Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Public Health

Background:

• Coronavirus disease 2019 (COVID-19) presents a significant global health challenge with varied clinical outcomes.
• A substantial number of COVID-19 patients require emergency room (ER) readmission shortly after discharge, increasing healthcare burdens.
• Early identification of patients at high risk for ER revisits is essential for optimizing medical resource allocation.

Purpose of the Study:

• To develop and evaluate predictive models for identifying COVID-19 patients at high risk of ER revisits within 7 days of discharge.
• To address data heterogeneity across multiple ERs using deep transfer learning techniques.
• To assess the effectiveness of the Domain Adversarial Neural Network (DANN) algorithms in predicting patient revisits.

Main Methods:

• Utilized Electronic Health Records (EHRs) from 3,210 COVID-19 patient encounters across 13 ERs.
• Employed Natural Language Processing (NLP) with ScispaCy to extract key clinical concepts.
• Developed 7-day revisit prediction models using frequent clinical concepts and compared Multi-DANN, Single-DANN, and baseline strategies.

Main Results:

• The Multi-DANN model demonstrated superior performance in predicting 7-day ER revisits for COVID-19 patients, achieving a median AUROC of 0.8.
• Multi-DANN significantly outperformed Single-DANN (median AUROC = 0.5) and baseline models, effectively handling domain differences.
• The study confirmed the utility of EHR data in developing robust predictive models for ER revisit risk.

Conclusions:

• Deep transfer learning, particularly the Multi-DANN approach, is highly effective in predicting COVID-19 patient ER revisits.
• The Multi-DANN strategy successfully mitigates data heterogeneity issues from multiple sources, enhancing model generalizability.
• This predictive capability can aid clinicians in proactive patient management and resource optimization within emergency departments.