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[2602.15852] Building Safe and Deployable Clinical Natural Language Processing under Temporal Leakage Constraints
Summary
This article discusses the development of clinical NLP models that mitigate risks associated with temporal leakage, emphasizing the importance of safety and calibration in predictive performance.
Why It Matters
As clinical NLP systems become integral to healthcare, ensuring their reliability and safety is crucial. This study addresses significant risks posed by temporal leakage, which can lead to erroneous predictions and impact patient care. By focusing on auditing and interpretability, the research contributes to the advancement of safer AI applications in clinical settings.
Key Takeaways
- Temporal leakage can inflate predictive performance in clinical NLP models.
- A lightweight auditing pipeline can help identify and suppress leakage-prone signals.
- Audited models provide better-calibrated probability estimates and reduce reliance on lexical cues.
- Prioritizing temporal validity and behavioral robustness is essential for deployment-ready systems.
- The study highlights the need for interpretability in clinical AI applications.
Computer Science > Computation and Language arXiv:2602.15852 (cs) [Submitted on 24 Jan 2026] Title:Building Safe and Deployable Clinical Natural Language Processing under Temporal Leakage Constraints Authors:Ha Na Cho, Sairam Sutari, Alexander Lopez, Hansen Bow, Kai Zheng View a PDF of the paper titled Building Safe and Deployable Clinical Natural Language Processing under Temporal Leakage Constraints, by Ha Na Cho and 4 other authors View PDF Abstract: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. Re...
