The invention discloses a re-hospitalization risk predicting method based on a
deep learning hybrid model. The method comprises the following steps of 1, collecting a
data set which comprises a patient personal characteristic and an outer environment characteristic; 2, performing characteristic grouping and preprocessing, dividing the characteristics to static characteristics and
time sequence characteristics; 3, mining the
time sequence characteristics, performing statistics analysis on the
time sequence characteristics, and establishing an LDA model and a bidirectional LSTM model; 4, performing characteristic splicing, fusing the static characteristics and the time sequence characteristics after characteristic
engineering processing, and using the fused characteristic as input of a CNN model; and 5, establishing the CNN model for predicting the patient re-hospitalization risk. The method is based on a
deep learning algorithm. Researching and analysis are performed on patient health medical
big data and the outer environment, thereby establishing the re-hospitalization risk predicting model, facilitating reasonable medical resource arrangement by a medical organization, supplyingbetter medical service to the patient, and improving efficiency and accuracy in performing re-hospitalization
risk identification of a participant by an insurance organization.