Latent COVID-19 Clusters in Patients with Opioid Misuse

Abstract

The goal of this paper is to apply unsupervised machine learning techniques in order to discover latent clusters in patients who have opioid misuse and also undergone COVID-19 testing. Target dataset has been constructed based on COVID-19 testing results at Mount Sinai Health System and opioid treatment program (OTP) information from New York State Office of Addiction Service and Support (OASAS). The dataset was preprocessed using factor analysis for mixed data (FAMD) method and then K-means algorithm along with elbow method were used to determine the number of optimal clusters. Four patient clusters were identified among which the fourth cluster constituted the maximum percentage of positive COVID-19 test results (20%). Compared to the other clusters, this cluster has the highest percentage of African Americans. This cluster has also the highest mortality rate (16.52%), hospitalization rate after receiving the COVID-19 test result (72.17%, use of ventilator (7.83%) and ICU admission rate (47.83%). In addition, this cluster has the highest percentage of patients with at least one chronic disease (99.13%) and age-adjusted comorbidity score more than 1 (83.48%). Longer participation in OTP was associated with the highest morbidity and mortality from COVID-19.