This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (488) Machine Learning (1471) Decision Support for Health Professionals (1191) Clinical Information and Decision Making (1411)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44666, first published .
Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach

Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach

Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach

Authors of this article:

Yuxuan Liu1 Author Orcid Image ;   Xiaoguang Lyu2 Author Orcid Image ;   Bo Yang3 Author Orcid Image ;   Zhixiang Fang1 Author Orcid Image ;   Dejun Hu3 Author Orcid Image ;   Lei Shi4 Author Orcid Image ;   Bisheng Wu5 Author Orcid Image ;   Yong Tian6 Author Orcid Image ;   Enli Zhang7 Author Orcid Image ;   YuanChao Yang8 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Chanif C, Nursalam N, Sriyono S, Yuniasari L, Pranata S, Armiyati Y. The correlation between nurses' knowledge of triage and the accuracy of triage level interpretation in the emergency department. Scripta Medica 2023;54(4):385 View
  2. Zhang S, Fan M, Zhang Y, Li S, Lu C, Zhou J, Zou L. Establishment and validation of a nomogram model for prediction of clinical outcomes in patients with amanita phalloides poisoning. Heliyon 2024;10(17):e37320 View

Books/Policy Documents

  1. Priyatna B, Bakar Z, Zamin N, Yahya Y. Advances in Visual Informatics. View