessment, multimodal data display and prediction result visualization.

[0049] S4. Implement interactive visualization, including filtering and transitioning, zooming in and out, mouse hovering to display detailed information and dynamic charts;

[0050] S5. Choose visualization tools and platforms, including Matlotlib / Seaborn, Plotly, Tableau / Power BI, Dash, and D3.js;

[0051] S6. Evaluation and optimization of visualization results, based on user feedback, chart clarity and performance evaluation;

[0052] S7. Deployment and integration. When deploying, you need to consider the system's stability, security, and seamless integration with other modules.

[0053] (III) Beneficial effects

[0054] The present invention provides a postoperative prediction system for lung inflammation based on Mycoplasma pneumoniae infection. It has the following beneficial effects:

[0055] 1. In the present invention, the patient's clinical data, imaging data, genomic data and other information are comprehensively considered, breaking through the limitations of the traditional single indicator prediction method and improving the accuracy and personalization of prognosis prediction.

[0056] 2. In the present invention, by combining machine learning technology, it is possible to mine potential rules and interactive relationships from massive data, improve the accuracy of prognosis prediction, and show strong adaptability and flexibility in the analysis of complex cases. The prediction results can be updated in real time, and the prognosis evaluation can be dynamically adjusted based on changes in postoperative follow-up data, so as to continuously track and optimize treatment strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the overall system process of the present invention;

[0058] Figure 2 It is a specific flow chart of the data acquisition module in the present invention;

[0059] Figure 3 It is a flow chart of the feature extraction and processing module in the present invention;

[0060] Figure 4 It is a flowchart of the multi-dimensional data fusion module in the present invention;

[0061] Figure 5 Schematic diagram of the process of the prognosis prediction module in the present invention;

[0062] Figure 6 It is a flow chart of the data visualization module in the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Embodiment 1:

[0065] like Figure 1-6 As shown, an embodiment of the present invention provides a postoperative prediction system for pulmonary inflammation based on Mycoplasma pneumoniae infection, comprising:

[0066] Data collection module, used to collect basic information of patients, clinical data after lung inflammation surgery, imaging data and biomarkers;

[0067] The feature extraction and processing module standardizes and normalizes the collected clinical data, extracts key features and performs data cleaning. This module also includes image processing of imaging data to extract lung morphological features;

[0068] Multi-dimensional data fusion module uses advanced machine learning algorithms to perform fusion analysis on multi-dimensional data and build a postoperative prognosis evaluation model;

[0069] The prognosis prediction module outputs the patient's postoperative survival prediction, recurrence risk assessment and complication probability through the model, providing a decision support tool for clinicians;

[0070] The data visualization module is used to present the prediction results to clinicians in the form of charts and curves, facilitating intuitive analysis and decision-making.

[0071] Preferably, the data acquisition module comprises the following steps:

[0072] S1. Collect basic information of patients, including gender, age, weight, height, race and family medical history;

[0073] S2. Preoperative clinical data collection, including lung function, lung status and other key clinical indicators of preoperative patients, providing preliminary information about the patient's pneumonia and overall health status;

[0074] S3. Postoperative clinical data collection, regular postoperative tracking of the patient's health status and lung function, monitoring of postoperative recovery and complications, recording the occurrence of postoperative complications, monitoring and recording the use and side effects of immunosuppressants;

[0075] S4. Postoperative imaging data collection. Postoperative imaging data is used to monitor the patient's lung status and any other postoperative complications. Regular CT or MRI scans of the patient's lungs are performed to evaluate the postoperative effect and possible signs of recurrence. Regular postoperative ultrasound examinations are performed to check for effusion and recurrence.

[0076] S5. Biomarker and molecular data collection: collect patients' molecular biological data, including genomic data, and detect the level of specific antibodies against Mycoplasma pneumoniae in patients, including IgG, IgM and miRNA-inflammatory factor regulatory clusters, for postoperative recurrence risk prediction and prognosis assessment;

[0077] S6. Patient follow-up data collection: Based on the patient's postoperative follow-up records, continue to track the patient's postoperative recovery, complications and recurrence to ensure that the system can update the patient's prognosis information in real time.

