This paper is in the following e-collection/theme issue:

Advanced Data Analytics in eHealth (63) Reviews (71) Machine Learning (1187) Methods and Instruments in Medical Informatics (65) Electronic Health Records (867) Clinical Information and Decision Making (1109)

Published on in Vol 9, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30022, first published .
Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

Authors of this article:

Ann Corneille Monahan 1 Author Orcid Image ;   Sue S Feldman 2 Author Orcid Image
Article Authors Cited by (6) Tweetations (3) Metrics

Journals

  1. Monahan A, Feldman S, Fitzgerald T. Reducing Crowding in Emergency Departments With Early Prediction of Hospital Admission of Adult Patients Using Biomarkers Collected at Triage: Retrospective Cohort Study. JMIR Bioinformatics and Biotechnology 2022;3(1):e38845 View
  2. Monahan A, Feldman S. The Utility of Predictive Modeling and a Systems Process Approach to Reduce Emergency Department Crowding: A Position Paper. Interactive Journal of Medical Research 2023;12:e42016 View
  3. Kim M, Holton M, Sweeting A, Koreshe E, McGeechan K, Miskovic-Wheatley J. Using health administrative data to model associations and predict hospital admissions and length of stay for people with eating disorders. BMC Psychiatry 2023;23(1) View
  4. Charan G, Charan A, Khurana M, Narang G. Impact of Analytics Applying Artificial Intelligence and Machine Learning on Enhancing Intensive Care Unit: A Narrative Review. Galician Medical Journal 2023 View
  5. 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
  6. Larburu N, Azkue L, Kerexeta J. Predicting Hospital Ward Admission from the Emergency Department: A Systematic Review. Journal of Personalized Medicine 2023;13(5):849 View