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预测ICU患者的急性损伤和资源利用情况,使用纵向,多式模式的模型

Yukun Tan¹, Merve Dede¹, Vakul Mohanty²

¹Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States. Electronic address: https://twitter.com/zhizhid.

Journal of biomedical informatics

|May 1, 2024

概括

这项研究引入了一种新的AI模型,用于在ICU患者中早期预测急性损伤 (AKI) 和连续脏替代疗法 (CRRT),实现高精度并为及时干预提供见解.

科学领域:

医疗保健中的人工智能
临床信息学临床信息学
预测建模预测建模

背景情况:

关于使用人工智能预测急性损伤 (AKI) 和持续置换疗法 (CRRT) 的研究有限.
现有的研究往往侧重于更广泛的ICU结果,忽视了特定的脏替代疗法需求.

研究的目的:

开发有效的AI模型来预测重症监护室 (ICU) 患者的AKI和CRRT.
确定成功实施这些预测模型的关键因素和方法.

主要方法:

使用时间序列和来自MIMIC-IV数据库的临床笔记,对已建立的预测模型进行比较分析.
开发一种新型的多式人工智能模型,集成长期短期记忆 (LSTM) 和BioMedBERT嵌入.
利用结构化EHR数据和非结构化临床笔记进行预测.

主要成果:

多式联网模型实现了AKI (AUROC: 0.888,AUPRC: 0.727) 和CRRT (AUROC: 0.997,AUPRC: 0.840) 的显著预测性能.
该模型提供了至少12小时的预测时间,以便在临床表现之前进行预测.
沙普利添加物扩张 (SHAP) 分析确定了AKI和CRRT的关键预测特征.

结论:

纵向的多式人工智能建模对于改善早期AKI和CRRT预测至关重要.
开发的模型的性能和可解释性表明,在优化AKI管理方面,该模型有可能用于临床应用.
研究结果为及时干预提供了洞察力,以提高重症监护机构患者的治疗结果.

关键词:

急性损伤 (AKI) 是一种急性损伤.生物医学BERTT 连续脏替代疗法 (CRRT)电子健康记录 (EHR) 是一种电子健康记录.长时间短期记忆 (LSTM)多式联运多式联运

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