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Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques

机译:使用机器学习技术对预测模型研究的方法和报告质量的系统审查的协议

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摘要

Studies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domains. Systematic reviews have shown that the methodological and reporting quality of prediction model studies is suboptimal. Due to the increasing availability of larger, routinely collected and complex medical data, and the rising application of Artificial Intelligence (AI) or machine learning (ML) techniques, the number of prediction model studies is expected to increase even further. Prediction models developed using AI or ML techniques are often labelled as a ‘black box’ and little is known about their methodological and reporting quality. Therefore, this comprehensive systematic review aims to evaluate the reporting quality, the methodological conduct, and the risk of bias of prediction model studies that applied ML techniques for model development and/or validation.
机译:解决诊断和预测预测模型的开发和/或验证的研究在大多数临床域中都是丰富的。系统评论表明,预测模型研究的方法和报告质量是次优。由于越来越大,常规收集和复杂的医疗数据的可用性以及人工智能(AI)或机器学习(ML)技术的上升,预测模型研究的数量将进一步增加。使用AI或ML技术开发的预测模型通常被标记为“黑匣子”,并且关于它们的方法论和报告质量很少。因此,这种全面的系统审查旨在评估报告质量,方法论行为和预测模型研究偏差的风险,这是应用ML技术的模型开发和/或验证。

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