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Published on in Vol 4, No 2 (2021): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29200, first published .
Predicting Prolonged Apnea During Nurse-Administered Procedural Sedation: Machine Learning Study

Predicting Prolonged Apnea During Nurse-Administered Procedural Sedation: Machine Learning Study

Predicting Prolonged Apnea During Nurse-Administered Procedural Sedation: Machine Learning Study

Journals

  1. Gopukumar D, Ghoshal A, Zhao H. Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach. JMIR Medical Informatics 2022;10(8):e37578 View
  2. Conway A, Goudarzi Rad M, Zhou W, Parotto M, Jungquist C. Deep learning classification of capnography waveforms: secondary analysis of the PRODIGY study. Journal of Clinical Monitoring and Computing 2023;37(5):1327 View
  3. Gheysen F, Rex S. Artificial intelligence in anesthesiology. Acta Anaesthesiologica Belgica 2023;74(3):185 View
  4. Conway A, Goudarzi Rad M, Chang K, Parotto M, Mafeld S. Integrated pulmonary index during procedural sedation and analgesia: A cluster‐randomized trial. Journal of Advanced Nursing 2025;81(9):5563 View
  5. Choi J, Lee H, Kim‐Godwin Y. Decoding machine learning in nursing research: A scoping review of effective algorithms. Journal of Nursing Scholarship 2025;57(1):119 View
  6. Bisschops R, Gómez I, Corbett G, Saunders S, Ehrhardt E, Kaduk S, Sfeir N, Saunders R, Özçelik M. Impact of Microstream capnography monitoring on adverse events during procedural sedation: retrospective analysis of a quality improvement initiative performed in five centres. European Journal of Gastroenterology & Hepatology 2026 View