自然语言处理系统的通用性和可移植性,以提取个别的社会风险因素
Tanja Magoc1, Katie S Allen2, Cara McDonnell1
1College of Medicine, University of Florida, Gainesville, FL, USA.
International journal of medical informatics
|June 11, 2023
概括
一个基于规则的自然语言处理 (NLP) 模型显示出强大的可移植性和可通用性,用于从不同机构的临床笔记中提取健康的社会决定因素. 简单的调整确保了在识别金融不安全和住房不稳定方面有前途的表现.
科学领域:
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
- 公共卫生 公共卫生
背景情况:
- 健康的社会决定因素 (SDOH) 显著影响患者的结果.
- 从临床笔记中提取SDOH对于有针对性的干预至关重要.
- 现有的自然语言处理 (NLP) 方法可能缺乏跨机构的通用性.
研究的目的:
- 验证并报告NLP方法用于提取社会因素的可移植性和通用性.
- 评估基于规则的NLP模型在不同医疗机构的性能.
主要方法:
- 开发了一个基于规则的确定性状态机NLP模型,并应用于来自两个不同的机构的临床笔记.
- 该模型提取了金融不安全和住房不稳定.
- 在手动注释和模型调整后,使用准确性,积极预测值,灵敏度和特异性来评估性能.
主要成果:
- 该NLP模型处理了600多万张纸币,确定了数千个金融不安全和住房不稳定的情况.
- 该模型在验证数据集上表现出色,所有绩效指标在两个社会因素上都超过了0.87.
- 在不同机构中,NLP模型显示出强大的可移植性和通用性.
结论:
- 基于规则的NLP模型可以通过相对简单的修改在各机构中有效地移植.
- 这项研究强调,需要将NLP模型适应机构特定的模板和术语.
- 与类似的概括性研究相比,开发的NLP模型在提取社会因素方面表现优越.
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