ReviewJournal of biomedical informatics2008
Evolution and challenges in the design of computational systems for triage assistance.
Review in Journal of biomedical informatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled 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.
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
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Technological Integration in Triage With AI and Machine Learning Compared With Conventional Triage and Narrative Synthesis of Telehealth for Patient Prioritization in Emergency and Acute Care Settings: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.BMC emergency medicine · 2024Pooled it
- Article
- Diagnosing an overcrowded emergency department from its Electronic Health Records.Scientific reports · 2024Article
- Applications of Machine Learning Approaches in Emergency Medicine; a Review Article.Archives of academic emergency medicine · 2019Review
- Altering physiological networks using drugs: steps towards personalized physiology.BMC medical genomics · 2013Article
- Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis.Critical care (London, England) · 2010Article
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
Abstract
Compared with expert systems for specific disease diagnosis, knowledge-based systems to assist decision making in triage usually try to cover a much wider domain but can use a smaller set of variables due to time restrictions, many of them subjective so that accurate models are difficult to build. In this paper, we first study criteria that most affect the performance of systems for triage assistance. Such criteria include whether principled approaches from machine learning can be used to increase accuracy and robustness and to represent uncertainty, whether data and model integration can be performed or whether temporal evolution can be modeled to implement retriage or represent medication responses. Following the most important criteria, we explore current systems and identify some missing features that, if added, may yield to more accurate triage systems.
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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.