[0078] Preferably, the feature extraction and processing module comprises the following steps:

[0079] S1. Data cleaning and preprocessing, through missing value processing, outlier detection and processing and data duplication check, ensure the premise of the validity of subsequent analysis, with the purpose of removing invalid, missing or erroneous records;

[0080] S2. Feature selection: Use chi-square test, t-test or ANOVA method to evaluate the correlation between each feature and the target variable, select the most significant features, perform correlation analysis on numerical features, remove features with high correlation, avoid information redundancy, perform heat map analysis on categorical features, evaluate the correlation between features, evaluate the importance of features through machine learning algorithms, and recursively delete unimportant features based on the importance of features. Use tree-based models to evaluate feature importance and select features that have a greater impact on postoperative prognosis.

[0081] S3. Feature engineering, the process of extracting, transforming and combining features from raw data, with the goal of providing more representative information for the prediction model, including numerical feature processing, categorical feature processing, time series feature processing and imaging data processing;

[0082] S4. Feature construction: Create new features based on existing data that can better reflect postoperative prognosis, including biomarker combinations, immunosuppressant use, and survival prediction features;

[0083] S5. Data integration and fusion: integrate features from different sources to create a multimodal dataset to provide more information for the model, integrate clinical data, imaging data and biomarkers into a comprehensive dataset, and use convolutional neural networks for joint analysis of multimodal data;

[0084] S6. Feature evaluation and model input. A tree-based feature importance evaluation method was used to evaluate the impact of each feature on postoperative prognosis prediction, assess the correlation between features, and avoid multicollinearity problems.

[0085] Preferably, the multi-dimensional data fusion module comprises the following steps:

[0086] S1. Data preprocessing and standardization: clean each data type, including missing value filling, outlier processing and data duplication check. Clinical data, imaging data and biomarkers should be cleaned separately to ensure the quality of each data set, and perform data standardization and normalization.

[0087] S2. Feature extraction and representation, extracting clinical data, imaging data and biomarker data respectively;

[0088] S3. Alignment of multimodal data. For clinical data involving time series, ensure that the timestamps corresponding to each data point are consistent. For imaging data and biomarker data, if multiple measurements are taken, they need to be time aligned to ensure that data of different modalities correspond to the same time nodes. Imaging data usually has spatial information, and it is necessary to ensure that the location of lung inflammation in the imaging data is consistent with the location information in the clinical or biological data.

[0089] S4. Data fusion method selection: early fusion, late fusion, mid-term fusion and weighted fusion can be selected according to specific usage needs to merge data from different sources into a unified feature set;

[0090] S5. Model training and optimization. After completing data fusion, the fused features can be input into the prediction model for training and optimization.

[0091] S6. Evaluation and validation: Use traditional evaluation indicators to evaluate the performance of the model on different data sets, pay special attention to the accuracy of the model in postoperative prognosis prediction, and use an independent validation set to validate the model to ensure that the fused model has good generalization ability;

[0092] S7. Deployment and Application,The trained and optimized multi-dimensional data fusion model can be deployed in the clinical system for practical application, helping doctors to conduct postoperative prognosis assessment.

[0093] Preferably, the prognosis prediction module comprises the following steps:

[0094] S1. Data collection and integration: collecting clinical data, imaging data, case data, and biomarker data;

[0095] S2. Data preprocessing, including data cleaning, data standardization / normalization, and feature engineering, so that it can be input into the prediction model;

[0096] S3. Feature selection and dimensionality reduction: Use correlation analysis, chi-square test and information gain methods to select the features most relevant to prognosis, use regularization methods for feature selection, reduce the complexity of the model, and use PCA to reduce the dimensionality of high-dimensional data, reducing the data dimension while retaining as much useful information as possible;

[0097] S4. Model selection, predicting the patient's prognosis based on the training data, including but not limited to the following models: logistic regression, support vector machine, random forest and K nearest neighbor algorithm;

[0098] S5. Model training and optimization: Use the collected training data to train the model, use grid search to tune the model's hyperparameters to further improve model performance, and use L1 / L2 regularization and Dropout methods to prevent overfitting;

[0099] S6. Model evaluation, including the following evaluation indicators: accuracy, ROC curve and AUC value and time-to-time analysis;

[0100] S7. Model validation: Use independent data sets to ensure the stability of the model under different data sources and environments. For prognostic prediction with time series data, use time windows to verify the stability of the model in different time periods.

[0101] S8. Model deployment and application. The validated model can be deployed into the clinical decision support system to provide real-time postoperative prognosis assessment for patients.

