  Objective: Clinical notes contain information not present elsewhere,
including drug response and symptoms, all of which are highly important when
predicting key outcomes in acute care patients. We propose the automatic
annotation of phenotypes from clinical notes as a method to capture essential
information, which is complementary to typically used vital signs and
laboratory test results, to predict outcomes in the Intensive Care Unit (ICU).
  Methods: We develop a novel phenotype annotation model to annotate phenotypic
features of patients which are then used as input features of predictive models
to predict ICU patient outcomes. We demonstrate and validate our approach
conducting experiments on three ICU prediction tasks including in-hospital
mortality, physiological decompensation and length of stay for over 24,000
patients by using MIMIC-III dataset.
  Results: The predictive models incorporating phenotypic information achieve
0.845 (AUC-ROC) to predict in-hospital mortality, 0.839 (AUC-ROC) for
physiological decompensation and 0.430 (Kappa) for length of stay, all of which
consistently outperform the baseline models leveraging only vital signs and
laboratory test results. Moreover, we conduct a thorough interpretability
study, showing that phenotypes provide valuable insights at the patient and
cohort levels.
  Conclusion: The proposed approach demonstrates phenotypic information
complements traditionally used vital signs and laboratory test results,
improving significantly forecast of outcomes in the ICU.
