Lung inflammation postoperative prediction system based on mycoplasma pneumoniae infection

By developing a postoperative prediction system for mycoplasma pneumoniae infected lung inflammation that includes data collection, feature extraction, data fusion, prognosis prediction and data visualization modules, the problem of data being difficult to present in the prior art is solved, and the accuracy and personalization of postoperative prognosis evaluation are improved.

CN119851958AInactive Publication Date: 2025-04-18QUJING MATERNAL & CHILD HEALTH HOSPITAL
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Patent Information

Application Number
CN202411927748.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-25
Publication Date
2025-04-18
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The prior art is difficult to present clinical data, imaging data and biomarker data after multi-dimensional and complex lung inflammation infected by Mycoplasma pneumoniae to clinicians in an intuitive and easy-to-understand way, affecting the timeliness and accuracy of decision-making.

Method used

A postoperative prediction system for lung inflammation based on Mycoplasma pneumoniae infection is developed, including a data acquisition module, a feature extraction and processing module, a multi-dimensional data fusion module, a prognosis prediction module and a data visualization module. A machine learning algorithm is used to fuse multi-dimensional data to build a postoperative prognosis evaluation model, and present the prediction results in the form of charts and curves.

Benefits of technology

It improves the accuracy and personalization of postoperative prognosis assessment, helping clinicians to more intuitively understand the patient's condition and prognosis, and make timely and accurate decisions.

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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.
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