Artificial intelligence sepsis prediction algorithm learns to say "I don't know".
Artificial intelligence sepsis prediction algorithm learns to say "I don't know".
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DOI：
10.1038/s41746-021-00504-6
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发表时间：
2021-09-09
影响因子：
15.2
通讯作者：
Nemati S
中科院分区：
文献类型：
--
作者：
Shashikumar SP;Wardi G;Malhotra A;Nemati S
脓毒症是世界范围内发病率和死亡率的主要原因。脓毒症的早期识别是重要的，因为它允许及时管理潜在的挽救生命的复苏和抗菌治疗。我们提出了COMPOSER（脓毒症风险的共形多维预测），这是一种用于脓毒症早期预测的深度学习模型，专门用于通过检测由错误数据、缺失、分布偏移和数据漂移引起的不熟悉患者/情况来减少误报。COMPOSER将这些不熟悉的情况标记为不确定，而不是做出虚假的预测。使用来自美国重症监护病房（ICU）和急诊科（艾德）的两个医疗保健系统的六个患者队列（515，720例患者）来训练和外部和临时验证该模型。在序贯预测设置中，COMPOSER实现了一致的高曲线下面积（AUC）（ICU：0.925-0.953;艾德：0.938-0.945）。在超过600万个预测窗口中，在非脓毒症和脓毒症患者中分别约有20%和8%被确定为不确定。COMPOSER在所有6个队列中在临床可行的时间范围内（ICU：首次抗生素医嘱前12.2 [3.2 22.8]小时和艾德：2.1 [0.8 4.5]小时）提供早期预警，从而允许识别和优先考虑脓毒症高风险患者。
Sepsis is a leading cause of morbidity and mortality worldwide. Early identification of sepsis is important as it allows timely administration of potentially life-saving resuscitation and antimicrobial therapy. We present COMPOSER (COnformal Multidimensional Prediction Of SEpsis Risk), a deep learning model for the early prediction of sepsis, specifically designed to reduce false alarms by detecting unfamiliar patients/situations arising from erroneous data, missingness, distributional shift and data drifts. COMPOSER flags these unfamiliar cases as indeterminate rather than making spurious predictions. Six patient cohorts (515,720 patients) curated from two healthcare systems in the United States across intensive care units (ICU) and emergency departments (ED) were used to train and externally and temporally validate this model. In a sequential prediction setting, COMPOSER achieved a consistently high area under the curve (AUC) (ICU: 0.925–0.953; ED: 0.938–0.945). Out of over 6 million prediction windows roughly 20% and 8% were identified as indeterminate amongst non-septic and septic patients, respectively. COMPOSER provided early warning within a clinically actionable timeframe (ICU: 12.2 [3.2 22.8] and ED: 2.1 [0.8 4.5] hours prior to first antibiotics order) across all six cohorts, thus allowing for identification and prioritization of patients at high risk for sepsis.
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影响因子：
2.9
作者：
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影响因子：
17.1
作者：
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通讯作者：
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影响因子：
3.2
作者：
Desautels T;Calvert J;Hoffman J;Jay M;Kerem Y;Shieh L;Shimabukuro D;Chettipally U;Feldman MD;Barton C;Wales DJ;Das R
通讯作者：
Das R