用结构化和非结构化电子健康记录数据为医院诱导的痴呆症的临床预测模型:开发和验证研究的协议
Sarah E Ser1, Kristen Shear2, Urszula A Snigurska2
1Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, United States.
JMIR research protocols
|November 9, 2023
概括
这项研究开发了医院诱导的狂妄症的预测模型,这是老龄化人口中常见的疾病. 整合电子健康记录数据和自然语言处理旨在提高患者安全和护理质量.
科学领域:
- 老年医学 老年医学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 医院诱导的痴呆症是一种普遍且昂贵的阳性疾病,由于美国人口老龄化,预计患病率将上升.
- 跨学科的系统方法对于减轻医院引起的妄想的频率和影响至关重要.
研究的目的:
- 开发医院诱导狂妄症的预测模型,以提高住院老年人的安全性.
- 为了创建一个可计算的医院诱导狂妄的表型.
- 设计物流回归和机器学习模型,使用来自电子健康记录的结构化和文本数据.
主要方法:
- 在临床笔记上利用监督和无监督的文本挖掘来增强预后模型的预测能力.
- 整合电子健康记录中的结构化数据和基于文本的数据,用于妄想风险预测.
- 确保开发和验证符合个人预后或诊断 (TRIPOD) 陈述的多变量预测模型的透明报告.
主要成果:
- 该研究将分析从2012年1月到2021年5月的约332,230例患者接触.
- 研究结果将通过科学会议和同行评审的出版物传播.
- 预计该项目将于2024年3月完成.
结论:
- 成功实施将建立一个强大的数据基础设施,用于实时分析临床文本.
- 开发的模型有可能被整合到电子健康记录中,以支持护理点的决策.
- 这旨在防止患者受到伤害,提高护理质量.
关键词:
大数据就是大数据.临床文本 临床文本数据科学数据科学妄想 妄想 妄想 妄想 妄想这是一个自由文本.医院收购的医院获得的医院医院诱导的医院诱导的在医院获得的疾病.机器学习是机器学习.模型模型模型模型模型模型模型 模型 模型 模型自然语言处理自然语言处理.预测 预测 预测 预测预测 预测 预测 预测预测性 预测性 预测性危险的风险 危险的风险有关风险因素的风险因素.这是一个结构化的结构.文本数据 文本数据没有结构的非结构化.更多相关视频
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