Fostering Reproducibility and Generalizability in Machine Learning for Clinical Prediction Modeling in Spine Surgery

As the use of machine learning algorithms in the development of clinical prediction models has increased, researchers are becoming more aware of the deleterious 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 three components: technical, statistical, and conceptual reproducibility.

Source: The Spine Journal - October 12, 2020 Category: Orthopaedics Authors: Tej D. Azad, Jeff Ehresman, A. Karim Ahmed, Victor E. Staartjes, Daniel Lubelski, Martin N. Stienen, Anand Veeravagu, John K. Ratliff Source Type: research