Multi-modal learning for inpatient length of stay prediction-Reference-Cited by-同舟云学术

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Multi-modal learning for inpatient length of stay prediction

Published:2024-03 Issue: Volume:171 Page:108121
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Chen Junde,Wen Yuxin^ORCID,Pokojovy Michael,Tseng Tzu-Liang (Bill),McCaffrey Peter^ORCID,Vo Alexander,Walser Eric,Moen Scott^ORCID

Funder

National Science Foundation

National Science Foundation Directorate for Computer and Information Science and Engineering

National Science Foundation Division of Mathematical Sciences

U.S. Department of Education

National Science Foundation Division of Undergraduate Education

National Institute on Minority Health and Health Disparities

Publisher

Elsevier BV

Reference46 articles.

1. Machine-learning-based hospital discharge predictions can support multidisciplinary rounds and decrease hospital length-of-stay;Levin;BMJ Innovations,2021

2. Association between intensive care unit transfer delay and hospital mortality: a multicenter investigation;Churpek;J. Hosp. Med.,2016

3. AHA Hospital Statistics: Fast Facts on US Hospitals,2017

4. Biased and unbiased estimation of the average length of stay in intensive care units in the Covid-19 pandemic;Lapidus;Ann. Intensive Care,2020

5. Length of stay prediction for ICU patients using individualized single classification algorithm;Ma;Comput. Methods Progr. Biomed.,2020

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multimodal fusion network for ICU patient outcome prediction;Neural Networks;2024-12

2. Beyond the Bedside: Machine Learning-Guided Length of Stay (LOS) Prediction for Cardiac Patients in Tertiary Care;Healthcare;2024-05-29

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