Rehospitalization risk predicting method based on cost-sensitive integrated learning model

Abstract

The invention discloses a rehospitalization risk predicting method based on a cost-sensitive integrated learning model. The method comprises the following specific steps of: 1), acquiring medical andexternal environment data information, and constructing a multi-source high-dimension characteristic matrix; 2), performing high-dimension characteristic matrix nonlinear compression expression basedon an automatic encoder; 3), constructing an integrated learning model in which a cost-sensitive support vector machine is used as a weak learner; and 4), through characteristic processing of the step1 and the step 2, inputting a predicting set into a training model, and obtaining a rehospitalization risk predicting result. The method aims at patient demography information, previous hospitalization history, family history and an external environment characteristic and constructs the multi-source high-dimension characteristic matrix, thereby extracting more characteristic information which fully reflects the health condition of the patient. Based on high-dimension characteristic matrix nonlinear compression expression of the automatic encoder, dimension reduction on a sparse characteristicis realized. For aiming at a sample disproportion problem, the integrated learning model in which the cost-sensitive support vector machine is used as the weak learner is constructed, thereby improving rehospitalization risk identification precision.

Description

Technical field [0001] The invention relates to a method for predicting the risk of readmission, in particular to a method for predicting the risk of readmission based on a cost-sensitive integrated learning model. Background technique [0002] The readmission rate is an important indicator reflecting the hospital's medical quality and management level. The readmission risk prediction can identify high-risk re-admission populations in advance, and follow-up and intervention measures can be taken to effectively improve the quality of medical services while reducing medical costs. . With the continuous development of machine learning and data mining technologies, these technologies have also been applied in the field of readmission risk research. Compared with traditional statistical regression methods, the prediction accuracy has been greatly improved, but there are still many shortcomings: [0003] (1) The data features used for readmission prediction have limitations. The re-adm...

AI Technical Summary

Problems solved by technology

Based on the characteristics of multi-source information, the present invention will construct a cost-sensitive learning model for the problem of sample imbalance, and improve the recognition accuracy of the model for patients with risk of readmission

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