用时序注意力模型精准预测脊柱手术住院时长，还能解释关键影响因素。
SurgeryLSTM: A Time-Aware Neural Model for Accurate and Explainable Length of Stay Prediction After Spine Surgery
- 基于双向LSTM与注意力机制建模患者术前时序数据。
- 预测准确率R2达0.86，优于XGBoost等传统模型。
- 可识别影响住院时长的关键临床事件，适合临床决策支持。
目的：开发并评估用于预测择期脊柱手术住院时长（LOS）的机器学习模型，重点关注时序建模与模型可解释性的优势。方法：将传统机器学习模型（如线性回归、随机森林、支持向量机、XGBoost）与我们提出的SurgeryLSTM模型进行对比，该模型采用带掩码的双向长短期记忆网络（BiLSTM）结合注意力机制，使用结构化围术期电子健康记录（EHR）数据。性能通过决定系数（R²）评估，关键预测因子通过可解释AI方法识别。结果：SurgeryLSTM取得最高预测精度（R²=0.86），优于XGBoost（R²=0.85）和基线模型。注意力机制通过动态识别术前临床序列中具有影响力的时序片段，提升了可解释性，使临床医生可追溯每个预测的关键依据。主要影响因素包括骨疾病、慢性肾病以及腰椎融合术。讨论：结合注意力机制的时序建模显著提升预测效果，能捕捉患者数据的动态变化。相比静态模型，SurgeryLSTM兼具更高准确率与更强可解释性，对临床应用至关重要。结果表明，基于注意力的时序模型有望融入医院规划流程。结论：SurgeryLSTM为择期脊柱手术的住院时长预测提供了高效且可解释的AI解决方案，支持将时序可解释机器学习方法集成至临床决策支持系统，以优化出院准备与个体化护理。
原文摘要 · Abstract (English)
Objective: To develop and evaluate machine learning (ML) models for predicting length of stay (LOS) in elective spine surgery, with a focus on the benefits of temporal modeling and model interpretability. Materials and Methods: We compared traditional ML models (e.g., linear regression, random forest, support vector machine (SVM), and XGBoost) with our developed model, SurgeryLSTM, a masked bidirectional long short-term memory (BiLSTM) with an attention, using structured perioperative electronic health records (EHR) data. Performance was evaluated using the coefficient of determination (R2), and key predictors were identified using explainable AI. Results: SurgeryLSTM achieved the highest predictive accuracy (R2=0.86), outperforming XGBoost (R2 = 0.85) and baseline models. The attention mechanism improved interpretability by dynamically identifying influential temporal segments within preoperative clinical sequences, allowing clinicians to trace which events or features most contributed to each LOS prediction. Key predictors of LOS included bone disorder, chronic kidney disease, and lumbar fusion identified as the most impactful predictors of LOS. Discussion: Temporal modeling with attention mechanisms significantly improves LOS prediction by capturing the sequential nature of patient data. Unlike static models, SurgeryLSTM provides both higher accuracy and greater interpretability, which are critical for clinical adoption. These results highlight the potential of integrating attention-based temporal models into hospital planning workflows. Conclusion: SurgeryLSTM presents an effective and interpretable AI solution for LOS prediction in elective spine surgery. Our findings support the integration of temporal, explainable ML approaches into clinical decision support systems to enhance discharge readiness and individualized patient care.
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