干旱区生态环境知识资源中心(Arid): OASIS plus : leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

Arid

DOI	10.1186/s12911-021-01517-7
	OASIS plus : leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality
	El-Manzalawy, Yasser; Abbas, Mostafa; Hoaglund, Ian; Cerna, Alvaro Ulloa; Morland, Thomas B.; Haggerty, Christopher M.; Hall, Eric S.; Fornwalt, Brandon K.
通讯作者	El-Manzalawy, Y (corresponding author), Geisinger, Dept Translat Data Sci & Informat, Danville, PA 17822 USA.
来源期刊	BMC MEDICAL INFORMATICS AND DECISION MAKING
EISSN	1472-6947
出版年	2021
卷号	21 期号:1
英文摘要	Background Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called weights or subscores) into a final score (or probability) for quantifying the severity of critical illness (or the likelihood of in-hospital mortality). Although these simple additive models are human readable and interpretable, their predictive performance needs to be further improved. Methods We present OASIS +, a variant of the Oxford Acute Severity of Illness Score (OASIS) in which an ensemble of 200 decision trees is used to predict in-hospital mortality based on the 10 same clinical variables in OASIS. Results Using a test set of 9566 admissions extracted from the MIMIC-III database, we show that OASIS + outperforms nine previously developed severity scoring methods (including OASIS) in predicting in-hospital mortality. Furthermore, our results show that the supervised learning algorithms considered in our experiments demonstrated higher predictive performance when trained using the observed clinical variables as opposed to OASIS subscores. Conclusions Our results suggest that there is room for improving the prognostic accuracy of the OASIS severity scores by replacing the simple linear additive scoring function with more sophisticated non-linear machine learning models such as RF and XGB.
英文关键词	In-hospital mortality prediction Point-based severity scores Critical care outcomes Supervised machine learning
类型	Article
语种	英语
开放获取类型	Green Published, Green Submitted, gold
收录类别	SCI-E
WOS记录号	WOS:000656260300002
WOS关键词	INTENSIVE-CARE-UNIT ; CHRONIC HEALTH EVALUATION ; ACUTE PHYSIOLOGY SCORE ; BETA-BLOCKERS ; ORGAN FAILURE ; APACHE IV ; CLASSIFICATION ; PATIENT ; SEPSIS ; MODELS
WOS类目	Medical Informatics
WOS研究方向	Medical Informatics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/349729
作者单位	[El-Manzalawy, Yasser; Abbas, Mostafa; Cerna, Alvaro Ulloa; Haggerty, Christopher M.; Hall, Eric S.; Fornwalt, Brandon K.] Geisinger, Dept Translat Data Sci & Informat, Danville, PA 17822 USA; [Hoaglund, Ian] Penn State Univ, Coll Informat Sci & Technol, University Pk, PA 16802 USA; [Morland, Thomas B.] Geisinger, Dept Gen Internal Med, Danville, PA 17822 USA; [Fornwalt, Brandon K.] Geisinger, Dept Radiol, Danville, PA 17822 USA
推荐引用方式 GB/T 7714	El-Manzalawy, Yasser,Abbas, Mostafa,Hoaglund, Ian,et al. OASIS plus : leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality[J],2021,21(1).
APA	El-Manzalawy, Yasser.,Abbas, Mostafa.,Hoaglund, Ian.,Cerna, Alvaro Ulloa.,Morland, Thomas B..,...&Fornwalt, Brandon K..(2021).OASIS plus : leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality.BMC MEDICAL INFORMATICS AND DECISION MAKING,21(1).
MLA	El-Manzalawy, Yasser,et al."OASIS plus : leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality".BMC MEDICAL INFORMATICS AND DECISION MAKING 21.1(2021).