A Symptom-Based Natural Language Processing Surveillance Pipeline for Post-COVID-19 Patients.

Post-acute sequelae of SARS CoV-2 (PASC) are a group of conditions in which patients previously infected with COVID-19 experience symptoms weeks/months post-infection. PASC has substantial societal burden, including increased healthcare costs and disabilities. This study presents a natural language processing (NLP) based pipeline for identification of PASC symptoms and demonstrates its ability to estimate the proportion of suspected PASC cases.

Predictors of Postacute Sequelae of COVID-19 Development and Rehabilitation: A Retrospective Study.

Objective: To examine the frequency of postacute sequelae of SARS-CoV-2 (PASC) and the factors associated with rehabilitation utilization in a large adult population with PASC.

Design: Retrospective study.

Setting: Midwest hospital health system.

A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.

Objective: With COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution.