为医疗保健提供者提供自然语言数据处理服务
Joshua Au Yeung1,2, Anthony Shek3, Thomas Searle3
1CogStack, Guys and St Thomas NHS Trust, London, UK. j.auyeung@nhs.net.
BMC medical informatics and decision making
|November 27, 2024
概括
本研究引入了英国的临床自然语言处理 (NLP) 服务.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 将机器学习集成到医院环境中,对于将模型与临床工作流程和现实数据保持一致至关重要.
- 在非医疗保健部门的数字化转型中,综合数据标签和质量管理服务的发展出现了.
研究的目的:
- 描述在英国国家卫生服务中开发和实施一项新的临床NLP服务.
- 详细创建临床NLP资源和实施框架,将专家知识提炼成NLP模型.
主要方法:
- 开发用于临床NLP服务实施的平行协调平台.
- 将专家临床知识提炼成NLP模型.
- 使用命名实体识别 (NER) 用于临床和操作用例.
主要成果:
- 累计超过26,086条注释,涵盖了来自二级护理专业的556个SNOMED CT概念.
- 通过综合语言建模服务,成功交付了众多临床和操作用例.
- 证明了医疗保健提供效率的提高,并启用了下游数据驱动技术.
结论:
- 开发的临床NLP服务为将AI整合到医疗保健中提供了可扩展的解决方案.
- NLP服务准备成为医疗保健提供商运营的组成部分.
- 这项工作为未来临床NLP实施提供了基础框架.
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