Exploration of Different Machine Learning Methods and Domains of Predictors for Chronic Postsurgical Pain After Video-Assisted Thoracoscopic Surgery
The study serves as a proof-of-concept, demonstrating that different ML models can yield varying results when predicting CPSP. Among these, a prediction model based on Gradient Boosting exhibited the best fit. However, the potential risk of overfitting cannot be ruled out, necessitating further validation before clinical implementation.
Authors
- Rocco Giordano
- Allan Vestergaard Danielsen (ORCID: https://orcid.org/0000-0002-3025-7873)
- Jannie Bisgaard (ORCID: https://orcid.org/0000-0001-6499-9691)
- Bodil Steen Rasmussen (ORCID: https://orcid.org/0000-0003-2190-145X)
- Jan Vollert
- Phillip K. Sperling
- Kristian K.-S. Petersen
- Lars Arendt-Nielsen
Institutions
- Steno Diabetes Centers (DK)
- University of Exeter (GB)
- Aalborg University Hospital (DK)
- Institute of Clinical Research (US)
- Center for Pain and the Brain (US)
- Institute of Mathematics and Mathematical Modeling (KZ)
- Aalborg University (DK)
Publication Details
- Journal
- Clinical Journal of Pain
- Published
- 2025-12-04
- DOI
- https://doi.org/10.1097/ajp.0000000000001347
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00