Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Kagerer, C; Jauk, S; Kramer, D; Avian, A; Neumann, T; Schlögl, M; Sandner-Kiesling, A.
A Machine Learning-Based Risk Assessment Model for Poor Postoperative Pain Outcome.
Stud Health Technol Inform. 2025; 324:98-104
Doi: 10.3233/SHTI250168
PubMed
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- Co-Autor*innen der Med Uni Graz
- Avian Alexander
- Sandner-Kiesling Andreas
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- 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.
- Find related publications in this database (using NLM MeSH Indexing)
- Machine Learning - administration & dosage
- Humans - administration & dosage
- Risk Assessment - methods
- Pain, Postoperative - diagnosis, epidemiology
- Electronic Health Records - statistics & numerical data
- Algorithms - administration & dosage
- Female - administration & dosage