Random survival forest-based postoperative liver cancer recurrence prediction method based on and storage medium

A prediction method and forest algorithm technology, applied in the field of bioinformatics, can solve the problems of difficult to deal with medical data, deviation, low accuracy and so on

Pending Publication Date: 2021-05-07
福州宜星大数据产业投资有限公司 +1
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In recent years, the use of various machine learning algorithms to achieve disease risk prediction has become a research hotspot in the field of medical big data. Various complex algorithms can deeply mine the relationship between disease variables, but mainstream machine learning algorithms are difficult to deal with medical data with censored characteristics. data, so there is still a certain deviation, and the accuracy rate is not high

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  • Random survival forest-based postoperative liver cancer recurrence prediction method based on and storage medium
  • Random survival forest-based postoperative liver cancer recurrence prediction method based on and storage medium
  • Random survival forest-based postoperative liver cancer recurrence prediction method based on and storage medium

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Embodiment 1

[0081] This embodiment provides a method for predicting postoperative recurrence of liver cancer based on random survival forest, which may include the following steps:

[0082] S1: Obtain each case that was enrolled in the group. The preoperative liver function evaluation of each case was normal, there was no previous history of malignant tumor, no adjacent organ invasion and distant metastasis, liver cancer resection was performed, and postoperative pathology confirmed hepatocytes cancer, and recurrence after surgery;

[0083] S2: Obtain the clinical data and recurrence time of each case;

[0084] S3: The preset enrollment dimensions include basic patient factors, preoperative examination factors and postoperative pathological factors;

[0085] Specifically, the basic patient factors include age and gender; the preoperative test factors include platelets, albumin, total bilirubin, etiological examination results, and alpha-fetoprotein; the postoperative pathological factors...

Embodiment 2

[0094] Please refer to figure 2 , this embodiment further defines on the basis of embodiment one:

[0095] The S5 specifically includes:

[0096] S51: Divide each case according to a preset ratio to obtain a training group case and a test group case;

[0097] S52: Divide the data set according to the cases in the training group and the test group to obtain a data set in the training group and a data set in the test group;

[0098] S53: According to the data set of the training group and the recurrence time of each case in the training group, the random survival forest algorithm is used to construct the corresponding early recurrence prediction model of liver cancer and its cumulative risk function;

[0099] S54: Use the cumulative risk function to predict each case in the training group of cases, and obtain a risk score set composed of the risk scores of each case;

[0100] S55: Divide the risk score set according to a preset ratio to obtain ranges of risk scores respectiv...

Embodiment 3

[0109] This embodiment corresponds to Embodiment 2, and the overall scheme is further limited, which can also be referred to figure 2 , methods include:

[0110] S1: Obtain the enrolled cases, and each case must meet the following conditions: normal preoperative liver function assessment, no previous history of malignant tumors, no adjacent organ invasion and distant metastasis, liver cancer resection, and postoperative pathologically confirmed hepatocytes Cancer, and recurrence after surgery;

[0111] S2: Obtain the recurrence time, relevant clinical data and follow-up data of each of the above cases, and exclude patients with incomplete data;

[0112] S3: Determine the inclusion dimensions, including at least:

[0113] 1. Basic factors of the case: gender, age;

[0114] 2. Preoperative inspection factors: platelets, albumin, total bilirubin, etiological examination (hepatitis B, hepatitis C, others), alpha-fetoprotein;

[0115] 3. Postoperative pathological factors: max...

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Abstract

The invention provides a random survival forest-based liver cancer postoperative recurrence prediction method and a storage medium. The method comprises the following steps: acquiring clinical data and recurrence time of each case, the preset grouping dimension comprising basic factors of the patient, preoperative examination factors and postoperative pathological factors; obtaining a data set according to the clinical data, wherein the data set is composed of preset grouping dimensions corresponding to each case; and according to the data set and the recurrence time of each case, a random survival forest algorithm is adopted to construct a corresponding liver cancer postoperative early recurrence prediction model. According to the method, the postoperative recurrence probability of liver cancer of an individual patient can be accurately predicted, and the postoperative attention can be better determined; active prevention is facilitated; particularly, for medical institutions, medical staff can be helped to accurately screen out high-risk relapse patients after the liver cancer operation, intervention in the early relapse stage is facilitated, and postoperative follow-up visit and treatment are guided.

Description

technical field [0001] The invention relates to the field of bioinformatics, in particular to a random survival forest-based method for predicting postoperative recurrence of liver cancer and a storage medium. Background technique [0002] Primary liver cancer (hereinafter referred to as liver cancer) is one of the most common malignant tumors in my country. Its incidence rate ranks fourth in my country's tumor incidence rate, and its mortality rate ranks third in my country's tumor mortality rate. Liver cancer seriously threatens the lives and health of our people. . At present, surgical resection is the main means of radical treatment of liver cancer, but postoperative recurrence is still an important cause of postoperative death of liver cancer. Clinical data show that the recurrence rate of liver cancer is about 50%. Recurrence is generally divided into early recurrence and late recurrence with 2 years as the boundary, and the number of early recurrence accounts for abo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20G16H50/70G16H50/80
CPCG16H50/20G16H50/70G16H50/80
Inventor 刘景丰曾建兴郭鹏飞刘红枝林孔英陈振伟黄起桢傅俊丁宗仁曾建阳陈传椿李保晟
Owner 福州宜星大数据产业投资有限公司
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