Evidence map›Paper›PMID 40750118›Full record

ArticleBMJ health & care informatics2025

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 Anisur Rahman, Damminda Alahakoon, Hamed Akhlaghi

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ health & care informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sam FreemanEmergency Department, St Vincent's Hospital (Melbourne) Limited, Fitzroy, Victoria, Australia samfreeman8@gmail.com.ORCID http://orcid.org/0000-0002-8193-6454
Isuru RanapanadaLa Trobe Business School, La Trobe University, Melbourne, Victoria, Australia.
Md Ali HossainRajshahi University of Engineering and Technology, Rajshahi, Bangladesh.
Kogul SrikandabalaLa Trobe Business School, La Trobe University, Melbourne, Victoria, Australia.ORCID http://orcid.org/0009-0003-7009-5084
Md Anisur Anisur RahmanLa Trobe Business School, La Trobe University, Melbourne, Victoria, Australia.ORCID http://orcid.org/0000-0002-8444-7648
Damminda AlahakoonLa Trobe Business School, La Trobe University, Melbourne, Victoria, Australia.ORCID http://orcid.org/0000-0003-3291-888X
Hamed AkhlaghiEmergency Department, St Vincent's Hospital (Melbourne) Limited, Fitzroy, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTo 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.

Indexed as

Emergency Service, HospitalNatural Language ProcessingNeural Networks, ComputerTriageElectronic Health RecordsHumansArtificial intelligenceData ScienceInformatics

Identifiers

PMID40750118
PMCPMC12314934

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.