Mortality Prediction in ICUs Using A Novel Time-Slicing Cox Regression Method

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題名:	Mortality Prediction in ICUs Using A Novel Time-Slicing Cox Regression Method
作者:	陳雯玲;Wang, Yuan;Chen, Wenlin;Heard, Kevin;Kollef, Marin H;Bailey, Thomas C;Cui, Zhicheng;He, Yujie;Lu, Chenyang;Chen, Yixin
貢獻者:	總教學中心語言中心
關鍵詞:	Data Mining;Databases, Factual;Humans;Intensive Care Units;Regression Analysis;Support Vector Machine
日期:	2015-01-01
上傳時間:	2026-04-23 14:27:36 (UTC+8)
出版者:	American Medical Informatics Association;United States: American Medical Informatics Association
摘要:	摘要： Over the last few decades, machine learning and data mining have been increasingly used for clinical prediction in ICUs. However, there is still a huge gap in making full use of the time-series data generated from ICUs. Aiming at filling this gap, we propose a novel approach entitled Time Slicing Cox regression (TS-Cox), which extends the classical Cox regression into a classification method on multi-dimensional time-series. Unlike traditional classifiers such as logistic regression and support vector machines, our model not only incorporates the discriminative features derived from the time-series, but also naturally exploits the temporal orders of these features based on a Cox-like function. Empirical evaluation on MIMIC-II database demonstrates the efficacy of the TS-Cox model. Our TS-Cox model outperforms all other baseline models by a good margin in terms of AUC_PR, sensitivity and PPV, which indicates that TS-Cox may be a promising tool for mortality prediction in ICUs. 其他題名： AMIA Annu Symp Proc 出版者： United States: American Medical Informatics Association 出版日期： 2015 出處： AMIA ... Annual Symposium proceedings, 2015, Vol.2015, p.1289-1295 版權： 2015 AMIA - All rights reserved. 2015 識別號： ISSN: 1942-597X 識別號： EISSN: 1559-4076 識別號： PMID: 26958269
顯示於類別:	[語言中心] 期刊論文

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