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Articles Published Processes
10/16/2023 10:26:55 AM | Browse: 223 | Download: 701
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Received |
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2023-07-22 05:01 |
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Peer-Review Started |
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2023-07-22 05:03 |
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To Make the First Decision |
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Return for Revision |
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2023-09-04 07:23 |
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Revised |
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2023-09-08 12:41 |
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Second Decision |
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2023-09-25 06:58 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2023-09-28 05:49 |
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Articles in Press |
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2023-09-28 05:49 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2023-10-08 03:12 |
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Publish the Manuscript Online |
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2023-10-16 10:26 |
| ISSN |
2218-5836 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Mycology |
| Manuscript Type |
Case Control Study |
| Article Title |
Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Chu-Wei Tian, Xiang-Xu Chen, Liu Shi, Huan-Yi Zhu, Guang-Chun Dai, Hui Chen and Yun-Feng Rui |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Winfast Charity Foundation for Financial Support |
No. YL20220525 |
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| Corresponding Author |
Yun-Feng Rui, MD, PhD, Chief Doctor, Deputy Director, Professor, Surgeon, Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, No. 87 Dingjiaqiao, Nanjing 210009, Jiangsu Province, China. ruiyunfeng@126.com |
| Key Words |
Machine learning; Extended length of stay; Hip fracture; Enhanced recovery after surgery; Risk factors |
| Core Tip |
Traditional models have been built to identify risk factors for extended length of stay (eLOS), offering new insights for optimizing treatment for hip fracture patients under the enhanced recovery after surgery concept. However, these traditional statistical methods suffer from poor performance and lack of features. Machine learning (ML) is a scientific discipline focused on teaching computers to learn from data, showing superior predictive performance compared to traditional methods. This study aims to develop ML models for predicting eLOS among geriatric patients with hip fractures, identify associated risk factors, and compare the performance of each model. |
| Publish Date |
2023-10-16 10:26 |
| Citation |
Tian CW, Chen XY, Shi L, Zhu HY, Dai GC, Chen H, Rui YF. Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients. World J Orthop 2023; 14(10): 741-754 |
| URL |
https://www.wjgnet.com/2218-5836/full/v14/i10/741.htm |
| DOI |
https://dx.doi.org/10.5312/wjo.v14.i10.741 |
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