Department of Neurosurgery, Johns Hopkins Hospital, 1800 Orleans Street, Baltimore, MD, USA 21287.
Machine Intelligence in Clinical Neuroscience (MICN) Lab, Clinical Neuroscience Centre, University of Zurich, Switzerland; Department of Neurosurgery, University Hospital Zurich, Zurich, Switzerland.
Spine J. 2021 Oct;21(10):1610-1616. doi: 10.1016/j.spinee.2020.10.006. Epub 2020 Oct 13.
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.
随着机器学习算法在临床预测模型开发中的应用日益增多，研究人员越来越意识到缺乏报告标准所带来的有害影响。其中最明显的后果之一是当前预测模型中发现的可重复性不足。为了描述提高可重复性和实现更好临床性能的方法，我们利用了先前提出的分类法，将可重复性分为 3 个组成部分：技术、统计和概念可重复性。通过遵循这个框架，我们讨论了导致可重复性差的常见错误，强调了在评估机器学习模型性能时通用性的重要性，并提出了优化通用性以确保足够性能的建议。在将这些模型应用于患者护理之前，这些努力是必要的。
