使用护理叙述和反向时间注意力 (RETAIN) 模型预测术后住院时间:回顾性队列研究
Sungjoo Han1, Yong Bum Kim2, Jae Hong No2
1Division of Statistics, Medical Research Collaborating Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
JMIR medical informatics
|December 22, 2023
概括
通过反向时间注意力 (RETAIN) 模型分析的护理叙述有效预测卵巢癌患者的手术后长时间停留 (LOS). 早期的护理笔记是延长住院时间的关键预测因素.
科学领域:
- 医疗信息学 医疗信息学
- 临床数据科学 临床数据科学
- 医疗保健中的人工智能
背景情况:
- 护理叙述对于预测临床结果非常有价值.
- 护理叙述对使用深度学习模型预测术后停留时间 (LOS) 的具体影响尚不清楚.
研究的目的:
- 应用反向时间注意力 (RETAIN) 模型来预测术后的LOS,使用护理叙述作为主要输入.
- 确定哪些护理叙述在深度学习模型中显著影响LOS预测.
主要方法:
- 354名卵巢癌手术患者 (2014-2020年) 的回顾性分析.
- 在手术后3天内使用护理叙述来预测长期的LOS (≥10天).
- 基于医生的回顾性笔记,与医生的评估进行比较.
主要成果:
- RETAIN模型的表现优于医生的评估 (AUC为0.81对0.58,P=.02).
- 从术后第一天开始的护理叙述是长期LOS最有影响力的预测因素.
- 频繁的医生检查和延迟静脉注射液/患者控制的止痛药的使用与增加的LOS有关.
结论:
- 通过使用护理叙述,RETAIN模型有效地预测卵巢癌患者的术后LOS.
- 从早期术后护理笔记中获得的可解释的深度学习见解可以准确地预测长期的LOS.
关键词:
欧洲人权理事会 欧洲人权理事会深度学习是一种深度学习.放电是什么意思 放电是什么意思排放预测 排放预测电子健康记录 电子健康记录提取 提取 提取 提取这是一个自由文本.在医院住院.可以解释的深度学习.停留时间的长度.机器学习是机器学习.护理 护理 护理卵巢癌是发生在卵巢的癌症.预测 预测 预测 预测风险预测风险预测文本采矿 文本采矿是什么更多相关视频
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