Kawasaki disease risk assessment model building method and system based on ensemble learning

A technology of risk assessment model and construction method, which is applied in the field of construction method and construction system of Kawasaki disease risk assessment model, which can solve problems such as easy misdiagnosis, children missing the best treatment time, missed diagnosis, etc., and achieve the effect of accurate evaluation model

Active Publication Date: 2018-12-21
DAOZHI PRECISION MEDICINE TECH SHANGHAI CO LTD
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Problems solved by technology

According to the above diagnostic criteria, a patient needs to have a fever for ≥ 5 days for the diagnosis of Kawasaki disease and wait for clinical symptoms to appear, which may easily cause the child to miss the best time for treatment
At the same time, the clinical sym...

Method used

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  • Kawasaki disease risk assessment model building method and system based on ensemble learning
  • Kawasaki disease risk assessment model building method and system based on ensemble learning
  • Kawasaki disease risk assessment model building method and system based on ensemble learning

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Embodiment

[0144] In order to verify the effectiveness of the Kawasaki disease risk assessment method based on integrated learning of the present invention, the time range selected in this embodiment is the data of 42498 patients in the electronic case from 2008.7 to March 2018.

[0145] The analysis of sensitivity, specificity and correctness is based on a two-class problem. Two classifications are defined as positive and negative. Each object in the positive class becomes a positive instance, and each object in the negative class becomes a negative instance. . Generally, when predicting Kawasaki disease, Kawasaki disease samples are in the positive category, and other fever patients are in the negative category. Using the classification model to predict the test sample, there will be four cases. If an instance is a positive class and is predicted as a true positive (TP), if the instance is a negative class and is predicted as a positive class, it is called a false positive class (false p...

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Abstract

The invention provides a Kawasaki disease risk assessment model building method and system based on ensemble learning; the method comprises the following steps: extracting valid samples from a sampledata set for building a model and model assessment; selecting at least 10 feature items that comply with field medical auxiliary diagnosis applications from a feature set that forms the sample data; respectively using a random forest, Boosting, a linear model and a nerve network algorithm to build a Kawasaki disease prevalence risk prediction basic model and a classification valve domain t; usinga Naive Bayes algorithm for integration, and evaluating the Kawasaki disease risks according to a comparison result between two types of posterior probabilities. The method can effectively solve the problems that most classifiers are over-fitting, can adopt advantages and avoid weaknesses, thus providing the more accurate assessment model.

Description

Technical field [0001] This application relates to the technical field of medical evaluation, and specifically to a construction method and construction system of a Kawasaki disease risk assessment model based on integrated learning. Background technique [0002] Kawasaki disease (Kawasaki disease, KD), also known as mucocutaneous lymph node syndrome, is an acute febrile eruptive pediatric disease with systemic vasculitis as the main lesion. Among them, the coronary artery is the most vulnerable part. The most important complication is coronary artery disease. If it cannot be diagnosed and treated in time, it will cause serious damage to the cardiovascular system. It has become one of the most common causes of acquired heart disease in children and ischemic heart in adulthood. Risk factors for disease. Therefore, early diagnosis, early treatment, and reduction of cardiovascular complications have important clinical significance. [0003] According to the diagnostic criteria for ...

Claims

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Application Information

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IPC IPC(8): G16H50/50
CPCG16H50/50
Inventor 丁国徽贾佳李光徐重飞周珍
Owner DAOZHI PRECISION MEDICINE TECH SHANGHAI CO LTD
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