为可穿戴设备心率信号建模提供不确定性量化实践指南
Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals
- 梳理适用于脉搏波信号的机器学习模型与不确定性量化方法
- 提出六类基准任务及对应数据集，支持模型评估与比较
- 面向医疗场景的模型开发者，尤其关注可靠性与伦理问题
本文介绍了QUMPHY项目在可穿戴设备脉搏波（PPG）信号上的机器学习与不确定性量化工作。针对回归与分类任务，提供了适合的模型选择建议，并比较了不同模型性能。系统梳理了模型相关与无关的不确定性量化方法，涵盖实现与结果验证策略。同时提供了六个基准问题及其对应的公开数据集。文中还介绍了辅助工具软件，并简要讨论了相关伦理问题。最后总结并提出实践建议。
原文摘要 · Abstract (English)
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.
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