Using natural language processing on free-Text clinical notes to identify patients with long-Term COVID effects

ABSTRACT

As of May 15th, 2022, the novel coronavirus SARS-COV-2 has infected 517 million people and resulted in more than 6.2 million deaths around the world. About 40% to 87% of patients suffer from persistent symptoms weeks or months after their original infection. Despite remarkable progress in preventing and treating acute COVID-19 conditions, the clinical diagnosis of long-Term COVID remains difficult. In this work, we use free-Text clinical notes and natural language processing (NLP) techniques to explore long-Term COVID effects. We first obtain free-Text clinical notes from 719 outpatient encounters representing patients treated by physicians at Emory Clinic to detect patterns in patients with long-Term COVID symptoms. We apply state-of-The-Art NLP frameworks to automatically identify patients with long-Term COVID effects, achieving 0.881 recall (sensitivity) score for note-level prediction. We further interpret the prediction outcomes and discuss potential phenotypes. Our work aims to provide a data-driven solution to identify patients who have developed persistent symptoms after acute COVID infection. With this work, clinicians may be able to identify patients who have long-Term COVID symptoms to optimize treatment. © 2022 Owner/Author.