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Data were collected from 114 sites in 10 countries (TICO\/ACTIV-3) and 63 sites in 11 countries (ITAC). A ML pipeline including 5 classification models, and 1 survival model was used for risk factor identification and clinical outcome prediction. Risk factors were compared between a ML-based classification model, a ML-based survival model and a previously published Cox model. Performance of the ML-based classification model was compared across TICO\/ACTIV-3 and ITAC.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n A total of 2625 (TICO\/ACTIV-3) and 579 (ITAC) adults hospitalized for COVID-19 were included. Some overlap of risk factors was identified across models. Five were identified in all models, 3 only in ML models, and 4 only in the non-ML model. The ML model showed good predictive performance in TICO\/ACTIV-3. Internal validation showed no overfitting. Lower model performance was observed in ITAC (\u221215.8%), but performance remained above chance level.<\/jats:p>\n <\/jats:sec>\n \n Discussion<\/jats:title>\n Differences in methods for risk factor identification using ML and non-ML complicates the comparison of results derived from each approach, but using multiple approaches may unveil overlooked risk factors.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Risk factor identification may benefit from integrating both ML and non-ML methods, but external validation is necessary, even in RCTs.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooag079","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T11:44:57Z","timestamp":1778067897000},"source":"Crossref","is-referenced-by-count":0,"title":["Comparing risk factors in severe COVID-19 using machine learning and non-machine learning methods: analysis from 2 international randomized controlled 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