ArticleJournal of medical systems2013
Construct an optimal triage prediction model: a case study of the emergency department of a teaching hospital in Taiwan.
Article in Journal of medical systems, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.BMC emergency medicine · 2024Pooled it
- Article
- Article
- Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review.Diagnostic and prognostic research · 2020Article
- An exhaustive review and analysis on applications of statistical forecasting in hospital emergency departments.Health systems (Basingstoke, England) · 2018Review
- A Semantic-Based Model for Triage Patients in Emergency Departments.Journal of medical systems · 2017Article
- A new method based for diagnosis of breast cancer cells from microscopic images: DWEE--JHT.Journal of medical systems · 2014Article
- Design and development of a mobile-based system for supporting emergency triage decision making.Journal of medical systems · 2014Article
- A new method based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for recognition of urine cells from microscopic images independent of rotation and scaling.Journal of medical systems · 2014Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of triage is to prevent the delay of treatment for patients in real emergencies due to excessive numbers of patients in the hospital. This study uses the data of patients of consistent triage to develop the triage prediction model. By integrating Principal Component Analysis (PCA) and Support Vector Machine (SVM), the anomaly detection (overestimate and underestimate) prediction accuracy rate can be 100%, which is better than the accuracy rate of SVM (about 89.2%) or Back- propagation Neural Networks (BPNN) (96.71%); afterwards, this study uses Support Vector Regression (SVR) to adopt Genetic Algorithm (GA) to determine three SVR parameters to predict triage. After using the scroll data predictive values, we calculate the Absolute Percentage Error (APE) of each scroll data. The resulting SVR's Mean Absolute Percentage Error (MAPE) is 3.78%, and BPNN's MAPE is 5.99%; therefore, the proposed triage prediction model of this study can effectively predict anomaly detection and triage.
Indexed as
Identifiers
23990379What 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.