Machine learning to identify attributes that predict patients who leave without being seen in a pediatric emergency department
Author:
Publisher
Springer Science and Business Media LLC
Subject
Emergency Medicine
Link
https://link.springer.com/content/pdf/10.1007/s43678-023-00545-8.pdf
Reference17 articles.
1. Gorski J, Arnold T, Usiak H, Showalter C. Crowding is the strongest predictor of left without being seen risk in a pediatric Emergency Department. Am J Emerg Med. 2021;48:73–8. https://doi.org/10.1016/j.ajem.2021.04.005.
2. Gaucher N, Bailey B, Gravel J. Who are the children leaving the Emergency Department without being seen by a physician? Acad Emerg Med. 2011;18(2):152–7. https://doi.org/10.1111/j.1553-2712.2010.00989.x.
3. Gravel J, Gouin S, Carrière B, Gaucher N, Bailey B. Unfavourable outcome for children leaving the Emergency Department without being seen by a physician. CJEM. 2013;15(5):289–99. https://doi.org/10.2310/8000.2013.130939.
4. Suastegui C, Lozano J, Maniaci V, Linares M. Comparison of the demographics and visit characteristics of patients who left the pediatric Emergency Department without being seen with those who were evaluated in the Emergency Department. Pediatr Emerg Care. 2021;37(6):e329–33. https://doi.org/10.1097/PEC.0000000000002447.
5. Sheraton M, Gooch C, Kashyap R. Patients leaving without being seen from the Emergency Department: a prediction model using machine learning on a nationwide database. J Am Coll Emerg Physicians Open. 2020;1(6):1684–90. https://doi.org/10.1002/emp2.12266.
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3