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A machine-learning algorithm has been developed to offer a personalized prediction of life expectancy following lung transplantation. The random survival forests (RSF) model showed “excellent performance” in predicting both survival overall and at the specific time points of one month and a year. Postoperative time using extracorporeal membrane oxygenation was the most critical factor in predicting overall survival among 22 clinical characteristics included. RSF machine learning algorithms are designed to specifically predict survival outcomes.
