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

Development and Evaluation of Research Methods, Instruments and Tools (744) Machine Learning (2076) Decision Support for Health Professionals (1539) Clinical Information and Decision Making (1915)

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 (7) Tweetations (2) Metrics

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  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
  3. Mehrpour O, Nakhaee S, Abdollahi J, Vohra V. Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the National Poison Data System for improved clinical decision-making. European Journal of Pediatrics 2025;184(2) View
  4. Mehrpour O, Vohra V, Nakhaee S, Mohtarami S, Shirazi F. Machine learning for predicting medical outcomes associated with acute lithium poisoning. Scientific Reports 2025;15(1) View
  5. Teferi M, Mengistie B, Teklehaimanot H, Mengistie C, Gemechu F, Negussie M, Jufara T, Hassen G. Artificial intelligence in clinical toxicology in Africa: Emerging applications and barriers. African Journal of Emergency Medicine 2025;15(4):100901 View
  6. Valdez Vega R, Noboa-Velástegui J, Fletes-Rayas A, Álvarez I, Ramos-Marquez M, Ruíz-Quezada S, Torres-Carrillo N, Navarro-Hernández R. Predicting Metabolic Syndrome Using Supervised Machine Learning: A Multivariate Parameter Approach. International Journal of Molecular Sciences 2025;26(20):9897 View

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