Published on in Vol 8, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20324, first published .
Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study

Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study

Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study

Journals

  1. Liu K, Kotani T, Nakamura K, Chihiro T, Morita Y, Ishii K, Fujizuka K, Yasumura D, Taniguchi D, Hamagami T, Shimojo N, Nitta M, Hongo T, Akieda K, Atsuo M, Kaneko T, Sakuda Y, Andoh K, Nagatomi A, Tanaka Y, Irie Y, Kamijo H, Hanazawa M, Kasugai D, Ayaka M, Oike K, Lefor A, Takahashi K, Katsukawa H, Ogura T. Effects of evidence-based ICU care on long-term outcomes of patients with sepsis or septic shock (ILOSS): protocol for a multicentre prospective observational cohort study in Japan. BMJ Open 2022;12(3):e054478 View
  2. Chang H, Cha W. Artificial intelligence decision points in an emergency department. Clinical and Experimental Emergency Medicine 2022;9(3):165 View
  3. Chee M, Chee M, Huang H, Mazzochi K, Taylor K, Wang H, Feng M, Ho A, Siddiqui F, Ong M, Liu N. Artificial intelligence and machine learning in prehospital emergency care: A scoping review. iScience 2023;26(8):107407 View
  4. Seo H, Ahn I, Gwon H, Kang H, Kim Y, Cho H, Choi H, Kim M, Han J, Kee G, Park S, Seo D, Jun T, Kim Y. Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data. Health Care Management Science 2024;27(1):114 View
  5. Lee S, Park H, Hwang J, Lee S, Han K, Kim W, Jeong J, Kang H, Kim A, Lee C, Kim S, Akman C. Machine Learning-Based Models for Prediction of Critical Illness at Community, Paramedic, and Hospital Stages. Emergency Medicine International 2023;2023:1 View
  6. Alghamdi A, Alshibani A, Binhotan M, Alsabani M, Alotaibi T, Alharbi R, Alabdali A. The Ability of Emergency Medical Service Staff to Predict Emergency Department Disposition: A Prospective Study. Journal of Multidisciplinary Healthcare 2023;Volume 16:2101 View
  7. Kim J. Data integration using information and communication technology for emergency medical services and systems. Clinical and Experimental Emergency Medicine 2023;10(2):129 View

Books/Policy Documents

  1. Stewart J, Goudie A, Lu J, Dwivedi G. AI in Clinical Medicine. View