A machine learning system to optimise triage in an adult ophthalmic emergency department: a model development and validation studyResearch in context

A machine learning system to optimise triage in an adult ophthalmic emergency department: a model development and validation studyResearch in context

Summary: Background: A substantial proportion of attendances to ophthalmic emergency departments are for non-urgent presentations. We developed and evaluated a machine learning system (DemDx Ophthalmology Triage System: DOTS) to optimise triage, with the aim of reducing inappropriate emergency atte...

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Bibliographic Details
Main Authors: Camilo Brandao-de-Resende, Mariane Melo, Elsa Lee, Anish Jindal, Yan N. Neo, Priyanka Sanghi, Joao R. Freitas, Paulo V.I.P. Castro, Victor O.M. Rosa, Guilherme F.S. Valentim, Maria Luisa O. Higino, Gordon R. Hay, Pearse A. Keane, Daniel V. Vasconcelos-Santos, Alexander C. Day
Format: Article
Language:English
Published: Elsevier 2023-12-01
Series:EClinicalMedicine
Subjects:
Ophthalmology
Triage
Machine learning
Artificial intelligence
Emergency care
Digital health
Online Access:http://www.sciencedirect.com/science/article/pii/S2589537023005084

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http://www.sciencedirect.com/science/article/pii/S2589537023005084

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