对基于人工智能的预警系统的性能来说,临床背景比数据量更重要
Taeyong Sim1, Eunyoung Cho1, Jihyun Kim1
1AITRICS Corporation, Seoul 06221, Republic of Korea.
Journal of clinical medicine
|July 12, 2025
概括
电子健康记录中缺少的数据可以为预测患者事件提供信息. 一个深度学习模型有效地预测了主要不良事件,显示临床背景比数据量更重要.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的人工智能
- 预测分析是一种预测分析.
背景情况:
- 临床数据数量有所不同,受临床医生的风险感知和测试决定的影响.
- 缺少电子健康记录数据可能意味着测试被认为是不必要的稳定患者.
研究的目的:
- 评估一个深度学习早期预警系统 (VitalCare-Major Adverse Event Score) 用于预测成人住院患者的关键事件.
- 评估患者的严重程度 (查尔森并发症指数 - CCI) 和实验室数据量是否会影响预测准确度.
主要方法:
- 成年住院患者的回顾性单中心研究.
- 使用深度学习模型预测计划外的ICU转移,心脏骤停或6小时前死亡.
- 根据CCI对患者进行分类,并分析数据完整性和数量的影响.
主要成果:
- 高CCI的患者有更多的测试和更少的缺失数据;中度至低CCI的患者有更少的测试和更多的缺失数据.
- 尽管数据量有差异,但CCI组之间的事件队列中,缺失模式在CCI组中的事件队列中是相似的.
- 深度学习模型的预测性能在重症组中强大,这表明临床背景的重要性.
结论:
- 电子健康记录中的数据完整性需要细微的解释.
- 保存实验室测试订单的现实世界模式可以提高预测模型的性能.
- 缺少数据的临床背景是预测准确性的关键因素.
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