Liver cancer early diagnosis risk prediction model construction method

By constructing a big data-based risk prediction model for early diagnosis of liver cancer, the problems of insufficient accuracy of liver cancer prediction models and lack of clinical diagnosis and treatment evaluation system in existing technologies have been solved, achieving the effect of improving early diagnosis rate and predicting treatment effects.

CN120656701AInactive Publication Date: 2025-09-16SICHUAN ACADEMY OF MEDICAL SCI SICHUAN PROVINCIAL PEOPLES HOSPITAL
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Patent Information

Application Number
CN202510554760.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-29
Publication Date
2025-09-16
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing liver cancer prediction model uses risk scoring to make predictions, which is inaccurate and unreliable, and lacks a clinical diagnosis and treatment evaluation system, resulting in a low early diagnosis rate and an inability to predict clinical treatment effects and postoperative recurrence risks in advance.

Method used

Construct a risk prediction model for early diagnosis of liver cancer. By processing, analyzing and learning clinical data, establish a project database of clinical diagnostic pathways for hepatocellular carcinoma based on big data, determine the main diagnostic points in the temporal diagnostic scheme of the disease and related indicators affecting postoperative outcomes, formulate rules for extracting diagnostic and treatment pathways, build a quality and efficiency evaluation indicator system, calculate the probability of early diagnosis and postoperative recurrence, and establish a Markov model for risk prediction.

Benefits of technology

It improves the accuracy and reliability of early diagnosis of liver cancer, enhances the scientific nature of clinical diagnosis and treatment decisions, can predict treatment effects and postoperative recurrence risks in advance, and improves the early diagnosis rate and treatment effect of HCC.

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Abstract

The invention discloses a construction method of a risk prediction model for early diagnosis of liver cancer. The construction method of the risk prediction model comprises the following steps: 1, preparing clinical data; 2, processing, analyzing and learning clinical data, and constructing a project database of a hepatocellular carcinoma clinical diagnosis path based on big data; 3, determining main diagnosis key points in the disease timing sequence diagnosis scheme and related indexes influencing the operation, and formulating a diagnosis and treatment path extraction rule; 4, constructing a quality-efficiency evaluation index system for the diagnosis and treatment path set, and calculating the early diagnosis probability and postoperative recurrence probability of the liver cancer; and 5, establishing a Markov model for early screening and diagnosis of liver cancer and risk prediction of postoperative recurrence. According to the method, the ANN principle is utilized, an HCC early diagnosis risk prediction model and an HCC clinical diagnosis and treatment path evaluation system are established, HCC high-risk groups are subjected to early recognition, and the HCC early diagnosis rate is increased; meanwhile, an optimal clinical diagnosis and treatment decision is provided for HCC treatment.
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