为医学机器学习研究提供样本量确定方法，解决缺乏标准指南的问题。
Sample size determination for machine learning in medical research
- 从测试集样本量出发，逐步推导训练集和总样本量。
- 提出可操作的分步计算流程，确保模型性能可靠。
- 适合从事医学人工智能研究的科研人员参考使用。
机器学习（ML）方法在医学研究中应用日益广泛，但针对医学机器学习研究的样本量确定仍缺乏明确指导。本文提出一种基于分步计算的样本量确定方法：首先确定测试集所需样本量，再据此推导训练集及总样本量。该方法为医学领域的机器学习研究提供了可操作的样本量规划框架，有助于提升研究的可靠性与可重复性。
原文摘要 · Abstract (English)
Machine learning (ML) methods are being increasingly used across various domains of medicine research. However, despite advancements in the use of ML in medicine, clear and definitive guidelines for determining sample sizes in medical ML research are lacking. This article proposes a method for determining sample sizes for medical research utilizing ML methods, beginning with the determination of the testing set sample size, followed with the determination of the training set and total sample sizes.
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