Year: 2025
Type: book-chapter
Abstract: Postoperative pain is a relevant and unresolved problem in clinical practice. In order to reduce the occurrence of severe postoperative pain, preventive, multi-professional and target group-specific pain management should be implemented. Risk assessment models based on machine learning and artificial intelligence are a resource-efficient way to identify the target group. The aim of this study was to develop a risk assessment model for early predicting poor postoperative pain outcomes that achieves good results without the need of additional, non-routine data collection. The various machine learning-based models were developed by using electronic medical records from over 70.000 in- and outpatient cases and 807 modelling features. The GBM (gradient boost machine) algorithm performed best with an area under the receiver operating characteristic curve (AUROC) of 0.82 on hold-out test data. Despite the excellent result, further research is needed to determine the modelt's performance in clinical practice.
DOI: 10.3233/shti250168
Authors: Claudia Kagerer, Stefanie Jauk, Diether Kramer, Alexander Avian, Thomas Neumann, Maria Schlögl, Andreas Sandner-Kiesling
Institution: Medical University of Graz
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409773066