Binary classification pipeline for fluid therapy decision support processes 560 ICU patient episodes through 5-fold cross-validation with 80/20 train-test split. Multimodal physiological inputs—heart rate, mean arterial pressure, central venous pressure, lactate, and respiratory rate—feed a deep learning model trained with Adam optimizer (learning rate 1e-3, batch size 32) and early stopping on validation loss. Performance evaluation uses AUC, recall, precision, and F1 metrics benchmarked against logistic regression and random forest baselines, with real-time inference latency assessment for clinical deployment. Fork this diagram on Diagrams.so to customize preprocessing steps, swap model architectures, or adapt evaluation metrics for your healthcare ML workflow.