Dara Meyer

TL;DR: Externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons were developed and established model interpretability to identify and rank variables that drive model predictions.

TL;DR: Electronic Health Records from COVID-19 positive hospitalized patients admitted to the Mount Sinai Health System in New York City are analyzed to establish model interpretability to identify and rank variables that drive model predictions and identify at-risk patients.

TL;DR: In this cohort of hospitalised patients, requirement of intensive care and mortality were high and patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.

TL;DR: This paper presents a decision tree-based machine learning model trained on electronic health records from patients with confirmed COVID-19 at a single center within the Mount Sinai Health System in New York City, and is one of the first models with external validation to both predict outcomes in CO VID-19 patients with strong validation performance.

TL;DR: In this paper , a transcriptome-wide investigation was performed in 165 acutely infected hospitalized individuals who were followed clinically into the post-acute period, and two clusters of sequelae exhibited divergent plasma-cell-associated gene expression patterns.