论文标题
AI误入歧途：技术补充
AI Gone Astray: Technical Supplement
论文作者
论文摘要
这项研究是“ AI误入歧途：患者数据的细微变化如何发送流行算法卷曲，破坏患者安全性”的技术补充。从STAT News调查时间漂移对临床部署机器学习模型的影响。我们使用Mimic-IV（一种公开可用的数据集）来训练模型，这些模型复制了Dascena和Epic的商业方法，以预测败血症的发作，这是一种致命但可治疗的状况。我们观察到其中一些模型的加时性降低；最值得注意的是，建立在史诗特征上的RNN在十年内从0.729 AUC降低到0.525 AUC，这使我们研究了技术和临床漂移，因为这种性能下降的根本原因。
This study is a technical supplement to "AI gone astray: How subtle shifts in patient data send popular algorithms reeling, undermining patient safety." from STAT News, which investigates the effect of time drift on clinically deployed machine learning models. We use MIMIC-IV, a publicly available dataset, to train models that replicate commercial approaches by Dascena and Epic to predict the onset of sepsis, a deadly and yet treatable condition. We observe some of these models degrade overtime; most notably an RNN built on Epic features degrades from a 0.729 AUC to a 0.525 AUC over a decade, leading us to investigate technical and clinical drift as root causes of this performance drop.
