Lasso-based esophageal squamous cell carcinoma patient risk prediction column diagram model establishment method

A technology for esophageal squamous cell carcinoma and risk prediction, applied in character and pattern recognition, medical data mining, instruments, etc., can solve the problems of unreliable models and low recognition rate, and achieve the effect of improving performance and reducing costs

Active Publication Date: 2021-04-09
ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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Problems solved by technology

[0004] In view of the deficiencies in the above-mentioned background technology, the present invention proposes a Lasso-based method for establishing a nomogram model for risk prediction of patien...

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  • Lasso-based esophageal squamous cell carcinoma patient risk prediction column diagram model establishment method
  • Lasso-based esophageal squamous cell carcinoma patient risk prediction column diagram model establishment method
  • Lasso-based esophageal squamous cell carcinoma patient risk prediction column diagram model establishment method

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[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0045] Such as figure 1 As shown, the embodiment of the present invention provides a method for establishing a Lasso-based risk prediction nomogram model for patients with esophageal squamous cell carcinoma, and the specific steps are as follows:

[0046] Step 1: Collect clinical data, survival data and follow-up data of patients with esophageal squamous cell carcinoma, and divide the clinical data of patients with esophageal squamous cell carcinoma into test ...

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Abstract

The invention provides a Lasso-based esophageal squamous cell carcinoma patient risk prediction column diagram model establishment method, which is used for evaluating the postoperative survival risk of esophageal squamous cell carcinoma patients. The method comprises the following steps: firstly, collecting clinical data of esophageal squamous cell carcinoma patients, analyzing the clinical data by utilizing single-factor Cox, Lasso and multi-factor Cox regression analysis methods to obtain important characteristic variables, and establishing probability prediction models with different characteristic dimensions; secondly, selecting a probability prediction model with better performance and establishing a postoperative risk prediction column diagram model of the esophageal squamous cell carcinoma patient; and finally, dividing the patients into a high-risk group and a low-risk group according to the postoperative risk prediction column diagram model of the esophageal squamous cell carcinoma patients, and verifying the reliability and effectiveness of model classification through a KM survival curve analysis method. According to the method, the postoperative survival risk of the esophageal squamous cell carcinoma patient can be accurately predicted, reference is better provided for treatment of the esophageal squamous cell carcinoma patient, and meanwhile the risk prediction cost is reduced.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a Lasso-based method for establishing a nomogram model for risk prediction of patients with esophageal squamous cell carcinoma. Background technique [0002] The risk prediction model to evaluate the prognosis of patients has been widely used in different diseases. In China, the incidence of esophageal squamous cell carcinoma is relatively high. Early detection and effective treatment of esophageal squamous cell carcinoma have always been concerned by experts and scholars. Accurate prognosis remains a major challenge. The occurrence of esophageal squamous cell carcinoma is not the result of a single factor. The clinical data collected from patients with esophageal squamous cell carcinoma has the characteristics of redundant information and noise. Current clinical medical methods cannot completely improve the prognosis of patients, but by mining clinical detection data E...

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Application Information

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IPC IPC(8): G16H50/30G16H50/70G06K9/62
CPCG16H50/30G16H50/70G06F18/29
Inventor 凌丹张桢桢王延峰王妍孙军伟王英聪姜素霞栗三一黄春李盼龙杨飞飞王立东宋昕赵学科
Owner ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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