SurgeryLSTM: A Time-Aware Neural Model for Accurate and Explainable Length of Stay Prediction After Spine Surgery

Temporal patterns matter for medical predictions: SurgeryLSTM achieved 86% accuracy by analyzing the sequence and timing of patient data leading up to surgery, slightly outperforming traditional methods. This demonstrates that when clinical events occur—not just what they are—significantly impacts predicting how long patients stay in the hospital after spine surgery.

Explainability is essential for clinical trust: The model's attention mechanism allows doctors to see exactly which patient events or time periods most influenced each length-of-stay prediction. This transparency is described as "critical for clinical adoption," suggesting that AI systems must show their reasoning to gain acceptance from medical professionals who need to understand and validate recommendations.

Specific patient factors drive outcomes: The research identified bone disorder, chronic kidney disease, and lumbar fusion type as the most impactful predictors of hospital stay length. This finding helps clinicians focus on which patient characteristics and conditions warrant closer monitoring and planning for post-operative recovery.

Advanced AI can balance accuracy with interpretability: Rather than choosing between a "black box" model with slightly higher accuracy or a simpler but less accurate model, SurgeryLSTM achieved both high performance (R²=0.86) and explainability. This suggests responsible AI development should pursue solutions that don't force tradeoffs between effectiveness and transparency.