Prediction of 90Y Radioembolization Outcome from Pretherapeutic Factors with Random Survival Forests

Conclusion: Random survival forests are a simple and straightforward machine-learning approach for prediction of overall survival. The predictive performance of the trained model was similar to a previously published Cox regression model. The model has revealed a strong predictive value for baseline cholinesterase and bilirubin levels with a highly nonlinear influence for each parameter.

Source: Journal of Nuclear Medicine - May 1, 2018 Category: Nuclear Medicine Authors: Ingrisch, M., Schoppe, F., Paprottka, K., Fabritius, M., Strobl, F. F., De Toni, E. N., Ilhan, H., Todica, A., Michl, M., Paprottka, P. M. Tags: Clinical Source Type: research