在时间泄露约束下构建安全且可部署的临床自然语言处理
Building Safe and Deployable Clinical Natural Language Processing under Temporal Leakage Constraints
AI总结：
本研究提出了一种轻量级审计流程，用于在时间泄露约束下构建安全且可部署的临床NLP系统，通过提升模型的校准性和鲁棒性来确保临床应用的安全性。
摘要：
临床自然语言处理（NLP）模型通过利用叙述性临床文档支持医院出院计划已展现出潜力。然而，基于笔记的模型尤其容易受到时间和词汇泄露的影响，其中文档痕迹编码了未来的临床决策并夸大了预测性能的明显程度。这种行为在现实部署中带来了重大风险，其中过于自信或时间无效的预测可能会破坏临床工作流程并损害患者安全。本研究聚焦于构建在时间泄露约束下安全且可部署的临床NLP所需系统级设计选择。我们提出了一种轻量级的审计流程，将其可解释性整合到模型开发过程中，以在最终训练前识别并抑制易泄露的信号。以择期脊柱手术后一天的出院预测为例，我们评估了审计对预测行为、校准和与安全相关的权衡的影响。结果显示，经过审计的模型表现出更加保守和校准更好的概率估计，减少了对出院相关词汇线索的依赖。这些发现强调，可部署的临床NLP系统应优先考虑时间有效性、校准和行为鲁棒性，而不是乐观的性能。
英文摘要：
Clinical natural language processing (NLP) models have shown promise for supporting hospital discharge planning by leveraging narrative clinical documentation. However, note-based models are particularly vulnerable to temporal and lexical leakage, where documentation artifacts encode future clinical decisions and inflate apparent predictive performance. Such behavior poses substantial risks for real-world deployment, where overconfident or temporally invalid predictions can disrupt clinical workflows and compromise patient safety. This study focuses on system-level design choices required to build safe and deployable clinical NLP under temporal leakage constraints. We present a lightweight auditing pipeline that integrates interpretability into the model development process to identify and suppress leakage-prone signals prior to final training. Using next-day discharge prediction after elective spine surgery as a case study, we evaluate how auditing affects predictive behavior, calibration, and safety-relevant trade-offs. Results show that audited models exhibit more conservative and better-calibrated probability estimates, with reduced reliance on discharge-related lexical cues. These findings emphasize that deployment-ready clinical NLP systems should prioritize temporal validity, calibration, and behavioral robustness over optimistic performance.
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