JMIR Formative Research 10, e76542Evaluating Biomedical Feature Fusion on Machine Learning's Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation2026

Full text on the publisher's site

Publications

Evaluating Biomedical Feature Fusion on Machine Learning's Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation

HN West-Page, K McGoff, H Latimer, I Olufadewa, S Chen

JMIR Formative Research 10, e76542 · 2026

Infectious Disease

~~How to cite~~

HN West-Page, K McGoff, H Latimer, I Olufadewa, S Chen (2026). Evaluating Biomedical Feature Fusion on Machine Learning's Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation. JMIR Formative Research 10, e76542.

Opens the publisher of record in a new tab.

Evaluating Biomedical Feature Fusion on Machine Learning's Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation

Related

Knowledge of HIV Prevention and Stigmatising Attitudes Among Young People With Disabilities in Abuja, Nigeria

Cholera amid the climate crisis: a systematic review of flooding-driven health risks in West Africa

Evaluating Automated Machine Learning Approaches for Early ICU Infection Surveillance