耦合隐马尔可夫模型混合用于多元医疗时间序列的稳健建模
Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series
AI总结：
针对多元医疗时间序列的挑战，提出耦合隐马尔可夫模型混合（M-CHMM），通过两种采样算法实现高效推理，在真实和半合成数据上提升拟合、预测和可解释性。
摘要：
多元医疗时间序列数据的分析固有地具有挑战性：不规则采样、噪声和缺失值，以及具有不同动态的异质患者群体违反可交换性。此外，可解释性和不确定性量化至关重要。在此，我们提出了一类新颖的模型，即耦合隐马尔可夫模型混合（M-CHMM），并展示了它如何优雅地克服这些挑战。为了使模型学习可行，我们推导了两种算法来对CHMM中的潜在变量序列进行采样：基于（i）粒子滤波和（ii）因子化近似的采样器。与现有推理方法相比，我们的算法在计算上易于处理，改善了混合，并允许进行似然估计，这是学习混合模型所必需的。在具有挑战性的真实流行病学和半合成数据上的实验证明了M-CHMM的优势：改进的数据拟合、有效处理缺失和噪声测量的能力、改进的预测准确性以及识别数据中可解释子集的能力。
英文摘要：
Analysis of multivariate healthcare time series data is inherently challenging: irregular sampling, noisy and missing values, and heterogeneous patient groups with different dynamics violating exchangeability. In addition, interpretability and quantification of uncertainty are critically important. Here, we propose a novel class of models, a mixture of coupled hidden Markov models (M-CHMM), and demonstrate how it elegantly overcomes these challenges. To make the model learning feasible, we derive two algorithms to sample the sequences of the latent variables in the CHMM: samplers based on (i) particle filtering and (ii) factorized approximation. Compared to existing inference methods, our algorithms are computationally tractable, improve mixing, and allow for likelihood estimation, which is necessary to learn the mixture model. Experiments on challenging real-world epidemiological and semi-synthetic data demonstrate the advantages of the M-CHMM: improved data fit, capacity to efficiently handle missing and noisy measurements, improved prediction accuracy, and ability to identify interpretable subsets in the data.
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