自然语言处理精确区分癌症症状信息在电子健康记录叙述中
Alaa Albashayreh1, Anindita Bandyopadhyay2, Nahid Zeinali3
1College of Nursing, University of Iowa, Iowa City, IA.
JCO clinical cancer informatics
|August 8, 2024
概括
自然语言处理 (NLP) 可以在电子健康记录 (EHR) 中识别癌症症状. 一个先进的NLP系统准确地检测出14个症状组,并将观察到的症状与副作用区分开来.
科学领域:
- 计算语言学计算语言学
- 医疗信息学医学信息学
- 在瘤学瘤学.
背景情况:
- 在电子健康记录 (EHR) 中识别癌症症状对于患者护理至关重要.
- 现有的自然语言处理 (NLP) 系统需要改进,以检测各种症状并将其与否定症状或药物副作用区分开来.
研究的目的:
- 评估NLP在癌症患者的EHR叙述中检测14个不同的症状组的准确性.
- 评估NLP区分观察到的症状与否定症状以及与药物相关的副作用的能力.
主要方法:
- 从11,784名癌症患者中提取了902,508张笔记.
- 开发了112个笔记的黄金标准集体,用于标记症状.
- 训练了一个嵌入式增强的NLP系统,集成人类和机器智能.
主要成果:
- 嵌入式增强NLP模型获得了0.877.7的高F1得分.
- 准确度因症状而异, (F1=0.937) 和 (F1=0.787) 分别是最高和最低的.
- 41%的笔记包含症状记录,疼痛是最常见的 (29%),记忆障碍是最不常见的 (0.7%).
结论:
- 在EHR叙述中,NLP可用于检测多个癌症症状组.
- 开发的嵌入式增强NLP系统在症状检测和差异化方面超过了传统的机器学习.
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