Zhu Tingting, Javed Hamza, Clifton David A
Institute of Biomedical Engineering Department of Engineering University of Oxford Oxford United Kingdom.
Healthc Technol Lett. 2021 Feb 23;8(2):25-30. doi: 10.1049/htl2.12003. eCollection 2021 Apr.
The rapid proliferation of wearable devices for medical applications has necessitated the need for automated algorithms to provide labelling of physiological time-series data to identify abnormal morphology. However, such algorithms are less reliable than gold-standard human expert labels (where the latter are typically difficult and expensive to obtain), due to their large inter- and intra-subject variabilities. Actions taken in response to these algorithms can therefore result in sub-optimal patient care. In a typical scenario where only unevenly sampled continuous or numeric estimates are provided, without access to the "ground truth", it is challenging to choose which algorithms to trust and which to ignore, or even how to merge the outputs from multiple algorithms to form a more precise final estimate for individual patients. In this work, the novel application of two previously proposed parametric fully-Bayesian graphical models is demonstrated for fusing labels from (i) independent and (ii) potentially correlated algorithms, validated on two publicly available datasets for the task of respiratory rate (RR) estimation. These unsupervised models aggregate RR labels and estimate jointly the assumed bias and precision of each algorithm. Fusing estimates in this way may then be used to infer the underlying ground truth for individual patients. It is shown that modelling the latent correlations between algorithms improves the RR estimates, when compared to commonly employed strategies in the literature. Finally, it is demonstrated that the adoption of a strongly Bayesian approach to inference using Gibbs sampling results in improved estimation over the current state-of-the-art (e.g. hierarchical Gaussian processes) in physiological time-series modelling.
用于医疗应用的可穿戴设备迅速普及，因此需要自动化算法来对生理时间序列数据进行标注，以识别异常形态。然而，由于这些算法在个体间和个体内存在较大差异，其可靠性低于金标准的人类专家标注（后者通常难以获得且成本高昂）。因此，根据这些算法采取的行动可能导致患者护理效果欠佳。在典型情况下，仅提供采样不均匀的连续或数值估计，且无法获取“真实情况”，那么选择信任哪些算法、忽略哪些算法，甚至如何合并多种算法的输出以形成针对个体患者更精确的最终估计，都具有挑战性。在这项工作中，展示了两种先前提出的参数化全贝叶斯图形模型的新颖应用，用于融合来自（i）独立和（ii）潜在相关算法的标注，并在两个公开可用的数据集上针对呼吸率（RR）估计任务进行了验证。这些无监督模型汇总RR标注，并联合估计每种算法的假定偏差和精度。以这种方式融合估计值，随后可用于推断个体患者的潜在真实情况。结果表明，与文献中常用的策略相比，对算法之间的潜在相关性进行建模可改善RR估计。最后，证明了采用基于吉布斯采样的强贝叶斯推理方法，在生理时间序列建模方面比当前的先进技术（例如分层高斯过程）能实现更好的估计。
