Publikation

Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery

Wissenschaftlicher Artikel/Review - 13.10.2020

Bereiche
PubMed
DOI

Zitation
Azad T, Ehresman J, Ahmed A, Staartjes V, Lubelski D, Stienen M, Veeravagu A, Ratliff J. Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery. Spine J 2020
Art
Wissenschaftlicher Artikel/Review (Englisch)
Zeitschrift
Spine J 2020
Veröffentlichungsdatum
13.10.2020
eISSN (Online)
1878-1632
Kurzbeschreibung/Zielsetzung

As the use of machine learning algorithms in the development of clinical prediction models has increased, researchers are becoming more aware of the deleterious effects that stem from the lack of reporting standards. One of the most obvious consequences is the insufficient reproducibility found in current prediction models. In an attempt to characterize methods to improve reproducibility and to allow for better clinical performance, we utilize a previously proposed taxonomy that separates reproducibility into 3 components: technical, statistical, and conceptual reproducibility. By following this framework, we discuss common errors that lead to poor reproducibility, highlight the importance of generalizability when evaluating a ML model's performance, and provide suggestions to optimize generalizability to ensure adequate performance. These efforts are a necessity before such models are applied to patient care.