[0102] Preferably, the data visualization module comprises the following steps:

[0103] S1. Determine the visualization goals. In the prediction system after lung surgery, the visualization goals include displaying the patient's clinical information, displaying the survival rate and recurrence risk, displaying the postoperative prognosis prediction results, and displaying the comprehensive evaluation of multimodal data;

[0104] S2. Data preparation and organization, preprocessing steps include data cleaning, data conversion, and data aggregation and statistics;

[0105] S3. Choose the appropriate visualization type. According to different data characteristics and preset goals, choose the appropriate visualization chart type, including basic statistical data display, survival analysis and risk assessment, multimodal data display and prediction result visualization.

[0106] S4. Implement interactive visualization, including filtering and transitioning, zooming in and out, mouse hovering to display detailed information and dynamic charts;

[0107] S5. Choose visualization tools and platforms, including Matlotlib / Seaborn, Plotly, Tableau / Power BI, Dash, and D3.js;

[0108] S6. Evaluation and optimization of visualization results, based on user feedback, chart clarity and performance evaluation;

[0109] S7. Deployment and integration. When deploying, you need to consider the system's stability, security, and seamless integration with other modules.

[0110] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A postoperative prediction system for lung inflammation based on Mycoplasma pneumoniae infection, characterized by: include: The data collection module is used to collect basic information of patients with Mycoplasma pneumoniae infection, clinical data after lung inflammation surgery, imaging data, and biomarkers; The feature extraction and processing module standardizes and normalizes the collected clinical data, extracts key features and performs data cleaning. This module also includes image processing of imaging data to extract lung morphological features; Multi-dimensional data fusion module uses advanced machine learning algorithms to perform fusion analysis on multi-dimensional data and build a postoperative prognosis evaluation model; The prognosis prediction module outputs the patient's postoperative survival prediction, recurrence risk assessment and complication probability through the model, providing a decision support tool for clinicians; The data visualization module is used to present the prediction results to clinicians in the form of charts and curves, facilitating intuitive analysis and decision-making.

2. A postoperative prediction system for pulmonary inflammation based on Mycoplasma pneumoniae infection according to claim 1, characterized in that: The data acquisition module comprises the following steps: S1. Collection of basic patient information: Collect basic demographic information of patients infected with Mycoplasma pneumoniae, including gender, age, weight, height, race, and family medical history; S2. Preoperative clinical data collection: collect lung function, lung status and other key clinical indicators of patients with preoperative Mycoplasma pneumoniae infection to provide preliminary information about the patient's pneumonia and overall health status; S3. Postoperative clinical data collection, regular postoperative tracking of the patient's health status and lung function, monitoring of postoperative recovery and complications, recording the occurrence of postoperative complications, monitoring and recording the use and side effects of immunosuppressants; S4. Postoperative imaging data collection. Postoperative imaging data is used to monitor the patient's lung status and any other postoperative complications. Regular CT or MRI scans of the patient's lungs are performed to evaluate the postoperative effect and possible signs of recurrence. Regular postoperative ultrasound examinations are performed to check for effusion and recurrence. S5. Biomarker and molecular data collection: collect patients' molecular biological data, including genomic data, and detect the level of specific antibodies against Mycoplasma pneumoniae in patients, including IgG, IgM and miRNA-inflammatory factor regulatory clusters, for postoperative recurrence risk prediction and prognosis assessment; S6. Patient follow-up data collection: Based on the patient's postoperative follow-up records, continue to track the patient's postoperative recovery, complications and recurrence to ensure that the system can update the patient's prognosis information in real time.

3. A postoperative prediction system for pulmonary inflammation based on Mycoplasma pneumoniae infection according to claim 1, characterized in that: The feature extraction and processing module comprises the following steps: S1. Data cleaning and preprocessing, through missing value processing, outlier detection and processing and data duplication check, ensure the premise of the effectiveness of subsequent analysis, with the purpose of removing invalid, missing or erroneous records; S2. Feature selection: Use chi-square test, t-test or ANOVA method to evaluate the correlation between each feature and the target variable, select the most significant features, perform correlation analysis on numerical features, remove features with high correlation, avoid information redundancy, perform heat map analysis on categorical features, evaluate the correlation between features, evaluate the importance of features through machine learning algorithms, and recursively delete unimportant features based on the importance of features. Use tree-based models to evaluate feature importance and select features that have a greater impact on postoperative prognosis. S3. Feature engineering, the process of extracting, transforming and combining features from raw data, with the goal of providing more representative information for the prediction model, including numerical feature processing, category feature processing, time series feature processing and imaging data processing; S4. Feature construction: Create new features based on existing data that can better reflect postoperative prognosis, including biomarker combinations, immunosuppressant use, and survival prediction features; S5. Data integration and fusion: integrate features from different sources to create a multimodal dataset to provide more information for the model, integrate clinical data, imaging data and biomarkers into a comprehensive dataset, and use convolutional neural networks for joint analysis of multimodal data; S6. Feature evaluation and model input. A tree-based feature importance evaluation method was used to evaluate the impact of each feature on postoperative prognosis prediction, assess the correlation between features, and avoid multicollinearity problems.

