Using data mining to predict emergency department length of stay greater than 4 hours: Derivation and single‐site validation of a decision tree algorithm-Reference-Cited by-同舟云学术

Using data mining to predict emergency department length of stay greater than 4 hours: Derivation and single‐site validation of a decision tree algorithm

Published:2019-12-06 Issue:3 Volume:32 Page:416-421
ISSN:1742-6731
Container-title:Emergency Medicine Australasia
language:en
Short-container-title:Emergency Medicine Australasia

Author:

Rahman Md Anisur¹²^ORCID,Honan Bridget³,Glanville Thomas¹,Hough Peter¹,Walker Katie⁴⁵^ORCID

Affiliation:

1. Murrumbidgee Local Health District Wagga Wagga New South Wales Australia

2. School of Computing and MathematicsCharles Sturt University Wagga Wagga New South Wales Australia

3. Emergency DepartmentWagga Wagga Base Hospital Wagga Wagga New South Wales Australia

4. Emergency DepartmentCabrini Health Melbourne Victoria Australia

5. Department of Epidemiology and Preventive MedicineMonash University Melbourne Victoria Australia

Publisher

Wiley

Subject

Emergency Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1742-6723.13421

Reference23 articles.

1. Aiming to be NEAT: safely improving and sustaining access to emergency care in a tertiary referral hospital

2. The National Emergency Access Target (NEAT) and the 4‐hour rule: time to review the target

3. Emergency Department Length of Stay: a Major Risk Factor for Pneumonia in Intubated Blunt Trauma Patients

4. Emergency department length of stay independently predicts excess inpatient length of stay

5. New emergency department quality measure: From access block to National Emergency Access Target compliance

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