Advanced artificial intelligence vs simpler models for 1-year death prediction among patients receiving hemodialysis

Abstract Objectives We evaluated the data requirement for modern AI tools to outperform simpler models in predicting short-term mortality in over 500 000 patients with hemodialysis-dependent kidney failure. Materials and Methods We compared logistic regression, boosting, and transformers using increasingly complex feature sets (from last-visit data to full trajectories). Performance was measured using the area under the ROC curve (AUC-ROC) and the Precision-Recall curve (AUC-PR) across training data sizes ranging from 500 to 490 197 samples. Results Using features with temporal information is beneficial across all models. On the full dataset, Transformers (AUC-ROC = 0.8568) and boosting (AUC-ROC = 0.8598) perform similarly. Discussion Transformers require large datasets to outperform simpler models like boosting, limiting their usefulness in smaller datasets, even on datasets as big as 500K. Conclusion Modern AI tools require substantial data to justify their computational cost over simpler approaches. However, a more complex feature set seems to be beneficial across all models.