用电子健康记录预测患者结果时减轻结果混
S Momsen Reincke1,2,3, Camilo Espinosa1,2,3, Philip Chung1
1Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA 94305, United States.
概括
由于混因素,人工智能 (AI) 疾病预测模型的特异性可能很差. 多类人工智能架构有效地提高了特异性,并减少了与单一结果模型相比的结果混.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床流行病学临床流行病学
背景情况:
- 使用电子健康记录 (EHR) 的人工智能 (AI) 模型显示出疾病预测和风险分层的前景.
- 然而,单一结果的人工智能模型可能缺乏特异性,导致假阳性和相关的患者负担.
- 混因素可以显著影响这些预测模型的准确性.
研究的目的:
- 评估混因子对单一结果AI疾病预测模型的特异性的影响.
- 评估多类人工智能架构在减轻结果结合和提高特异性的有效性.
- 在真实世界和模拟的临床数据中,比较单一结果与多类模型的性能.
主要方法:
- 一个先进的AI模型从EHR疾病代码预测胰腺癌,在230万患者队列中进行了评估.
- 一个预测多种癌症类型的多类AI模型同时被开发和比较.
- 进行了一项临床模拟实验,以调查混因子对单个结果模型特异性的影响.
主要成果:
- 胰腺癌预测模型的得分与卵巢癌相关,这表明结果因混因素而混.
- 临床模拟证实,混因素会损害单一结果AI预测模型的特异性.
- 多类架构增强了预测癌症类型的特异性,同时保持了性能,解决了结果的混.
结论:
- 单一结果的AI疾病预测模型容易受到潜在混因素引起的结果混的影响.
- 多类人工智能架构提供了使用EHR数据进行疾病预测的更具体和更强大的方法.
- 仔细考虑预测结果的数量对于可靠的AI疾病风险预测至关重要.
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