NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov.

Core outcome sets (COS) are necessary to ensure the systematic collection, metadata analysis and sharing the information across studies. However, development of an area-specific clinical research is costly and time consuming. ClinicalTrials.gov, as a public repository, provides access to a vast collection of clinical trials and their characteristics such as primary outcomes. With the growing number of COVID-19 clinical trials, identifying COSs from outcomes of such trials is crucial. This paper introduces a semi-automatic pipeline that can efficiently identify, aggregate and rank the COS from the primary outcomes of COVID-19 clinical trials. Using Natural language processing (NLP) techniques, our proposed pipeline successfully downloads and processes 5090 trials from all over the world and identifies COVID-19-specific outcomes that appeared in more than 1% of the trials. The top-of-the-list outcomes identified by the pipeline are mortality due to COVID-19, COVID-19 infection rate and COVID-19 symptoms.

Latent COVID-19 Clusters in Patients with Opioid Misuse.

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.