Multi-series Time-aware Sequence Partitioning for Disease Progression Modeling
Multi-series Time-aware Sequence Partitioning for Disease Progression Modeling
复制标题
DOI：
10.24963/ijcai.2021/493
复制
发表时间：
2021-08
期刊：
影响因子：
--
通讯作者：
Xi Yang;Yuan Zhang;Min Chi
中科院分区：
文献类型：
--
作者：
Xi Yang;Yuan Zhang;Min Chi
电子医疗记录 (EHR) 是患者数据的全面纵向收集，在疾病进展建模以促进临床决策方面发挥着关键作用。基于电子病历，在这项工作中，我们重点关注脓毒症——一种几乎所有类型的感染（例如流感、肺炎）均可引发的广泛综合征。脓毒症的症状，如心率加快、发烧和呼吸短促，在其他疾病中很模糊且常见，这使得对其进展的建模极具挑战性。受最近成功的新型子序列聚类方法：基于 Toeplitz 逆协方差的聚类（TICC）的启发，我们将脓毒症进展建模为子序列分区问题，并提出了多系列时间感知 TICC（MT-TICC），其中结合了 EHR 的多系列性质和不规则时间间隔。 MT-TICC 的有效性首先通过使用带有真实标签的真实手势数据集的案例研究得到验证。然后我们进一步将其应用于使用 EHR 的脓毒症进展建模。结果表明 MT-TICC 可以显着优于包括 TICC 在内的竞争基线模型。更重要的是，它揭示了可解释的模式，这为更好地理解脓毒症进展提供了一些线索。
Electronic healthcare records (EHRs) are comprehensive longitudinal collections of patient data that play a critical role in modeling the disease progression to facilitate clinical decision-making. Based on EHRs, in this work, we focus on sepsis -- a broad syndrome that can develop from nearly all types of infections (e.g., influenza, pneumonia). The symptoms of sepsis, such as elevated heart rate, fever, and shortness of breath, are vague and common to other illnesses, making the modeling of its progression extremely challenging. Motivated by the recent success of a novel subsequence clustering approach: Toeplitz Inverse Covariance-based Clustering (TICC), we model the sepsis progression as a subsequence partitioning problem and propose a Multi-series Time-aware TICC (MT-TICC), which incorporates multi-series nature and irregular time intervals of EHRs. The effectiveness of MT-TICC is first validated via a case study using a real-world hand gesture dataset with ground-truth labels. Then we further apply it for sepsis progression modeling using EHRs. The results suggest that MT-TICC can significantly outperform competitive baseline models, including the TICC. More importantly, it unveils interpretable patterns, which sheds some light on better understanding the sepsis progression.