从临床笔记中检测护理人员的态度。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2024-01-11 eCollection Date: 2023-01-01
Gaetano Manzo, Leo Anthony Celi, Yasmeen Shabazz, Rory Mulcahey, Lorenzo Jaime Flores, Dina Demner-Fushman
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引用次数: 0

摘要

护理人员的态度会影响医疗质量和差异。临床笔记包含高度专业化和含糊不清的语言,需要广泛的领域知识才能理解,而使用负面语言并不一定意味着态度消极。本研究讨论了从护理人员的临床笔记中检测其态度所面临的挑战。为了应对这些挑战,我们对 MIMIC 临床笔记进行了注释,并从 Hugging Face 平台训练了最先进的语言模型。本研究以新生儿重症监护室为重点,评估了零镜头、少量镜头和完全训练场景下的模型。在所选模型中,RoBERTa 能从临床笔记中识别护理人员的态度,F1 分数为 0.75。这种方法不仅提高了患者满意度,而且为检测和预防护理人员综合症(如疲劳、压力和职业倦怠)提供了令人兴奋的可能性。论文最后讨论了局限性和未来可能开展的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Caregivers Attitude Detection From Clinical Notes.

Caregivers' attitudes impact healthcare quality and disparities. Clinical notes contain highly specialized and ambiguous language that requires extensive domain knowledge to understand, and using negative language does not necessarily imply a negative attitude. This study discusses the challenge of detecting caregivers' attitudes from their clinical notes. To address these challenges, we annotate MIMIC clinical notes and train state-of-the-art language models from the Hugging Face platform. The study focuses on the Neonatal Intensive Care Unit and evaluates models in zero-shot, few-shot, and fully-trained scenarios. Among the chosen models, RoBERTa identifies caregivers' attitudes from clinical notes with an F1-score of 0.75. This approach not only enhances patient satisfaction, but opens up exciting possibilities for detecting and preventing care provider syndromes, such as fatigue, stress, and burnout. The paper concludes by discussing limitations and potential future work.

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