  ICU readmission is associated with longer hospitalization, mortality and
adverse outcomes. An early recognition of ICU re-admission can help prevent
patients from worse situation and lower treatment cost. As the abundance of
Electronics Health Records (EHR), it is popular to design clinical decision
tools with machine learning technique manipulating on healthcare large scale
data. We designed data-driven predictive models to estimate the risk of ICU
readmission. The discharge summary of each hospital admission was carefully
represented by natural language processing techniques. Unified Medical Language
System (UMLS) was further used to standardize inconsistency of discharge
summaries. 5 machine learning classifiers were adopted to construct predictive
models. The best configuration yielded a competitive AUC of 0.748. Our work
suggests that natural language processing of discharge summaries is capable to
send clinicians warning of unplanned 30-day readmission upon discharge.
