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

Clinical Informatics (1378) Artificial Intelligence (2249) Clinical Information and Decision Making (2004) Machine Learning (2167) Development and Evaluation of Research Methods, Instruments and Tools (774) Secondary Use of Clinical Data for Research and Surveillance (483) Decision Support for Health Professionals (1591) Perioperative Software Tools for Clinicians (19) Adoption and Change Management of eHealth Systems (927) Electronic Health Records (1354)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59260, first published .
Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study

Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study

Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study

Authors of this article:

Haeun Lee1, 2 Author Orcid Image ;   Seok Kim2 Author Orcid Image ;   Hui-Woun Moon2 Author Orcid Image ;   Ho-Young Lee2 Author Orcid Image ;   Kwangsoo Kim3 Author Orcid Image ;   Se Young Jung4 Author Orcid Image ;   Sooyoung Yoo2 Author Orcid Image
Article Authors Cited by (6) Tweetations Metrics

Journals

  1. Wong S, Wong Y, Cheng K. Predicting prolonged length of stay in the emergency medicine ward using machine learning. Hong Kong Journal of Emergency Medicine 2025;32(4) View
  2. Shi J, Huang Y, Han J, Zhang S, Cheng X, Luo T. Development and validation of a machine learning-based prediction model for prolonged length of stay after laparoscopic gastrointestinal surgery: a secondary analysis of the FDP-PONV trial. BMC Gastroenterology 2025;25(1) View
  3. Wei J, Gay J, Brent A, Clifton D, Walker S, Eyre D. Concurrent prediction of in-hospital mortality and length of stay using single-task, multiclass and multitask machine learning. BMJ Digital Health & AI 2025;1(1):e000071 View
  4. Wu D, Wang S, Wang C, Xiang Y, Hao L, Wang Z, Zhai X, Wang Y. Machine learning models for predicting extended length of stay and hospital charges in nontraumatic subarachnoid hemorrhage. Frontiers in Neurology 2026;17 View
  5. Oh S, Kim H, Park Y. Development and validation of machine learning models for predicting operative duration in assisted reproductive technology procedures. BMC Medical Research Methodology 2026 View

Books/Policy Documents

  1. Gharbi M, Verdier C, Di Mascolo M, Babouchkine J. Research Challenges in Information Science. View