Re-admission risk prediction method based on adaptive ensemble learning model

Abstract

The invention discloses a re-admission risk prediction method based on an adaptive ensemble learning model. The method comprises the steps of collecting basic information and clinical diagnosis and treatment information of a patient, and constructing a clinical high-dimensional feature matrix and a re-admission label; sequentially performing data preprocessing and KPCA dimension reduction on the clinical high-dimensional feature matrix to obtain a dimension reduction feature set; and constructing an adaptive ensemble learning model, training the adaptive ensemble learning model according to the dimension reduction feature set and the re-admission label, and inputting the dimension reduction feature set of the patient to be predicted into the trained adaptive ensemble learning model to obtain a re-admission risk prediction result of the patient. According to the re-admission risk prediction method based on the self-adaptive ensemble learning model, the re-admission risk of the patient is accurately predicted through the ensemble learning model, a doctor is assisted in taking intervention measures on a high-risk patient in advance, the disease burden of the patient can be reduced, the economic burden of the patient is reduced, the hospital re-admission rate can be reduced, and the medical service quality can be improved.

Description

technical field [0001] The invention belongs to the field of computer science, and in particular relates to a readmission risk prediction method based on an adaptive integrated learning model. Background technique [0002] The readmission rate is an important indicator reflecting the hospital's medical quality and management level. It can accurately predict the risk of patient readmission and provide auxiliary decision-making support for doctors and hospital managers, thereby effectively reducing the readmission rate. While improving the quality of medical services and reducing treatment costs, it helps hospitals allocate medical resources more effectively and rationally. At present, there are still some problems in the research on the risk prediction of readmission: [0003] The characteristics of readmission risk prediction are high-dimensional and nonlinear, and the traditional linear dimensionality reduction methods cannot achieve good dimensionality reduction results. ...

AI Technical Summary

Problems solved by technology

However, in the study of readmission risk prediction, due to the lack of background knowledge, it is difficult for the integrated learning model to select an effective model combination, which affects the prediction effect of the model

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