干旱区生态环境知识资源中心(Arid): Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation

Arid

DOI	10.1038/s41598-020-79142-z
	Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation
	Deasy, Jacob; Lio, Pietro; Ercole, Ari
通讯作者	Deasy, J (corresponding author), Univ Cambridge, Comp Lab, William Gates Bldg,15 JJ Thomson Ave, Cambridge CB3 0FD, England.
来源期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
出版年	2020
卷号	10 期号:1
英文摘要	Extensive monitoring in intensive care units (ICUs) generates large quantities of data which contain numerous trends that are difficult for clinicians to systematically evaluate. Current approaches to such heterogeneity in electronic health records (EHRs) discard pertinent information. We present a deep learning pipeline that uses all uncurated chart, lab, and output events for prediction of in-hospital mortality without variable selection. Over 21,000 ICU patients and tens of thousands of variables derived from the MIMIC-III database were used to train and validate our model. Recordings in the first few hours of a patient's stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores, AUROC 0.72 and 0.76 at 24 h respectively, within just 12 h of ICU admission. Our model achieves a very strong predictive performance of AUROC 0.85 (95% CI 0.83-0.86) after 48 h. Predictive performance increases over the first 48 h, but suffers from diminishing returns, providing rationale for time-limited trials of critical care and suggesting that the timing of decision making can be optimised and individualised.
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000603252200023
WOS关键词	NEURAL-NETWORKS ; CHALLENGES ; RISK
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/349180
作者单位	[Deasy, Jacob; Lio, Pietro] Univ Cambridge, Comp Lab, William Gates Bldg,15 JJ Thomson Ave, Cambridge CB3 0FD, England; [Ercole, Ari] Univ Cambridge, Addenbrookes Hosp, Div Anaesthesia, Hills Rd, Cambridge CB2 0QQ, England
推荐引用方式 GB/T 7714	Deasy, Jacob,Lio, Pietro,Ercole, Ari. Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation[J],2020,10(1).
APA	Deasy, Jacob,Lio, Pietro,&Ercole, Ari.(2020).Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation.SCIENTIFIC REPORTS,10(1).
MLA	Deasy, Jacob,et al."Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation".SCIENTIFIC REPORTS 10.1(2020).