This invention relates to a
deep learning-based
system and method for predicting the risk of re-emergence of blood lead levels after
lead poisoning, belonging to the field of intelligent medical technology. The method includes: S1: acquiring raw clinical data of the patient to be evaluated; S2: preprocessing the
raw data, including iterative imputation of missing values,
nonlinear transformation of skewed continuous variables, and mode imputation and encoding of categorical variables; S3:
feature engineering construction, based on the preprocessed data, constructing a low-dimensional engineered feature space with physiological mechanism orientation through cross-dimensional interaction, including the "
exposure source-
exposure intensity-follow-up trend-organ involvement" chain features and a comprehensive medical index; S4: inputting the constructed features into a pre-trained prediction model, outputting the probability value of re-emergence of blood lead, and performing
risk stratification based on the probability value. This invention achieves accurate prediction of re-emergence of blood lead levels in
lead poisoning patients after lead
chelation therapy.