用语言模型分析临床记录，实时预警呼吸机使用风险
Realtime, multimodal invasive ventilation risk monitoring using language models and BoXHED
- 用语言模型提炼病历文本，融合到呼吸机风险监测
- 在多个指标上超越现有最佳方法，最高AUC达0.86
- 能提前更长时间预警，适合重症监护医生参考
目的：在重症监护室（ICU）中实时监测有创通气（iV）对及时干预和改善患者预后至关重要。然而，传统方法常忽略临床笔记中的宝贵信息，仅依赖表格数据。本研究提出一种新方法，通过语言模型对文本进行摘要，将临床笔记融入监测流程，以提升iV风险监测能力。结果：我们在所有报告指标上均优于当前最优方法，达到AUROC 0.86、AUC-PR 0.35，以及最高AUCt 0.86。同时，我们证明该方法可在特定时间区间内提供更长的预警提前期。结论：研究证实，结合临床笔记与语言模型可显著增强iV风险的实时监测，为改善重症患者护理和辅助临床决策提供新路径。
原文摘要 · Abstract (English)
Objective: realtime monitoring of invasive ventilation (iV) in intensive care units (ICUs) plays a crucial role in ensuring prompt interventions and better patient outcomes. However, conventional methods often overlook valuable insights embedded within clinical notes, relying solely on tabular data. In this study, we propose an innovative approach to enhance iV risk monitoring by incorporating clinical notes into the monitoring pipeline through using language models for text summarization. Results: We achieve superior performance in all metrics reported by the state-of-the-art in iV risk monitoring, namely: an AUROC of 0.86, an AUC-PR of 0.35, and an AUCt of up to 0.86. We also demonstrate that our methodology allows for more lead time in flagging iV for certain time buckets. Conclusion: Our study underscores the potential of integrating clinical notes and language models into realtime iV risk monitoring, paving the way for improved patient care and informed clinical decision-making in ICU settings.
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