Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48862, first published .
Interpretable Deep Learning System for Identifying Critical Patients Through the Prediction of Triage Level, Hospitalization, and Length of Stay: Prospective Study

Interpretable Deep Learning System for Identifying Critical Patients Through the Prediction of Triage Level, Hospitalization, and Length of Stay: Prospective Study

Interpretable Deep Learning System for Identifying Critical Patients Through the Prediction of Triage Level, Hospitalization, and Length of Stay: Prospective Study

Journals

  1. Ganatra H, Latifi S, Baloglu O. Pediatric Intensive Care Unit Length of Stay Prediction by Machine Learning. Bioengineering 2024;11(10):962 View
  2. Stylianides C, Nicolaou A, Sulaiman W, Alexandropoulou C, Panagiotopoulos I, Karathanasopoulou K, Dimitrakopoulos G, Kleanthous S, Politi E, Ntalaperas D, Papageorgiou X, Garcia F, Antoniou Z, Ioannides N, Palazis L, Vavlitou A, Pattichis M, Pattichis C, Panayides A. AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview. Machine Learning and Knowledge Extraction 2025;7(1):6 View
  3. Berikol G, Kanbakan A, Ilhan B, Doğanay F. Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education. Turkish Journal of Emergency Medicine 2025;25(2):67 View

Conference Proceedings

  1. Elbattah M, Arnaud E, Ghazali D, Dequen G. 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Exploring the Ethical Challenges of Large Language Models in Emergency Medicine: A Comparative International Review View
  2. Khope S, Hulle N. 2024 IEEE Pune Section International Conference (PuneCon). Integrated Deep Learning Model for Predictive Diagnosis of Life-Threatening Disease Condition View