Machine-learning based prediction of appendicitis for patients presenting with acute abdominal pain at the emergency department
Both ML models demonstrated comparable high accuracy in predicting appendicitis in patients with AAP, outperforming the Alvarado scoring system. The ML models matched or surpassed ED physician performance in detecting appendicitis, with the largest potential performance gain observed in absence of laboratory test results. Integration could assist ED physicians in early and accurate diagnosis of appendicitis.
Authors
- Steef Kurstjens (ORCID: https://orcid.org/0009-0008-4409-5079)
- Joeran S. Bosma (ORCID: https://orcid.org/0000-0002-6315-124X)
- Matthieu Rutten
- Robin Dooijes
- Bram van Ginneken (ORCID: https://orcid.org/0000-0003-2028-8972)
- Ron Kusters
- Rory O’Connor
- Kim E. Jie (ORCID: https://orcid.org/0000-0001-7661-5712)
- Anoeska Schipper
- Peter Belgers
Institutions
- Radboud University Nijmegen (NL)
- Radboud University Medical Center (NL)
- Radboud Institute for Molecular Life Sciences (NL)
- Canisius-Wilhelmina Ziekenhuis (NL)
- Jeroen Bosch Ziekenhuis (NL)
- University of Twente (NL)
Publication Details
- Journal
- World Journal of Emergency Surgery
- Published
- 2024-12-23
- DOI
- https://doi.org/10.1186/s13017-024-00570-7
- Citations
- 9
- Primary Topic
- Appendicitis Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 6.22
Funders
- Radboud Universitair Medisch Centrum