The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision‐making-Reference-Cited by-同舟云学术

The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision‐making

Published:2018-11-23 Issue:3 Volume:31 Page:429-435
ISSN:1742-6731
Container-title:Emergency Medicine Australasia
language:en
Short-container-title:Emergency Medicine Australasia

Author:

Rendell Kathryn¹,Koprinska Irena²^ORCID,Kyme Andre¹^ORCID,Ebker‐White Anja A³⁴^ORCID,Dinh Michael M³⁵

Affiliation:

1. School of Aerospace, Mechanical and Mechatronic Engineering, Faculty of Engineering and Information TechnologiesThe University of Sydney Sydney New South Wales Australia

2. School of Information Technologies, Faculty of Engineering and Information TechnologiesThe University of Sydney Sydney New South Wales Australia

3. Emergency DepartmentRoyal Prince Alfred Hospital Sydney New South Wales Australia

4. School of Nursing, Faculty of Medicine and HealthThe University of Sydney Sydney New South Wales Australia

5. Discipline of Emergency Medicine, School of Medicine, Faculty of Medicine and HealthThe University of Sydney Sydney New South Wales Australia

Publisher

Wiley

Subject

Emergency Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1742-6723.13199

Reference24 articles.

1. Achieving Quality in Clinical Decision Making: Cognitive Strategies and Detection of Bias

2. The multitasking clinician: Decision-making and cognitive demand during and after team handoffs in emergency care

3. Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach

4. Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings

5. Artificial Neural Network Models for Prediction of Acute Coronary Syndromes Using Clinical Data From the Time of Presentation

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