A pediatric recurrent respiratory tract infection recurrence risk prediction method and system based on the gut-lung axis theory
By using automated data processing and ensemble learning models based on the gut-lung axis theory, the problems of inconsistent standards, low prediction accuracy, and insufficient automation in assessing the risk of recurrent respiratory infections in children have been solved. This has enabled efficient and accurate risk assessment and early warning, and is applicable to chronic disease management systems and family health monitoring.
Patent Information
- Application Number
- CN202610830713.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-10
- Publication Date
- 2026-07-17
AI Technical Summary
Current risk assessment methods for recurrent respiratory infections in children rely on clinicians' experience, have inconsistent assessment standards, highly subjective results, limited data dimensions, fail to integrate gut microbiota and immune function, have low prediction accuracy and delayed early warning, lack automated integration of auxiliary tools, have low data collection efficiency, cannot support large-scale population screening, and lack purely information processing-based prediction methods.
This paper presents a method for predicting the risk of recurrent respiratory infections in children based on the gut-lung axis theory. The method automatically collects, cleans, and extracts multi-dimensional data features through a computer or server, constructs an integrated learning model, and realizes risk assessment and early warning. The entire process, including data collection, cleaning, feature extraction, model calculation, and result output, is performed by a computer and does not involve human diagnosis or treatment.
It achieves fully automated fusion and standardized processing of multimodal medical data, improves the efficiency and consistency of risk assessment, significantly enhances prediction accuracy and early warning capabilities, supports batch screening of large populations, meets the requirements for patent protection, and is clinically applicable.
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