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Prognosis prediction system after liver transplantation

A prediction system and technology for liver transplantation, applied in diagnostic recording/measurement, biological neural network model, health index calculation, etc., can solve problems such as inaccurate research results, and achieve the goals of saving research costs, ensuring comprehensiveness, and improving accuracy Effect

Inactive Publication Date: 2019-12-31
SICHUAN PROVINCIAL PEOPLES HOSPITAL
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention intends to provide a prognosis prediction system after liver transplantation to solve the problem of inaccurate research results caused by research on the premise of using one or more influencing factors in the prior art

Method used

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  • Prognosis prediction system after liver transplantation
  • Prognosis prediction system after liver transplantation
  • Prognosis prediction system after liver transplantation

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0024] Basic as attached figure 1 Shown: the prognosis prediction system after liver transplantation, including the data acquisition module, used to obtain clinical data;

[0025] The database is used to store clinical data and establish data sets. Clinical data includes basic information of patients, clinical indicators, intraoperative treatment measures and prognosis after liver transplantation; during the process of collecting clinical data, in order to ensure the collected clinical Data validity, data screening criteria are stored in the database, and the data collection module collects clinical data according to the set data screening criteria;

[0026] The data correction module is used to organize and correct the collected clinical data; the data correction module is also used to fill in the missing values ​​in the collected clinical data. Specifically, in this plan, the data correction module uses LOWESS local fitting interpolation The algorithm fills in the rows or c...

Embodiment 2

[0063] The difference from Embodiment 1 is that in this embodiment, the model construction module constructs a risk prediction model by combining a deep neural network and a random forest algorithm.

[0064]According to the characteristics of the data itself, considering the three stages of liver transplantation, namely, the diseased liver stage, the anhepatic stage, and the neoliver stage, due to the uneven frequency of various operations in the operation, the lack of many parameters, and the large number of Sparse indication parameters will cause the multi-layer deep neural network model to be almost impossible to learn sometimes. Therefore, in this embodiment, a combination of multi-layer deep neural network model and random forest is used to establish a risk prediction model for poor prognosis after liver transplantation. Carry out machine learning training; specifically, the above process is implemented in Python, and SQL is used for database construction. The computing s...

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Abstract

The invention relates to an artificial intelligence technology and the intelligent medical treatment technology field, aims at solving a problem of an inaccurate research result caused by research onthe premise of assumption of one or more influence factors in the prior art, and provides a prognosis prediction system after liver transplantation. The system comprises a data acquisition module foracquiring clinical data, a database for storing the clinical data, a factor statistics module and a correlation analysis module, wherein the clinical data includes basic information of a patient, clinical indexes, intraoperative processing measures and prognosis conditions after the liver transplantation; the factor statistics module is used for carrying out statistical testing on the clinical data and obtaining an influence factor with relatively strong significance; and the correlation analysis module is used for analyzing and detecting correlation between the influence factor and the prognosis conditions after the liver transplantation so as to obtain a risk factor having significant statistical correlation with the prognosis conditions after the liver transplantation. The method is applied to prediction of the prognosis conditions after the liver transplantation of the patient.

Description

technical field [0001] The invention relates to the field of artificial intelligence technology and smart medical technology, in particular to a prognosis prediction system after liver transplantation. Background technique [0002] Liver transplantation refers to a surgical treatment that implants a healthy liver into the patient's body through surgery, so that the liver function of patients with end-stage liver disease can be well restored. Liver transplantation is currently recognized as the most effective measure for the treatment of end-stage liver disease. After more than 50 years of development, the survival time of patients after liver transplantation has been continuously prolonged, and the incidence of complications has also been reduced. However, although the survival rate and quality of life of liver transplant recipients have improved, there are many and complicated complications after liver transplantation, such as biliary tract complications, which are serious...

Claims

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Application Information

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IPC IPC(8): G16H50/50G16H50/30A61B5/00G06N3/08G06N3/00
CPCA61B5/7275G06N3/006G06N3/08G16H50/30G16H50/50
Inventor 杨玺
Owner SICHUAN PROVINCIAL PEOPLES HOSPITAL
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