论文标题
数据驱动的慢性病动态治疗计划
Data-driven dynamic treatment planning for chronic diseases
论文作者
论文摘要
为了提供有效的护理，健康管理必须考虑慢性疾病的独特轨迹。这些疾病反复发生急性，不稳定和稳定的阶段，每种疾病都需要不同的治疗方案。但是，正确识别轨迹阶段以及因此治疗方案是具有挑战性的。在本文中，我们提出了一种数据驱动的动态方法，用于鉴定慢性疾病的轨迹阶段，从而提出治疗方案。具体而言，我们开发了一种新颖的变量持续型副物隐藏的马尔可夫模型（VDC-HMMX）。在我们的VDC-HMMX中，该轨迹被建模为一系列具有急性，稳定和不稳定相的潜在状态，这些状态最终被回收。我们根据纵向研究证明了VDC-HMMX模型的有效性，其中928例患有下背部疼痛的患者。近视分类器以平衡的精度略高于70％，以识别正确的治疗方案。相比之下，我们的VDC-HMMX模型是正确的，平衡精度为83.65％。因此，这突出了纵向监测对慢性疾病管理的价值。
In order to deliver effective care, health management must consider the distinctive trajectories of chronic diseases. These diseases recurrently undergo acute, unstable, and stable phases, each of which requires a different treatment regimen. However, the correct identification of trajectory phases, and thus treatment regimens, is challenging. In this paper, we propose a data-driven, dynamic approach for identifying trajectory phases of chronic diseases and thus suggesting treatment regimens. Specifically, we develop a novel variable-duration copula hidden Markov model (VDC-HMMX). In our VDC-HMMX, the trajectory is modeled as a series of latent states with acute, stable, and unstable phases, which are eventually recovered. We demonstrate the effectiveness of our VDC-HMMX model on the basis of a longitudinal study with 928 patients suffering from low back pain. A myopic classifier identifies correct treatment regimens with a balanced accuracy of slightly above 70%. In comparison, our VDC-HMMX model is correct with a balanced accuracy of 83.65%. This thus highlights the value of longitudinal monitoring for chronic disease management.
