Predicting length of stay in ICU and mortality with temporal dilated separable convolution and context-aware feature fusion-Reference-Cited by-同舟云学术

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Predicting length of stay in ICU and mortality with temporal dilated separable convolution and context-aware feature fusion

Published:2022-12 Issue: Volume:151 Page:106278
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Al-Dailami Abdulrahman^ORCID,Kuang Hulin,Wang Jianxin

Funder

National Natural Science Foundation of China

Central South University

National Key Research and Development Program of China

Changsha Science and Technology Project

National Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and Informatization

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference49 articles.

1. Machine learning for real-time prediction of complications in critical care: a retrospective study;Meyer;Lancet Respir. Med.,2018

2. Assessment of the worldwide burden of critical illness: the intensive care over nations (ICON) audit;Vincent;Lancet Respir. Med.,2014

3. Critical care medicine in the United States 2000–2005: an analysis of bed numbers, occupancy rates, payer mix, and costs;Halpern;Crit. Care Med.,2010

4. Cost analysis on intensive care unit costs based on the length of stay;Kılıç;Turk. J. Anaesthesiol. Reanim.,2019

5. Which models can I use to predict adult ICU length of stay? A systematic review;Verburg;Crit. Care Med.,2017

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4. An efficient random forest algorithm-based telemonitoring framework to predict mortality and length of stay of patients in ICU;Multimedia Tools and Applications;2023-11-07

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