用于光电容积脉搏波描记术信号的机器学习模型不确定性量化的良好实践指南
Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals
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中文总结 AI 辅助
该指南介绍QUMPHY项目，针对以可穿戴设备PPG信号为输入的机器学习及不确定性量化问题，给出适用模型类型、量化方法实施与结果验证指导，描述六个基准问题及相关数据集，还有辅助软件及伦理考量，最后总结并提建议。
中文摘要 AI 辅助
本良好实践指南展示了QUMPHY项目（用于光电容积脉搏波描记术信号的机器学习模型的不确定性量化）所做的工作，该项目考虑了将可穿戴设备的光电容积脉搏波描记术（PPG）信号作为输入的机器学习和不确定性量化问题。它提供了关于可能使用的机器学习模型类型以及不同模型在应用于回归和分类任务时如何比较的高级指导。它为不同的不确定性量化方法的实施提供指导，涵盖依赖模型和独立于模型的技术，以及对这些方法提供的结果的验证。它还描述了六个基准问题以及每个问题指向不同基准数据集的指针。描述了可帮助从业者实施本文所述方法的软件，并简要考虑了伦理问题。最后进行了总结并提出了建议。
英文摘要
This Good Practice Guide presents work done in the QUMPHY project (Uncertainty quantification for machine learning models applied to photoplethysmography signals) that considered both machine learning and uncertainty quantification for problems which used photoplethysmography (PPG) signals from wearable devices as input. It provides high-level guidance on what types of machine learning model might be used and how different models compare when applied to both regression and classification tasks. It provides guidance on the implementation of different methods for uncertainty quantification, covering both model-dependent and model-independent techniques, and on the validation of the results provided by those methods. It also describes six benchmark problems together with pointers to different benchmark datasets for each problem. Software is described that can assist practitioners in implementing the methods described herein and there is a brief consideration of ethical issues. It concludes with a summary and recommendations.
