Tree-based classification model for Long-COVID infection prediction with age stratification using data from the National COVID Cohort Collaborative.

ABSTRACT:

Objectives

We propose and validate a domain knowledge-driven classification model for diagnosing post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHRs) data.

Materials and methods

We developed a robust model that incorporates features strongly indicative of PASC or associated with the severity of COVID-19 symptoms as identified in our literature review. The XGBoost tree-based architecture was chosen for its ability to handle class-imbalanced data and its potential for high interpretability. Using the training data provided by the Long COVID Computation Challenge (L3C), which was a sample of the National COVID Cohort Collaborative (N3C), our models were fine-tuned and calibrated to optimize Area Under the Receiver

SUBMITTER: Wang WK 

PROVIDER: S-EPMC11547948 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature