Published on in Vol 24, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30210, first published .
Machine Learning–Based Text Analysis to Predict Severely Injured Patients in Emergency Medical Dispatch: Model Development and Validation

Machine Learning–Based Text Analysis to Predict Severely Injured Patients in Emergency Medical Dispatch: Model Development and Validation

Machine Learning–Based Text Analysis to Predict Severely Injured Patients in Emergency Medical Dispatch: Model Development and Validation

Journals

  1. Hong D, Chang H, He X, Zhan Y, Tong R, Wu X, Li G. Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning. Kidney Diseases 2023;9(5):433 View
  2. Okada Y, Mertens M, Liu N, Lam S, Ong M. AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges. Resuscitation Plus 2023;15:100435 View
  3. Ding X, Wang Y, Ma W, Peng Y, Huang J, Wang M, Zhu H. Development of early prediction model of in-hospital cardiac arrest based on laboratory parameters. BioMedical Engineering OnLine 2023;22(1) View
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  5. Vakili Ojarood M, Yaghoubi T, Farzan R. Machine learning for prehospital care of patients with severe burns. Burns 2024;50(4):1041 View
  6. Syyrilä T, Koskiniemi S, Manias E, Härkänen M. Taxonomy development methods regarding patient safety in health sciences – A systematic review. International Journal of Medical Informatics 2024;187:105438 View