Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper

Sam Freeman; Isuru Ranapanada; Md Ali Hossain; Kogul Srikandabala; Md Anisur Rahman; Damminda Alahakoon; Hamed Akhlaghi

doi:10.1136/bmjhci-2024-101285

Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper

Sam Freeman, Isuru Ranapanada, Md Ali Hossain, Kogul Srikandabala, Md Anisur Rahman, Damminda Alahakoon, Hamed Akhlaghi

Australian Institute of Health Innovation

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Abstract

Introduction: To address timely care in emergency departments, artificial neural networks (ANNs) with natural language processing will be applied to triage notes to predict patient disposition. This study will develop a predictive model that predicts disposition and type of admission.

Methods and analysis: This will include data preprocessing and quality enhancement, masked language modelling, ANN-based fusion network for prediction. Generative artificial intelligence, along with a medical dictionary, will be employed to augment and contextually reconstruct triage notes to disambiguate and improve linguistic quality. Text features will be extracted, and cluster analysis will be performed on the extracted topics and text features to identify distinct patterns.

Original language	English
Article number	e101285
Pages (from-to)	1-6
Number of pages	6
Journal	BMJ Health and Care Informatics
Volume	32
Issue number	1
DOIs	https://doi.org/10.1136/bmjhci-2024-101285
Publication status	Published - Aug 2025

Bibliographical note

Copyright the Author(s) 2025. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

Artificial intelligence
Data Science
Informatics

Access to Document

10.1136/bmjhci-2024-101285Licence: CC BY-NC

Publisher version (open access)Final published version, 617 KBLicence: CC BY-NC

Cite this

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abstract = "Introduction: To address timely care in emergency departments, artificial neural networks (ANNs) with natural language processing will be applied to triage notes to predict patient disposition. This study will develop a predictive model that predicts disposition and type of admission. Methods and analysis: This will include data preprocessing and quality enhancement, masked language modelling, ANN-based fusion network for prediction. Generative artificial intelligence, along with a medical dictionary, will be employed to augment and contextually reconstruct triage notes to disambiguate and improve linguistic quality. Text features will be extracted, and cluster analysis will be performed on the extracted topics and text features to identify distinct patterns.",

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AU - Rahman, Md Anisur

AU - Alahakoon, Damminda

AU - Akhlaghi, Hamed

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KW - Informatics

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Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper

Abstract

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