Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019
Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019
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DOI：
10.23919/cinc49843.2019.9005736
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发表时间：
2019-09
影响因子：
8.8
通讯作者：
M. Reyna;C. Josef;S. Seyedi;R. Jeter;S. Shashikumar;M. Westover;Ashish Sharma;S. Nemati;
中科院分区：
文献类型：
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作者：
M. Reyna;C. Josef;S. Seyedi;R. Jeter;S. Shashikumar;M. Westover;Ashish Sharma;S. Nemati;
PhysioNet/Computing in Cardiology挑战赛侧重于从临床数据中早期发现败血症。来自两个不同医院系统的40,336例患者记录与参与者共享，而来自三个不同医院系统的22,761例患者记录被隔离为隐藏测试集。每个患者记录包含多达40个生命体征、实验室和人口统计数据，超过250万个小时时间窗口和超过1500万个数据点。我们使用脓毒症-3临床标准来定义脓毒症的发病时间。我们要求参与者在脓毒症临床诊断前6小时设计自动化的开源算法来预测脓毒症。我们开发了一种新颖的，基于临床效用的评估指标来评估每种算法，这些算法奖励早期脓毒症预测，惩罚延迟或错过的预测和假警报。在挑战赛的正式阶段，来自学术界和工业界的104个团队共提交了853个参赛作品。我们接受了90篇基于挑战赛参赛作品的摘要，用于心脏病学计算。我们还比较了条目，以确保来自不同团队的方法保持独立。本文介绍了我们的分析，并讨论了挑战对早期脓毒症预测和相关顺序预测任务的影响。
The PhysioNet/Computing in Cardiology Challenge focused on the early detection of sepsis from clinical data. A total of 40,336 patient records from two distinct hospital systems were shared with participants while 22,761 patient records from three distinct hospital systems were sequestered as hidden test sets. Each patient record contained up to 40 measurements of vital sign, laboratory, and demographics data for over 2.5 million hourly time windows and over 15 million data points. We used the Sepsis-3 clinical criteria to define the onset time of sepsis.We challenged participants to design automated, open-source algorithms for predicting sepsis 6 hours before clinical recognition of sepsis. We developed a novel, clinical utility-based evaluation metric to assess each algorithm that rewards early sepsis predictions and penalizes late or missed predictions and false alarms.A total of 104 teams from academia and industry submitted a total of 853 entries during the official phase of the Challenge. We accepted 90 abstracts based on Challenge entries for presentations at Computing in Cardiology. We also compared entries to ensure that approaches from different teams remained independent. This article presents our analysis and discusses the implications of the Challenge for early sepsis predictions and related sequential prediction tasks.