  We developed MLHO (pronounced as melo), an end-to-end Machine Learning
framework that leverages iterative feature and algorithm selection to predict
Health Outcomes. MLHO implements iterative sequential representation mining,
and feature and model selection, for predicting the patient-level risk of
hospitalization, ICU admission, need for mechanical ventilation, and death. It
bases this prediction on data from patients' past medical records (before their
COVID-19 infection). MLHO's architecture enables a parallel and
outcome-oriented model calibration, in which different statistical learning
algorithms and vectors of features are simultaneously tested to improve the
prediction of health outcomes. Using clinical and demographic data from a large
cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse
outcomes utilizing about 600 features representing patients' pre-COVID health
records and demographics. The mean AUC ROC for mortality prediction was 0.91,
while the prediction performance ranged between 0.80 and 0.81 for the ICU,
hospitalization, and ventilation. We broadly describe the clusters of features
that were utilized in modeling and their relative influence for predicting each
outcome. Our results demonstrated that while demographic variables (namely age)
are important predictors of adverse outcomes after a COVID-19 infection, the
incorporation of the past clinical records are vital for a reliable prediction
model. As the COVID-19 pandemic unfolds around the world, adaptable and
interpretable machine learning frameworks (like MLHO) are crucial to improve
our readiness for confronting the potential future waves of COVID-19, as well
as other novel infectious diseases that may emerge.
