Lung disease monitoring and predicting system based on artificial intelligence

By using an artificial intelligence system to process multi-source data and classify positions, an individualized lung disease monitoring model is constructed, which solves the shortcomings of traditional lung disease screening methods, achieves high-precision and stable lung disease risk identification and early warning, and adapts to the health monitoring needs of different working environments.

CN120656719AInactive Publication Date: 2025-09-16DANZHOU PEOPLES HOSPITAL
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Patent Information

Application Number
CN202510762882.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-09
Publication Date
2025-09-16
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Traditional lung disease screening methods have slow response, delayed risk identification, and poor individual adaptability. They also lack adaptive modeling mechanisms tailored to differences in job characteristics, leading to insufficient stability in prediction results and making it difficult to meet the dynamic and personalized health monitoring needs of high-risk work groups.

Method used

An artificial intelligence-based lung disease monitoring and prediction system is used to collect and process multi-source data, perform job classification and sub-model construction, screen typical individuals to build label sets, implement model feedback fine-tuning and performance tracking, and has the capabilities of multimodal data fusion, multi-task output, job differentiation adaptation and individualized dynamic calibration.

Benefits of technology

It significantly improves the accuracy of lung disease status identification and prediction stability, adapts to the health risk characteristics of different operating environments, reduces system construction and maintenance costs, and realizes high-frequency and low-intervention lung disease risk identification and early warning.

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Abstract

The invention relates to a lung disease monitoring and predicting system based on artificial intelligence, and belongs to the technical field of medical data processing. The system collects multi-source sample data of an operator, and generates a fusion feature vector through standardization and semantic feature extraction. According to the system, operators are divided into a plurality of post groups by using a clustering algorithm, and post sub-models are respectively constructed in a model training stage and are used for risk scoring, staging identification and trend prediction of lung diseases. In the long-term operation process, the system screens typical individuals from each post group and constructs a label set to realize model feedback fine tuning and performance tracking. The system has the capabilities of multi-modal data fusion, multi-task output, post differentiation adaptation and individualized dynamic calibration, and is suitable for carrying out high-frequency and low-intervention lung disease risk identification and early warning on large-scale operation crowds.
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