SynthesisJournal of primary care & community health
The Use of Machine Learning in Emergency Care Units: A Systematic Review.
Synthesis in Journal of primary care & community health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emergency departments (EDs) are critical points of entry in healthcare systems where timely and accurate decision-making is vital. Machine learning (ML) offers promising capabilities to enhance patient triage, optimize resource allocation, and improve clinical outcomes in these high-pressure environments. This systematic review investigates the application of ML in EDs, identifies commonly used algorithms and tools, examines their limitations, and provides recommendations for improvement. A structured literature search was conducted across 5 major databases: Google Scholar, Scopus, Web of Science, IEEE Xplore, and PubMed, yielding 1257 peer-reviewed articles. Studies were included if they were published between 2017 and 2024, written in English, and focused on ML applications in EDs within the fields of Computer Science, Engineering, Decision Science, or Mathematics. Exclusion criteria eliminated articles under 6 pages, inaccessible full texts, non-ML-focused studies, and publications such as proposals, abstracts, or book reviews. After screening and quality assessment by 2 independent reviewers, 27 studies were selected for in-depth analysis. Of these, 88.9% were journal articles, 7.4% book chapters, and 3.7% conference proceedings. Findings reveal that the various ML algorithms applied in EDs are context-dependent and use various evaluation metrics, while tools for data extraction and analysis include Python, Keras, TensorFlow, SQL, MATLAB, RStudio, and IBM SPSS. The identified limitations involved data complexity, model accuracy, lack of generalizability, and incomplete datasets. Recommendations across studies emphasized the need to broaden data sources, integrate additional predictors, and improve algorithmic comparisons. This review contributes to the growing body of knowledge on ML in emergency care by synthesizing current practices, highlighting critical challenges, and offering practical directions for future research and implementation.
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What OpenQuestion holds
Registered trials
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.