背景:人工智能(AI)极大地影响了我们的日常生活和医疗领域,改变医学的范围。人工智能的领域之一,and,特别是,预测建模,脊柱肿瘤学大有希望。准确的患者预后对于确定脊柱转移瘤患者的最佳治疗策略至关重要。多项研究表明,医生的生存预测是不准确的,这导致了许多预测模型的发展。然而,当试图解释这些模型时,会出现困难,更重要的是,评估他们的质量。
目的:以骨骼肿瘤学研究小组机器学习算法为例,概述开发预测模型的所有阶段和挑战。
方法:对作者已知的所有相关文章进行叙述性回顾。
结果:建立预测模型包括6个阶段:准备,发展,内部验证,介绍,外部验证,和执行。在验证期间,以下措施对于评估模型的性能至关重要:校准,歧视,决策曲线分析,还有Brier的分数.开发中的结构化方法,正在验证,在构建预测模型时,报告模型至关重要。两个主要指南是透明报告用于个体预后或诊断清单的多变量预测模型以及偏倚风险评估的预测模型。迄今为止,许多预测建模研究缺乏正确的验证措施或不正确地报告他们的方法。
结论:人工智能的快速发展及其在脊柱肿瘤学中的应用正在迎来一个新的医疗保健时代。正在开发无数的预测模型;然而,后续阶段,验证质量,透明的报告,实施仍需改进。
结论:鉴于AI预测模型在患者护理中的迅速兴起和使用,了解如何评估其质量以及了解这些模型如何影响临床实践是很有价值的.本文提供有关如何处理此问题的指导。
方法:
BACKGROUND: Artificial intelligence (AI) tremendously influences our daily lives and the medical field, changing the scope of medicine. One of the fields where AI, and, in particular, predictive modeling, holds great promise is spinal oncology. An accurate patient prognosis is essential to determine the optimal treatment strategy for patients with spinal metastases. Multiple studies demonstrated that the physician\'s survival predictions are inaccurate, which resulted in the development of numerous predictive models. However, difficulties arise when trying to interpret these models and, more importantly, assess their quality.
OBJECTIVE: To provide an overview of all stages and challenges in developing predictive models using the Skeletal Oncology Research Group machine learning algorithms as an example.
METHODS: A narrative review of all relevant articles known to the authors was conducted.
RESULTS: Building a predictive model consists of 6 stages: preparation, development, internal validation, presentation, external validation, and implementation. During validation, the following measures are essential to assess the model\'s performance: calibration, discrimination, decision curve analysis, and the Brier score. The structured methodology in developing, validating, and reporting the model is vital when building predictive models. Two principal guidelines are the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis checklist and the prediction model risk of bias assessment. To date, many predictive modeling studies lack the right validation measures or improperly report their methodology.
CONCLUSIONS: A new health care age is being ushered in by the rapid advancement of AI and its applications in spinal oncology. A myriad of predictive models are being developed; however, the subsequent stages, quality of validation, transparent reporting, and implementation still need improvement.
CONCLUSIONS: Given the rapid rise and use of AI prediction models in patient care, it is valuable to know how to assess their quality and to understand how these models influence clinical practice. This article provides guidance on how to approach this.
METHODS: