Evidence map›Paper›PMID 35596154›Full record

ArticleBMC emergency medicine2022

Machine learning-based triage to identify low-severity patients with a short discharge length of stay in emergency department.

Yu-Hsin Chang, Hong-Mo Shih, Jia-En Wu, Fen-Wei Huang, Wei-Kung Chen, Dar-Min Chen, Yu-Ting Chung, Charles C N Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC emergency medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 4 pooled it
5.4field-weighted citation impact, top 4% of its field
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

17 citing papers in PubMed, 4 syntheses or guidelines pooled it, 22 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Review
  6. Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Review
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

8 authors at 3 institutions in 1 country.

Yu-Hsin ChangDepartment of Emergency Medicine, China Medical University Hospital, No. 2, Yude Rd., North Dist., Taichung City, 40447, Taiwan.
Hong-Mo ShihDepartment of Emergency Medicine, China Medical University Hospital, No. 2, Yude Rd., North Dist., Taichung City, 40447, Taiwan.
Jia-En WuDepartment of Bioinformatics and Medical Engineering, Asia University, No. 500, Liufeng Rd., Wufeng Dist., Taichung City, 413305, Taiwan.
Fen-Wei HuangDepartment of Medical Research, China Medical University Hospital, No. 2, Yude Rd., North Dist., Taichung City, 40447, Taiwan.
Wei-Kung ChenDepartment of Emergency Medicine, China Medical University Hospital, No. 2, Yude Rd., North Dist., Taichung City, 40447, Taiwan.
Dar-Min ChenDepartment of Emergency Medicine, Asia University Hospital, No. 222, Fuxin Rd., Wufeng Dist., Taichung City, 413505, Taiwan.
Yu-Ting ChungDepartment of Emergency Medicine, Asia University Hospital, No. 222, Fuxin Rd., Wufeng Dist., Taichung City, 413505, Taiwan.
Charles C N WangDepartment of Bioinformatics and Medical Engineering, Asia University, No. 500, Liufeng Rd., Wufeng Dist., Taichung City, 413305, Taiwan. cnwang@asia.edu.tw.
Asia University · TWChina Medical University · TWChina Medical University Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOvercrowding in emergency departments (ED) is a critical problem worldwide, and streaming can alleviate crowding to improve patient flows. Among triage scales, patients labeled as "triage level 3" or "urgent" generally comprise the majority, but there is no uniform criterion for classifying low-severity patients in this diverse population. Our aim is to establish a machine learning model for prediction of low-severity patients with short discharge length of stay (DLOS) in ED.

methodsThis was a retrospective study in the ED of China Medical University Hospital (CMUH) and Asia University Hospital (AUH) in Taiwan. Adult patients (aged over 20 years) with Taiwan Triage Acuity Scale level 3 were enrolled between 2018 and 2019. We used available information during triage to establish a machine learning model that can predict low-severity patients with short DLOS. To achieve this goal, we trained five models-CatBoost, XGBoost, decision tree, random forest, and logistic regression-by using large ED visit data and examined their performance in internal and external validation.

resultsFor internal validation in CMUH, 33,986 patients (75.9%) had a short DLOS (shorter than 4 h), and for external validation in AUH, there were 13,269 (82.7%) patients with short DLOS. The best prediction model was CatBoost in internal validation, and area under the receiver operating cha racteristic curve (AUC) was 0.755 (95% confidence interval (CI): 0.743-0.767). Under the same threshold, XGBoost yielded the best performance, with an AUC value of 0.761 (95% CI: 0.742- 0.765) in external validation.

conclusionsThis is the first study to establish a machine learning model by applying triage information alone for prediction of short DLOS in ED with both internal and external validation. In future work, the models could be developed as an assisting tool in real-time triage to identify low-severity patients as fast track candidates.

Indexed as

Patient DischargeTriageAdultAgedEmergency Service, HospitalHumansLength of StayMachine LearningRetrospective StudiesDecision-making supportDischarge length of stayEmergency departmentMachine learningStreamingTriage

Identifiers

PMID35596154
PMCPMC9123815
OpenAlexW4280594175

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.