  Patient monitoring is vital in all stages of care. We here report the
development and validation of ICU length of stay and mortality prediction
models. The models will be used in an intelligent ICU patient monitoring module
of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the
health status of patients, and generates timely alerts, maneuver guidance, or
reports when adverse medical conditions are predicted. We utilized the publicly
available Medical Information Mart for Intensive Care (MIMIC) database to
extract ICU stay data for adult patients to build two prediction models: one
for mortality prediction and another for ICU length of stay. For the mortality
model, we applied six commonly used machine learning (ML) binary classification
algorithms for predicting the discharge status (survived or not). For the
length of stay model, we applied the same six ML algorithms for binary
classification using the median patient population ICU stay of 2.64 days. For
the regression-based classification, we used two ML algorithms for predicting
the number of days. We built two variations of each prediction model: one using
12 baseline demographic and vital sign features, and the other based on our
proposed quantiles approach, in which we use 21 extra features engineered from
the baseline vital sign features, including their modified means, standard
deviations, and quantile percentages. We could perform predictive modeling with
minimal features while maintaining reasonable performance using the quantiles
approach. The best accuracy achieved in the mortality model was approximately
89% using the random forest algorithm. The highest accuracy achieved in the
length of stay model, based on the population median ICU stay (2.64 days), was
approximately 65% using the random forest algorithm.
