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We developed MLHO (pronounced as melo), an end-to-end Machine Learningframework that leverages iterative feature and algorithm selection to predictHealth Outcomes. MLHO implements iterative sequential representation mining,and feature and model selection, for predicting the patient-level risk ofhospitalization, ICU admission, need for mechanical ventilation, and death. Itbases this prediction on data from patients' past medical records (before theirCOVID-19 infection). MLHO's architecture enables a parallel andoutcome-oriented model calibration, in which different statistical learningalgorithms and vectors of features are simultaneously tested to improve theprediction of health outcomes. Using clinical and demographic data from a largecohort of over 13,000 COVID-19-positive patients, we modeled the four adverseoutcomes utilizing about 600 features representing patients' pre-COVID healthrecords 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 featuresthat were utilized in modeling and their relative influence for predicting eachoutcome. Our results demonstrated that while demographic variables (namely age)are important predictors of adverse outcomes after a COVID-19 infection, theincorporation of the past clinical records are vital for a reliable predictionmodel. As the COVID-19 pandemic unfolds around the world, adaptable andinterpretable machine learning frameworks (like MLHO) are crucial to improveour readiness for confronting the potential future waves of COVID-19, as wellas other novel infectious diseases that may emerge.

Individualized Prediction of COVID-19 Adverse outcomes with MLHO