4. The postoperative prediction system for pulmonary inflammation based on Mycoplasma pneumoniae infection according to claim 1, characterized in that: The multi-dimensional data fusion module includes the following steps: S1. Data preprocessing and standardization: clean each data type, including missing value filling, outlier processing and data duplication check. Clinical data, imaging data and biomarkers should be cleaned separately to ensure the quality of each data set, and perform data standardization and normalization. S2. Feature extraction and representation, extracting clinical data, imaging data and biomarker data respectively; S3. Alignment of multimodal data. For clinical data involving time series, ensure that the timestamps corresponding to each data point are consistent. For imaging data and biomarker data, if multiple measurements are taken, they need to be time aligned to ensure that data of different modalities correspond to the same time nodes. Imaging data usually has spatial information, and it is necessary to ensure that the location of lung inflammation in the imaging data is consistent with the location information in the clinical or biological data. S4. Data fusion method selection: early fusion, late fusion, mid-term fusion and weighted fusion can be selected according to specific usage needs to merge data from different sources into a unified feature set; S5. Model training and optimization. After completing data fusion, the fused features can be input into the prediction model for training and optimization. S6. Evaluation and validation: Use traditional evaluation indicators to evaluate the performance of the model on different data sets, pay special attention to the accuracy of the model in predicting postoperative prognosis, and use an independent validation set to validate the model to ensure that the fused model has good generalization ability; S7. Deployment and Application,The trained and optimized multi-dimensional data fusion model can be deployed in the clinical system for practical application, helping doctors to conduct postoperative prognosis assessment.

5. The postoperative prediction system for pulmonary inflammation based on Mycoplasma pneumoniae infection according to claim 1, characterized in that: The prognosis prediction module comprises the following steps: S1. Data collection and integration: collecting clinical data, imaging data, case data, and biomarker data; S2. Data preprocessing, including data cleaning, data standardization / normalization, and feature engineering, so that it can be input into the prediction model; S3. Feature selection and dimensionality reduction: Use correlation analysis, chi-square test and information gain methods to select the features most relevant to prognosis, use regularization methods for feature selection, reduce the complexity of the model, and use PCA to reduce the dimensionality of high-dimensional data, reducing the data dimension while retaining as much useful information as possible; S4. Model selection, predicting the patient's prognosis based on the training data, including but not limited to the following models: logistic regression, support vector machine, random forest and K nearest neighbor algorithm; S5. Model training and optimization: Use the collected training data to train the model, use grid search to tune the model's hyperparameters to further improve model performance, and use L1 / L2 regularization and Dropout methods to prevent overfitting; S6. Model evaluation, including the following evaluation indicators: accuracy, ROC curve and AUC value and time-to-time analysis; S7. Model validation: Use independent data sets to ensure the stability of the model under different data sources and environments. For prognostic prediction with time series data, use time windows to verify the stability of the model in different time periods. S8. Model deployment and application. The validated model can be deployed into the clinical decision support system to provide real-time postoperative prognosis assessment for patients.

6. The postoperative prediction system for pulmonary inflammation based on Mycoplasma pneumoniae infection according to claim 1, characterized in that: The data visualization module includes the following steps: S1. Determine the visualization goals. In the prediction system after lung surgery, the visualization goals include displaying the patient's clinical information, displaying the survival rate and recurrence risk, displaying the postoperative prognosis prediction results, and displaying the comprehensive evaluation of multimodal data; S2. Data preparation and organization, preprocessing steps include data cleaning, data conversion, and data aggregation and statistics; S3. Choose the appropriate visualization type. According to different data characteristics and preset goals, choose the appropriate visualization chart type, including basic statistical data display, survival analysis and risk assessment, multimodal data display and prediction result visualization. S4. Implement interactive visualization, including filtering and transitioning, zooming in and out, mouse hovering to display detailed information and dynamic charts; S5. Choose visualization tools and platforms, including Matplotlib / Seaborn, Plotly, Tableau / PowerBI, Dash, and D3.js; S6. Evaluation and optimization of visualization results, based on user feedback, chart clarity and performance evaluation; S7. Deployment and integration. When deploying, you need to consider the system's stability, security, and seamless integration with other modules.

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