开发一种自然语言处理算法,从临床医生的笔记中提取健康的社会决定因素
Hamed Zaribafzadeh1, Jacqueline B Henson2, Norine W Chan1
1Department of Surgery, Duke University, Durham, North Carolina, USA.
概括
自然语言处理 (NLP) 确定了影响移植等候名单访问的健康社会决定因素 (SDOH). 负面情绪和运输障碍与减少访问有关,而收入和积极情绪则改善了这一点.
科学领域:
- 医疗信息学 医疗信息学
- 移植手术 移植手术
- 公共卫生 公共卫生
背景情况:
- 器官移植等候名单访问的差异已被记录.
- 由于缺乏有组织的预等名单数据,对可修改因素的研究受到限制.
研究的目的:
- 开发一种自然语言处理 (NLP) 算法,从临床笔记中提取健康的社会决定因素 (SDOH).
- 量化SDOH与获得移植等候名单之间的关联.
主要方法:
- 从11,111个成年人/肝移植转诊 (2016-2022) 中收集了261,802份临床医生笔记.
- 创建了一个SDOH本体学和基于规则的NLP算法用于术语提取.
- 分析了提取的SDOH和等待列表访问之间的关联.
主要成果:
- 教育,交通和年龄经常被确定为SDOH.
- 负面情绪和"推"与脏和肝脏移植的列表负面相关.
- 收入/就业 (脏) 和判断/积极情绪 (肝脏) 与上市积极相关.
结论:
- 在移植工作流程中,NLP工具可以改善SDOH数据收集和组织.
- 告知资源分配可能会改善等候名单的访问和移植后的结果.
- 整合NLP可以解决移植获取方面的差异.
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