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

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
  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

Books/Policy Documents

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