了解老年长期护理用户中孤独的模式,使用自然语言处理与自由文本案例笔记
Sam Rickman1, Jose-Luis Fernandez1, Juliette Malley1
1Care Policy and Evaluation Centre, The London School of Economics and Political Science, London, United Kingdom.
PloS one
|April 2, 2025
概括
新的自然语言处理 (NLP) 模型能够准确地识别老年人的孤独和社会隔离.
科学领域:
- 老年学是一门学科.
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 孤独和社会孤立对老年人来说是一个重大问题,影响他们的福祉和死亡率.
- 在公共资助的长期护理机构中,关于这些问题的现有数据有限.
- 自然语言处理 (NLP) 为分析非结构化文本数据提供了新的方法.
研究的目的:
- 开发和验证一种基于NLP的方法,用于在老年人的行政案例笔记中识别孤独和社会隔离.
- 使用这种新方法,评估长期护理人口中孤独和社会隔离的流行程度.
- 探索这种NLP措施在理解服务使用和护理需求方面的实用性.
主要方法:
- 分析了伦敦3046名老年人的110万份自由文本病例笔记 (2008-2020年).
- 应用三个NLP技术:文档术语矩阵,预训练嵌入和基于变压器的模型.
- 使用F1分数,调查数据和文献比较,验证表现最好的双向变压器模型.
主要成果:
- 双向变压器模型获得了0.92的高F1得分,在识别孤独和孤立方面表现出色.
- 大约43%的个人在初始评估时有病例说明表明孤独或孤立,与调查估计相一致.
- 源自NLP的测量结果显示了构造有效性,与孤独生活和记忆障碍等因素相关,并预测了社会包容性服务的使用情况.
结论:
- 在行政健康记录中,NLP提供了一种强大而可扩展的方法来检测孤独和社会隔离.
- 这种方法克服了传统调查的局限性,通过将文本数据与其他行政信息联系起来.
- 开源模型有助于进一步研究和开发针对弱势老年人的有针对性的干预措施.
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