临床文档的自然语言处理,以评估心力衰竭患者的功能状态
Philip Adejumo1, Phyllis M Thangaraj1, Lovedeep Singh Dhingra1
1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.
JAMA network open
|November 7, 2024
概括
深度学习自然语言处理 (NLP) 策略准确地从临床笔记中提取心力衰竭 (HF) 功能状态. 这有助于更好地跟踪护理提供和识别临床试验的患者.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 连续功能状态评估对于心力衰竭 (HF) 管理至关重要.
- 目前的叙述性文档限制了质量改善和临床试验患者选择.
- 从非结构化的临床笔记中提取结构化数据仍然是一个挑战.
研究的目的:
- 开发和验证深度学习自然语言处理 (NLP) 策略.
- 从非结构化的临床文档中提取功能状态评估.
- 为了提高临床笔记的实用性,用于HF管理和研究.
主要方法:
- 在三个实践网络中利用了34,070名高温患者的电子健康记录数据.
- 开发并验证了NLP模型来检测纽约心脏协会 (NYHA) 的分类和HF症状.
- 专家注释的笔记被用于NLP模型培训和绩效评估.
主要成果:
- NYHA类检测模型在开发和验证站点实现了高精度 (类加权AUROC 0.98-0.99).
- 这种HF症状检测模型表现出强的性能 (AUROC 0.94-0.95).
- NLP在13.1%的笔记中发现了NYHA类,在10.8%的笔记中发现了症状,将分类笔记增加了83%.
结论:
- 该NLP方法准确地从临床笔记中提取NYHA症状类和HF症状.
- 这一策略增强了对心力衰竭患者最佳护理的跟踪.
- 它可以从非结构化数据中识别符合条件的患者参与临床试验.
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