ReviewMedicina (Kaunas, Lithuania)2021
Machine Learning and Syncope Management in the ED: The Future Is Coming.
Review in Medicina (Kaunas, Lithuania), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed, 12 citations in OpenAlex.
- The hope and the hype of artificial intelligence for syncope management.European heart journal. Digital health · 2025Article
- Machine Learning Methods for Predicting Syncope Severity in the Emergency Department: A Retrospective Analysis.Health science reports · 2025Article
- Will Artificial Intelligence Be "Better" Than Humans in the Management of Syncope?JACC. Advances · 2024Review
- Recent Advances and Future Directions in Syncope Management: A Comprehensive Narrative Review.Journal of clinical medicine · 2024Review
- A Hybrid Model for 30-Day Syncope Prognosis Prediction in the Emergency Department.Journal of personalized medicine · 2023Article
- Can Artificial Intelligence Enhance Syncope Management?: AJACC. Advances · 2023Review
- Investigating brain cortical activity in patients with post-COVID-19 brain fog.Frontiers in neuroscience · 2023Article
- Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department.Journal of personalized medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, machine learning (ML) has been promisingly applied in many fields of clinical medicine, both for diagnosis and prognosis prediction. Aims of this narrative review were to summarize the basic concepts of ML applied to clinical medicine and explore its main applications in the emergency department (ED) setting, with a particular focus on syncope management. Through an extensive literature search in PubMed and Embase, we found increasing evidence suggesting that the use of ML algorithms can improve ED triage, diagnosis, and risk stratification of many diseases. However, the lacks of external validation and reliable diagnostic standards currently limit their implementation in clinical practice. Syncope represents a challenging problem for the emergency physician both because its diagnosis is not supported by specific tests and the available prognostic tools proved to be inefficient. ML algorithms have the potential to overcome these limitations and, in the future, they could support the clinician in managing syncope patients more efficiently. However, at present only few studies have addressed this issue, albeit with encouraging results.
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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.