The present application relates to the technical field of medical
data processing, and particularly relates to a cervical
disease two-stage dynamic risk prediction method based on
multidimensional data, comprising the following steps: step S1,
data acquisition and structuring: a plurality of dimensions of
patient data are collected and structured by questionnaire, and a cervical
disease database is constructed; step S2, first-stage model construction: for a
general screening population, a first prediction model is constructed by using the questionnaire survey data collected in step S1; step S3, second-stage model construction: for a
population with
HPV positive or other suspicious indications obtained by the first prediction model, high-dimensional clinical data collected in step S1 are integrated, and a second prediction model is constructed by using an
ensemble learning strategy; step S4, model series early warning output; and step S5, decision support generation. The present application integrates low-cost and easily-obtained
multidimensional data, constructs a scientific
risk stratification model, and realizes two-stage and
continuous dynamic early warning.