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EMR-LIP:一种轻量级的框架,用于标准化电子病历中纵向不规则数据的预处理

Jiawei Luo¹, Shixin Huang², Lan Lan³

¹Department of Cardiovascular Surgery and West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, Sichuan, 610041, China; Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China.

Computer methods and programs in biomedicine

|November 30, 2024

概括

新的电子医疗记录纵向不规则数据预处理 (EMR-LIP) 框架标准化了EMR数据预处理,提高了模型性能和公平性. 这种开源工具提高了临床任务的研究效率和可重复性.

科学领域:

* 生物医学信息学
* * 临床数据科学
* 医疗保健中的机器学习

背景情况:

*电子医疗记录 (EMR) 为预测建模提供了宝贵的纵向患者数据.
*不一致的EMR数据的预处理妨碍了研究的可复制性和可比性.
* 缺乏用于EMR数据预处理的标准化工具是一个重大挑战.

研究的目的:

* 引入电子医疗记录纵向不规则数据预处理 (EMR-LIP) 框架.
* 提供轻量级的模块化方法,以优化纵向,不规则的EMR数据预处理.
*提高临床研究的研究效率,一致性,可重复性和可比性.

主要方法:

*EMR-LIP采用模块化设计,用于预处理工具的低级封装.
*变量被细分分类,每个类型都有特定的预处理技术.
*使用深度学习模型对MIMIC-IV和eICU-CRD数据库进行实证验证.

主要成果:

*使用EMR-LIP预处理数据的模型在MIMIC-IV和eICU-CRD上表现出卓越的基线性能.
* 传统模型 (LSTM,GRU) 的表现优于复杂模型,在医院死亡预测中达到高达0.94的AUROC.
*EMR-LIP在重新抽样间隔中确保了稳定的表现,并改善了跨种族群体的公平性.

结论:

*EMR-LIP提供了一个端到端的解决方案,用于创建模型准备好的EMR数据.
* 该框架简化了复杂,不规则的纵向EMR数据的预处理.
*EMR-LIP是开源的,以鼓励社区驱动的改进和进步.

关键词:

深度学习是一种深度学习.电子医疗记录电子医疗记录不规则的数据不规则的数据.纵向数据纵向数据纵向数据预处理管道预处理管道

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