Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper
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 anal...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2025-07-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/32/1/e101285.full |
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| author | Hamed Akhlaghi Sam Freeman Isuru Ranapanada Md Ali Hossain Kogul Srikandabala Damminda Alahakoon Md Anisur Rahman |
| author_facet | Hamed Akhlaghi Sam Freeman Isuru Ranapanada Md Ali Hossain Kogul Srikandabala Damminda Alahakoon Md Anisur Rahman |
| author_sort | Hamed Akhlaghi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-76b37ad6c15e4e11be6b64f0eaf1ed2e |
| institution | DOAJ |
| issn | 2632-1009 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj-art-76b37ad6c15e4e11be6b64f0eaf1ed2e2025-08-20T02:55:06ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092025-07-0132110.1136/bmjhci-2024-101285Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paperHamed Akhlaghi0Sam Freeman1Isuru Ranapanada2Md Ali Hossain3Kogul Srikandabala4Damminda Alahakoon5Md Anisur Rahman6Emergency Department, St Vincent’s Hospital (Melbourne) Limited, Fitzroy, Victoria, AustraliaEmergency Department, St Vincent’s Hospital (Melbourne) Limited, Fitzroy, Victoria, AustraliaLa Trobe Business School, La Trobe University, Melbourne, Victoria, AustraliaRajshahi University of Engineering and Technology, Rajshahi, BangladeshLa Trobe Business School, La Trobe University, Melbourne, Victoria, AustraliaLa Trobe Business School, La Trobe University, Melbourne, Victoria, AustraliaLa Trobe Business School, La Trobe University, Melbourne, Victoria, AustraliaIntroduction 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.https://informatics.bmj.com/content/32/1/e101285.full |
| spellingShingle | Hamed Akhlaghi Sam Freeman Isuru Ranapanada Md Ali Hossain Kogul Srikandabala Damminda Alahakoon Md Anisur Rahman Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper BMJ Health & Care Informatics |
| title | Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper |
| title_full | Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper |
| title_fullStr | Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper |
| title_full_unstemmed | Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper |
| title_short | Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper |
| title_sort | multisite study using a customised nlp model to predict disposition in the emergency department protocol paper |
| url | https://informatics.bmj.com/content/32/1/e101285.full |
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