Using Natural Language Processing in the LACE Index Scoring Tool to Predict Unplanned Trauma and Surgical Readmissions in South Africa
The LACE score demonstrated the predictive value for readmission in trauma and general surgery patients. The LACE score was relatively effective in identifying patients who were less likely to be readmitted but showed limitations in identifying patients at higher risk of readmission. However, the successful use of natural language processing for data extraction of comorbidities shows promise on addressing the challenges around text-based medical records.
Authors
- Umit Tokac (ORCID: https://orcid.org/0000-0001-6698-1401)
- John Bruce (ORCID: https://orcid.org/0000-0001-8666-4104)
- Jennifer Chipps (ORCID: https://orcid.org/0000-0002-7895-4483)
- Damian Clarke (ORCID: https://orcid.org/0000-0002-8467-1455)
- Petra Brysiewicz
Institutions
- University of the Witwatersrand (ZA)
- University of Missouri–St. Louis (US)
- University of the Western Cape (ZA)
- University of KwaZulu-Natal (ZA)
Publication Details
- Journal
- World Journal of Surgery
- Published
- 2025-03-09
- DOI
- https://doi.org/10.1002/wjs.12523
- Citations
- 1
- Primary Topic
- Heart Failure Treatment and Management
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- South African Medical Research Council