Venous thromboembolism (VTE) is the third most
common cardiovascular condition. Some high risk patients diagnosed with VTE need immediate treatment and monitoring
in intensive care units (ICU) as the mortality rate is high.
Most of the published predictive models for ICU mortality give
information on in-hospital mortality using data recorded in the
first day of ICU admission. The purpose of the current study is to
predict in-hospital and after-discharge mortality in patients with
VTE admitted to ICU using a machine learning (ML) framework.
We studied 2,468 patients from the Medical Information Mart
for Intensive Care (MIMIC-III) database, admitted to ICU with
a diagnosis of VTE. We formed ML classification tasks for
early and late mortality prediction. In total, 1,471 features were
extracted for each patient, grouped in seven categories each
representing a different type of medical assessment. We used an
automated ML platform, JADBIO, as well as a class balancing
combined with a Random Forest classifier, in order to evaluate the
importance of class imbalance. Both methods showed significant
ability in prediction of early mortality (AUC=0.92). Nevertheless,
the task of predicting late mortality was less efficient (AUC=0.82).
To the best of our knowledge, this is the first study in which
ML is used to predict short-term and long-term mortality for
ICU patients with VTE based on a multitude of clinical features
collected over time