Applying Machine Learning to the <scp>ANZELA</scp> ‐ <scp>QI</scp> Database to Predict Adverse Outcomes for Patients Undergoing Emergency Laparotomy
This study applied novel machine learning programs to the ANZELA-QI database to develop risk stratification models for adverse outcomes in patients undergoing emergency laparotomy. The results showed high accuracy for the prediction of prolonged length of stay and post-operative ICU admission.
Authors
- Richard Turner (ORCID: https://orcid.org/0000-0002-2276-8999)
- Joshua Blum (ORCID: https://orcid.org/0009-0001-5910-3034)
- Steven Xu (ORCID: https://orcid.org/0000-0001-6997-5533)
- Dafydd J. Jones (ORCID: https://orcid.org/0000-0001-5407-6340)
- Claire Cartwright (ORCID: https://orcid.org/0000-0002-3544-4926)
- Nikki Verhagen
- Benjamin Denholm (ORCID: https://orcid.org/0000-0002-6519-2037)
- Lucinda Southcott
Institutions
- University of Tasmania (AU)
- Royal Hobart Hospital (AU)
Publication Details
- Journal
- ANZ Journal of Surgery
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1111/ans.70185
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00