Machine learning models for predicting dysphonia following anterior cervical discectomy and fusion: a Swedish Registry Study
In this study, machine learning models were employed to identify predictors of persistent dysphonia following ACDF. Among the models tested, the RF classifier demonstrated superior performance, with an AUC value of 0.790. The RF model identified NDI, EQ5D<sub>index</sub>, and number of fused vertebrae as key variables. These findings underscore the potential of machine learning models in identifying patients at increased risk for dysphonia persisting for more than 1 month after surgery.
Authors
- Anna MacDowall (ORCID: https://orcid.org/0000-0002-9231-7608)
- Victor E. Staartjes (ORCID: https://orcid.org/0000-0003-1039-2098)
- Erik Edström (ORCID: https://orcid.org/0000-0002-8781-1169)
- Victor Gabriel El-Hajj (ORCID: https://orcid.org/0000-0001-9479-761X)
- Paul Gerdhem (ORCID: https://orcid.org/0000-0001-8061-7163)
- Adrian Elmi‐Terander (ORCID: https://orcid.org/0000-0002-3776-6136)
- Ali Buwaider (ORCID: https://orcid.org/0009-0000-1966-7911)
Institutions
- Uppsala University Hospital (SE)
- University of Zurich (CH)
- Karolinska Institutet (SE)
- University Hospital of Zurich (CH)
- Örebro University Hospital (SE)
Publication Details
- Journal
- The Spine Journal
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1016/j.spinee.2024.10.010
- Citations
- 18
- Primary Topic
- Cervical and Thoracic Myelopathy
- Type
- article
- Field-Weighted Citation Impact
- 10.31
Funders
- Karolinska Institutet
- Stockholms Läns Landsting
- Vetenskapsrådet
- Uppsala Universitet
- Center for Innovative Medicine