稳定的临床风险预测与电子健康记录中的分布转移相对应
Seungyeon Lee1,2, Changchang Yin1,2, Ping Zhang1,2
1Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA.
Patterns (New York, N.Y.)
|September 18, 2023
概括
用于临床风险预测的人工智能 (AI) 模型因数据转移而随着时间的推移而退化. 这项研究引入了一种新的方法来重新权衡转移前的数据,并使用Kullback-Leibler分歧损失,提高AI性能.
科学领域:
- 人工智能在医学中的应用
- 机器学习用于医疗保健
- 临床信息学 临床信息学
背景情况:
- 大规模的电子健康记录使人工智能能够进行临床风险预测.
- 人工智能模型由于时间数据集的变化而随着时间的推移表现出性能衰退.
- 有限的后轮数据阻碍了人工智能模型的再培训.
研究的目的:
- 开发一种方法来缓解人工智能临床风险预测模型中因时间数据集转移引起的性能下降.
- 为了应对模型再培训后不够的轮班数据的挑战.
主要方法:
- 提出了一种新的方法,包括从变班前的数据重新加权患者,以减少分配转移.
- 实施Kullback-Leibler分歧损失,以鼓励跨环境的类似患者表示.
- 评估了该方法在缓解时间转移和改善预测性能方面的有效性.
主要成果:
- 提出的方法成功地减轻了时间数据集的变化.
- 在应用该方法后,观察到预测性能显著改善.
- 这种方法有效地解决了小规模下班后数据的局限性.
结论:
- 开发的方法为临床风险预测中的AI模型性能衰退提供了有效的解决方案.
- 重新加权转移前数据和使用KL分歧损失是处理时间转移的有希望的策略.
- 这种方法提高了人工智能驱动的临床决策支持系统的可靠性和寿命.
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