Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients

Understand

Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare.

• This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research.
• Conventional approaches in ML use cross-entropy loss (CEL) that often suffers from poor margin classification.
• For the first time, we show that contrastive loss (CL) improves the performance of CEL especially for imbalanced EHR data and the related COVID-19 analyses.