A malignant tumor risk assessment and diagnosis decision system, device and medium

Through the malignant tumor risk assessment and diagnostic decision-making system, the weight of disease characteristics is dynamically adjusted, and the problem of insufficient accuracy of malignant tumor risk assessment in the prior art is solved, and a more accurate tumor risk assessment is achieved.

CN119170262BActive Publication Date: 2025-03-18CANCER INST & HOSPITAL CHINESE ACADEMY OF MEDICAL SCI
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Patent Information

Application Number
CN202311638877.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-12-01
Publication Date
2025-03-18
Estimated Expiration
2043-12-01

AI Technical Summary

Technical Problem

The existing malignant tumor risk assessment methods have the shortcomings to reduce the accuracy of risk assessment, because the weight of malignant tumors in different time stages is different, and it cannot effectively reflect the impact of changes in lifestyle habits on tumor risk.

Method used

It provides a malignant tumor risk assessment and diagnostic decision-making system. Through information acquisition, data processing, feature extraction, feature evaluation and diagnostic feedback modules, user information is obtained, data processing and feature extraction is carried out, and the predictive model training is used to obtain the user's disease probability value at different time stages and feedback it to the target device.

Benefits of technology

The accuracy of malignant tumor risk assessment is improved, and by dynamically adjusting the weight of disease characteristics, the tumor risk at different time stages can be more accurately reflected.

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Abstract

The present application relates to a malignant tumor risk assessment and diagnosis decision system, device and medium, and relates to the technical field of malignant tumor assessment, including: an information acquisition module for acquiring user information, a data processing module for processing user data in the user information to obtain user verification data, a feature extraction module for extracting data features from the user verification data to obtain data parameter features corresponding to the user and parameter feature values ​​corresponding to the data parameter features, a feature evaluation module for inputting the data parameter features and parameter feature values ​​into a prediction model for training to obtain the user's disease probability values ​​at different time stages, and a diagnosis feedback module for formatting the disease probability values ​​based on a preset conversion standard to obtain disease probability information, and feeding back the disease probability information to the target device. The present application improves the accuracy of malignant tumor risk assessment.
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