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This paper introduces SynerT, a leakage-aware multimodal evaluation framework for early intraoperative acute kidney injury prediction, demonstrating that structured clinical context is necessary for effective risk stratification over waveform-only modeling.
This paper introduces a leakage-aware evaluation framework for Ethereum actor classification, comparing tree-based models like XGBoost against sequential deep learning models (Transformer, BiLSTM). The authors find that XGBoost outperforms sequence models under leak-reduced conditions while offering lower latency and energy use.