论文标题
用用户驱动的纵向健康数据分析具有隐藏的马尔可夫模型的临床见解
User-driven Analysis of Longitudinal Health Data with Hidden Markov Models for Clinical Insights
论文作者
论文摘要
临床研究人员的一个目标是通过一组生物标志物了解疾病的发展。研究人员经常进行观察性研究，在多年内，他们从选定受试者中收集了许多样本。隐藏的马尔可夫模型（HMM）可用于发现潜在状态及其过渡概率。但是，临床研究人员解释结果并获得有关该疾病的见解是一项挑战。因此，该演示引入了一个称为DPVI的交互式可视化系统，该系统旨在帮助研究人员交互探索HMM结果。该演示提供了如何实施临床医生在环境中的指南，以通过视觉分析分析纵向，观察性健康数据。
A goal of clinical researchers is to understand the progression of a disease through a set of biomarkers. Researchers often conduct observational studies, where they collect numerous samples from selected subjects throughout multiple years. Hidden Markov Models (HMMs) can be applied to discover latent states and their transition probabilities over time. However, it is challenging for clinical researchers to interpret the outcomes and to gain insights about the disease. Thus, this demo introduces an interactive visualization system called DPVis, which was designed to help researchers to interactively explore HMM outcomes. The demo provides guidelines of how to implement the clinician-in-the-loop approach for analyzing longitudinal, observational health data with visual analytics.
