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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34295, first published .
Machine Learning–Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance

Machine Learning–Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance

Machine Learning–Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance

Journals

  1. Fliegenschmidt J, Hulde N, Gedinha Preising M, Ruggeri S, Szymanowsky R, Meesseman L, Sun H, Dahlweid M, von Dossow V. Leveraging artificial intelligence for the management of postoperative delirium following cardiac surgery. European Journal of Anaesthesiology Intensive Care 2023;2(1):e0010 View
  2. Matsumoto K, Nohara Y, Sakaguchi M, Takayama Y, Fukushige S, Soejima H, Nakashima N. Delirium Prediction Using Machine Learning Interpretation Method and Its Incorporation into a Clinical Workflow. Applied Sciences 2023;13(3):1564 View
  3. Wehkamp K, Krawczak M, Schreiber S. The quality and utility of artificial intelligence in patient care. Deutsches Ärzteblatt international 2023 View
  4. Maheshwari A, Motta M, Lui K. We Need New Tools to Evaluate Neurological Development in Utero and after Birth. Newborn 2023;2(2):iv View
  5. Bhat J, Feng X, Mir Z, Raina A, Siddique K. Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics‐assisted breeding. Physiologia Plantarum 2023;175(4) View
  6. Jankowska A, Ngai J. I, Robot: Healthcare Decisions Made With Artificial Intelligence. Journal of Cardiothoracic and Vascular Anesthesia 2023;37(10):1852 View