Predicting Hospital Readmission among Patients with Sepsis Using Clinical and Wearable Data-Reference-Cited by-同舟云学术

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Predicting Hospital Readmission among Patients with Sepsis Using Clinical and Wearable Data

Published:2023-07-24 Issue: Volume: Page:
ISSN:
Container-title:2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Amrollahi Fatemeh¹,Shashikumar Supreeth Prajwal¹,Boussina Aaron¹,Yhdego Haben¹,Nayebnazar Arshia¹,Yung Nathan²,Wardi Gabriel²,Nemati Shamim¹

Affiliation:

1. University of California San Diego,Division of Biomedical Informatics,La Jolla,CA,92093

2. UC San Diego Health,Department of Emergency Medicine,La Jolla,CA,92093

Funder

Health

National Institute of General Medical Sciences

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10339936/10339939/10341165.pdf?arnumber=10341165

Reference21 articles.

1. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)

2. Incidence and mortality of hospital- and ICU-treated sepsis: results from an updated and expanded systematic review and meta-analysis

3. Incidence and Trends of Sepsis in US Hospitals Using Clinical vs Claims Data, 2009-2014

4. Enhancing Recovery From Sepsis

5. Unplanned Readmissions After Hospitalization for Severe Sepsis at Academic Medical Center–Affiliated Hospitals*

Cited by 1 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. Impact of a deep learning sepsis prediction model on quality of care and survival;npj Digital Medicine;2024-01-23

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